# Skills for Claude Code

> Find essential skills for Claude Code in the Doutor AI Library. Improve what your application can do with our components.

- Source: https://doutorai.pt/en/claude-code/skills
- Site: Doutor AI (https://doutorai.pt/en) - AI tools for small businesses

Skills are key components that give Claude Code specific knowledge and capabilities, offering tailored solutions for different needs. With more than 5,573 skills available in the Doutor AI Library, you can boost your applications efficiently and effectively.

Installing skills through Doutor AI gives you access to a wide range of options, so developers and companies can find exactly what they need to take their projects to the next level. Flexibility and customization are essential in modern development, and Doutor AI skills are the key to getting the most out of Claude Code.

## Explore skills

- [PocketBase API Rules](https://doutorai.pt/en/components/skills__pocketbase-api-rules): API rules and filter expressions for PocketBase access control. Use when setting permissions, writing filter expressions, configuring who can access what, or de
- [PocketBase Collections](https://doutorai.pt/en/components/skills__pocketbase-collections): Collection and schema design for PocketBase. Use when creating collections, designing schemas, adding fields, setting up relations, or choosing between base/aut
- [PocketBase Deploy](https://doutorai.pt/en/components/skills__pocketbase-deploy): Production deployment for PocketBase. Use when deploying PocketBase to a server, setting up Docker, configuring systemd, reverse proxy (nginx/Caddy), TLS, SMTP,
- [PocketBase Hooks](https://doutorai.pt/en/components/skills__pocketbase-hooks): Server-side JavaScript hooks for PocketBase (pb_hooks). Use when writing custom routes, event hooks, cron jobs, sending emails, making HTTP requests, querying t
- [PocketBase Migrations](https://doutorai.pt/en/components/skills__pocketbase-migrations): Schema migrations and versioning for PocketBase. Use when creating migrations, managing schema versions, syncing collections between environments, using automig
- [PocketBase SDK](https://doutorai.pt/en/components/skills__pocketbase-sdk): JavaScript SDK usage for PocketBase client applications. Use when calling PocketBase from frontend or Node.js, authenticating users, subscribing to realtime eve
- [develop-web-game](https://doutorai.pt/en/components/skills__develop-web-game): Use when Codex is building or iterating on a web game (HTML/JS) and needs a reliable development + testing loop: implement small changes, run a Playwright-based
- [doc](https://doutorai.pt/en/components/skills__doc): Use when the task involves reading, creating, or editing `.docx` documents, especially when formatting or layout fidelity matters; prefer `python-docx` plus the
- [figma-implement-design](https://doutorai.pt/en/components/skills__figma-implement-design): Translate Figma nodes into production-ready code with 1:1 visual fidelity using the Figma MCP workflow (design context, screenshots, assets, and project-convent
- [imagegen](https://doutorai.pt/en/components/skills__imagegen): Use when the user asks to generate or edit images via the OpenAI Image API (for example: generate image, edit/inpaint/mask, background removal or replacement, t
- [jupyter-notebook](https://doutorai.pt/en/components/skills__jupyter-notebook): Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and r
- [luma-imagegen](https://doutorai.pt/en/components/skills__luma-imagegen): Use when the user asks to generate images via the Luma AI API (Dream Machine / Photon); collects a prompt and options interactively, then calls the API using th
- [openai-docs](https://doutorai.pt/en/components/skills__openai-docs): Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations (for example: Codex, Responses API, 
- [playwright](https://doutorai.pt/en/components/skills__playwright): Use when the task requires automating a real browser from the terminal (navigation, form filling, snapshots, screenshots, data extraction, UI-flow debugging) vi
- [screenshot](https://doutorai.pt/en/components/skills__screenshot): Use when the user explicitly asks for a desktop or system screenshot (full screen, specific app or window, or a pixel region), or when tool-specific capture cap
- [security-best-practices](https://doutorai.pt/en/components/skills__security-best-practices): Perform language and framework specific security best-practice reviews and suggest improvements. Trigger only when the user explicitly requests security best pr
- [security-ownership-map](https://doutorai.pt/en/components/skills__security-ownership-map): Analyze git repositories to build a security ownership topology (people-to-file), compute bus factor and sensitive-code ownership, and export CSV/JSON for graph
- [security-threat-model](https://doutorai.pt/en/components/skills__security-threat-model): Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown
- [sora](https://doutorai.pt/en/components/skills__sora): Use when the user asks to generate, remix, poll, list, download, or delete Sora videos via OpenAI\u2019s video API using the bundled CLI (`scripts/sora.py`), in
- [speech](https://doutorai.pt/en/components/skills__speech): Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run t
- [spreadsheet](https://doutorai.pt/en/components/skills__spreadsheet): Use when tasks involve creating, editing, analyzing, or formatting spreadsheets (`.xlsx`, `.csv`, `.tsv`) using Python (`openpyxl`, `pandas`), especially when f
- [transcribe](https://doutorai.pt/en/components/skills__transcribe): Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio/video, extract text from 
- [yeet](https://doutorai.pt/en/components/skills__yeet): Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).
- [02-elementary-rules-of-usage](https://doutorai.pt/en/components/skills__02-elementary-rules-of-usage): Follow this rule whatever the final consonant. Thus write,
- [03-elementary-principles-of-composition](https://doutorai.pt/en/components/skills__03-elementary-principles-of-composition): If the subject on which you are writing is of slight extent, or if you intend to treat it very briefly, there may be no need of subdividing it into topics. Thus
- [04-a-few-matters-of-form](https://doutorai.pt/en/components/skills__04-a-few-matters-of-form): I went to his house yesterday (my third attempt to see him), but he had left town.
- [05-words-and-expressions-commonly-misused](https://doutorai.pt/en/components/skills__05-words-and-expressions-commonly-misused): (Some of the forms here listed, as like I did, are downright bad English; others, as the split infinitive, have their defenders, but are in such general disfavo
- [2d-games](https://doutorai.pt/en/components/skills__2d-games): 2D game development principles. Sprites, tilemaps, physics, camera.
- [3d-games](https://doutorai.pt/en/components/skills__3d-games): 3D game development principles. Rendering, shaders, physics, cameras.
- [3d-web-experience](https://doutorai.pt/en/components/skills__3d-web-experience): Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfoli
- [3p-updates](https://doutorai.pt/en/components/skills__3p-updates): They represent the work of the team across a time period, almost always one week. They include three sections: 1) Progress: what the team has accomplished over 
- [3p-updates](https://doutorai.pt/en/components/skills__3p-updates-2): They represent the work of the team across a time period, almost always one week. They include three sections: 1) Progress: what the team has accomplished over 
- [ACKNOWLEDGEMENTS](https://doutorai.pt/en/components/skills__acknowledgements): Loki Mode stands on the shoulders of giants. This project incorporates research, patterns, and insights from the leading AI labs, academic institutions, and pra
- [ADVANCED](https://doutorai.pt/en/components/skills__advanced): Ideas and concepts for future improvements to the skill system.
- [AGENTS](https://doutorai.pt/en/components/skills__agents): Comprehensive Postgres performance optimization guide for developers using Supabase and Postgres. Contains performance rules across 8 categories, prioritized by
- [AGENTS](https://doutorai.pt/en/components/skills__agents-2): Comprehensive Postgres performance optimization guide for developers using Supabase and Postgres. Contains performance rules across 8 categories, prioritized by
- [AGENTS](https://doutorai.pt/en/components/skills__agents-3): Comprehensive performance optimization guide for React and Next.js applications, designed for AI agents and LLMs. Contains 40+ rules across 8 categories, priori
- [AI_CURATION](https://doutorai.pt/en/components/skills__ai-curation): Guide to using AI visual analysis for unit curation, inspired by SpikeAgent's approach.
- [ANALYSIS](https://doutorai.pt/en/components/skills__analysis): Comprehensive guide to quality metrics, visualization, and analysis of sorted Neuropixels data.
- [ANTHROPIC_ATTRIBUTION](https://doutorai.pt/en/components/skills__anthropic-attribution): These skills are provided by Anthropic as examples to demonstrate what's possible with Claude's skills system. We are grateful for their contribution to the AI 
- [API Fuzzing for Bug Bounty](https://doutorai.pt/en/components/skills__api-fuzzing-for-bug-bounty): This skill should be used when the user asks to "test API security", "fuzz APIs", "find IDOR vulnerabilities", "test REST API", "test GraphQL", "API penetration
- [API Integration Specialist](https://doutorai.pt/en/components/skills__api-integration-specialist): Expert in integrating third-party APIs with proper authentication, error handling, rate limiting, and retry logic. Use when integrating REST APIs, GraphQL endpo
- [API_REFERENCE](https://doutorai.pt/en/components/skills__api-reference): This document contains the comprehensive Playwright API reference and advanced patterns. For quick-start execution patterns, see SKILL.md.
- [AUTHENTICATION](https://doutorai.pt/en/components/skills__authentication): Playwright/Patchright has a known bug (#36139) where session cookies (cookies without an Expires attribute) do not persist correctly when using launch_persisten
- [AUTOMATED_CURATION](https://doutorai.pt/en/components/skills__automated-curation): Guide to automated spike sorting curation using Bombcell, UnitRefine, and other tools.
- [AWS Penetration Testing](https://doutorai.pt/en/components/skills__aws-penetration-testing): This skill should be used when the user asks to "pentest AWS", "test AWS security", "enumerate IAM", "exploit cloud infrastructure", "AWS privilege escalation",
- [Accessibility Auditor](https://doutorai.pt/en/components/skills__accessibility-auditor): Web accessibility specialist for WCAG compliance, ARIA implementation, and inclusive design. Use when auditing websites for accessibility issues, implementing W
- [Active Directory Attacks](https://doutorai.pt/en/components/skills__active-directory-attacks): This skill should be used when the user asks to "attack Active Directory", "exploit AD", "Kerberoasting", "DCSync", "pass-the-hash", "BloodHound enumeration", "
- [Agent Development](https://doutorai.pt/en/components/skills__agent-development): This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent ex
- [ArsenalSC-OFL](https://doutorai.pt/en/components/skills__arsenalsc-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [ArsenalSC-Regular](https://doutorai.pt/en/components/skills__arsenalsc-regular): GDEF)T ^GPOS8 l| 3|GSUB 6 OS/2 E~ cmapM cvt fpgmb/ \ gasp glyflɝ head Ϥ h 6hhea ( D $hmtxx 'h loca˚Q maxp $ named ' postُ It # prepM9r m ^ ;! B0p,,_ > A A P P S
- [BUILTIN_FUNCTIONS](https://doutorai.pt/en/components/skills__builtin-functions): Complete reference for n8n's built-in JavaScript functions and helpers.
- [BasePage](https://doutorai.pt/en/components/skills__basepage): / BasePage – foundation for all Page Object classes. Responsibilities: - Inject Page via constructor (never create Playwright here) - Provide common action help
- [BaseTest](https://doutorai.pt/en/components/skills__basetest): / Thread-safe BaseTest for parallel Playwright execution. Handles Playwright/Browser/Context/Page lifecycle. Captures traces and screenshots on test completion.
- [BigShoulders-Bold](https://doutorai.pt/en/components/skills__bigshoulders-bold): GDEFy , GPOSaͪ d QxGSUB M ,p OS/2 _2 STATY 2cmapB L X tgasp glyf X head% 6hhea m post] > 6 . & N C S K % E H & ( @ B ^ x z , . R U
- [BigShoulders-OFL](https://doutorai.pt/en/components/skills__bigshoulders-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [BigShoulders-Regular](https://doutorai.pt/en/components/skills__bigshoulders-regular): GDEFx' 0 GPOS5 d QnGSUB M , OS/2 S^ STATSY 6cmapB L h tgasp glyfO Zhead&U 6hhea L &preph ) S i | ( $ opsz wght 6 . & 1 D # % E H
- [Boldonse-OFL](https://doutorai.pt/en/components/skills__boldonse-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [BricolageGrotesque-OFL](https://doutorai.pt/en/components/skills__bricolagegrotesque-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [Broken Authentication Testing](https://doutorai.pt/en/components/skills__broken-authentication-testing): This skill should be used when the user asks to "test for broken authentication vulnerabilities", "assess session management security", "perform credential stuf
- [Burp Suite Web Application Testing](https://doutorai.pt/en/components/skills__burp-suite-web-application-testing): This skill should be used when the user asks to "intercept HTTP traffic", "modify web requests", "use Burp Suite for testing", "perform web vulnerability scanni
- [CHANGELOG](https://doutorai.pt/en/components/skills__changelog): All notable changes to this project will be documented in this file.
- [CHANGELOG](https://doutorai.pt/en/components/skills__changelog-2): All notable changes to Loki Mode will be documented in this file.
- [CLAUDE](https://doutorai.pt/en/components/skills__claude): Multi-agent autonomous startup system for Claude Code. Takes PRD to fully deployed, revenue-generating product with zero human intervention.
- [CLAUDE_MD_TESTING](https://doutorai.pt/en/components/skills__claude-md-testing): Testing different documentation variants to find what actually makes agents discover and use skills under pressure.
- [COMMON_MISTAKES](https://doutorai.pt/en/components/skills__common-mistakes): Complete catalog of expression errors with explanations and fixes.
- [COMMON_PATTERNS](https://doutorai.pt/en/components/skills__common-patterns): Production-tested Python patterns for n8n Code nodes.
- [COMMON_PATTERNS](https://doutorai.pt/en/components/skills__common-patterns-2): Production-tested patterns for n8n Code nodes. These patterns are proven in real workflows.
- [COMPETITIVE-ANALYSIS](https://doutorai.pt/en/components/skills__competitive-analysis): Loki Mode has unique differentiation in business operations automation but faces significant gaps in benchmarks, community adoption, and enterprise security fea
- [CONSTITUTION](https://doutorai.pt/en/components/skills__constitution): This constitution can only be amended through: 1. Version bump in header 2. Git commit with [CONSTITUTION] prefix 3. Changelog entry documenting what changed an
- [CONTEXT-EXPORT](https://doutorai.pt/en/components/skills__context-export): When starting a new session, read these files: 1. SKILL.md - The actual skill instructions 2. autonomy/run.sh - Main entry point 3. VERSION and CHANGELOG.md - C
- [CREATION-LOG](https://doutorai.pt/en/components/skills__creation-log): Reference example of extracting, structuring, and bulletproofing a critical skill.
- [Claude Code Guide](https://doutorai.pt/en/components/skills__claude-code-guide): Master guide for using Claude Code effectively. Includes configuration templates, prompting strategies "Thinking" keywords, debugging techniques, and best pract
- [Cloud Penetration Testing](https://doutorai.pt/en/components/skills__cloud-penetration-testing): This skill should be used when the user asks to "perform cloud penetration testing", "assess Azure or AWS or GCP security", "enumerate cloud resources", "exploi
- [Command Development](https://doutorai.pt/en/components/skills__command-development): This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command fr
- [CrimsonPro-Italic](https://doutorai.pt/en/components/skills__crimsonpro-italic): GDEFFE GSUB;\ &( OS/2; ^] STATp DcmapX R 2gasp glyfO- L head rJO 6hhea post ) 3, preph 7 L $ Z D\+_ L W b p { 0 ; F Q \ g r 1 = I U a m y ! - ^ j & 1 J ] i $ i 
- [CrimsonPro-OFL](https://doutorai.pt/en/components/skills__crimsonpro-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [Cross-Site Scripting and HTML Injection Testing](https://doutorai.pt/en/components/skills__cross-site-scripting-and-html-injection-testing): This skill should be used when the user asks to "test for XSS vulnerabilities", "perform cross-site scripting attacks", "identify HTML injection flaws", "exploi
- [DATA_ACCESS](https://doutorai.pt/en/components/skills__data-access): Complete guide to accessing data in n8n Code nodes using Python.
- [DATA_ACCESS](https://doutorai.pt/en/components/skills__data-access-2): Comprehensive guide to accessing data in n8n Code nodes using JavaScript.
- [DELIVERY_SUMMARY](https://doutorai.pt/en/components/skills__delivery-summary): A production-ready Claude AI skill for marketing teams, built following Anthropic's official best practices and designed for immediate deployment.
- [DEPENDENCIES](https://doutorai.pt/en/components/skills__dependencies): Deep dive into n8n property dependencies and displayOptions mechanism.
- [DIRECTORY_STRUCTURE](https://doutorai.pt/en/components/skills__directory-structure): Each reference file is under 500 lines, allowing Claude to load complete files when needed without excessive context penalty. Files are independent and can be r
- [DMMono-OFL](https://doutorai.pt/en/components/skills__dmmono-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [Data Privacy Compliance](https://doutorai.pt/en/components/skills__data-privacy-compliance): Data privacy and regulatory compliance specialist for GDPR, CCPA, HIPAA, and international data protection laws. Use when implementing privacy controls, conduct
- [E2E_VERIFICATION_REPORT](https://doutorai.pt/en/components/skills__e2e-verification-report): All source files verified to exist and be properly implemented. Frontend builds successfully. Backend has expected TypeScript compilation issues related to miss
- [ERROR_CATALOG](https://doutorai.pt/en/components/skills__error-catalog): Comprehensive catalog of n8n validation errors with real examples and fixes.
- [ERROR_PATTERNS](https://doutorai.pt/en/components/skills__error-patterns): Common Python Code node errors and how to fix them.
- [ERROR_PATTERNS](https://doutorai.pt/en/components/skills__error-patterns-2): Complete guide to avoiding the most common Code node errors.
- [EXAMPLES](https://doutorai.pt/en/components/skills__examples): Practical examples of common analytics tasks and analysis patterns.
- [EXAMPLES](https://doutorai.pt/en/components/skills__examples-2): Real working examples from n8n workflows.
- [Email Composer](https://doutorai.pt/en/components/skills__email-composer): Draft professional emails for various contexts including business, technical, and customer communication. Use when the user needs help writing emails or composi
- [EricaOne-OFL](https://doutorai.pt/en/components/skills__ericaone-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [Error Resolver](https://doutorai.pt/en/components/skills__error-resolver): Systematic error diagnosis and resolution using first-principle analysis. Use when encountering any error message, stack trace, or unexpected behavior. Supports
- [Ethical Hacking Methodology](https://doutorai.pt/en/components/skills__ethical-hacking-methodology): This skill should be used when the user asks to "learn ethical hacking", "understand penetration testing lifecycle", "perform reconnaissance", "conduct security
- [Excel Analysis](https://doutorai.pt/en/components/skills__excel-analysis): Analyze Excel spreadsheets, create pivot tables, generate charts, and perform data analysis. Use when analyzing Excel files, spreadsheets, tabular data, or .xls
- [FALSE_POSITIVES](https://doutorai.pt/en/components/skills__false-positives): When validation warnings are acceptable and how to handle them.
- [FORMATTING_GUIDE](https://doutorai.pt/en/components/skills__formatting-guide): Quick reference for using the market_research.sty style package.
- [FORMATTING_GUIDE](https://doutorai.pt/en/components/skills__formatting-guide-2): This guide provides quick reference for using the hypothesis generation LaTeX template and style package. For complete documentation, see SKILL.md.
- [FORMS](https://doutorai.pt/en/components/skills__forms): Complete guide for processing PDF forms in production environments.
- [FORMS](https://doutorai.pt/en/components/skills__forms-2): If you need to fill out a PDF form, first check to see if the PDF has fillable form fields. Run this script from this file's directory: python scripts/check_fil
- [File Path Traversal Testing](https://doutorai.pt/en/components/skills__file-path-traversal-testing): This skill should be used when the user asks to "test for directory traversal", "exploit path traversal vulnerabilities", "read arbitrary files through web appl
- [GeistMono-Bold](https://doutorai.pt/en/components/skills__geistmono-bold): GDEFd f | xGPOS/ ' RGSUB YT ,OS/2 5- STAT B Dcmap8 "gasp $ glyf head!D; 6hhea L $hmtx[3 loca l maxp n , metag p /name > post@# # 9 =preph N i X6 ; ( dlng ( sln
- [GeistMono-OFL](https://doutorai.pt/en/components/skills__geistmono-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [Git Commit Helper](https://doutorai.pt/en/components/skills__git-commit-helper): Generate descriptive commit messages by analyzing git diffs. Use when the user asks for help writing commit messages or reviewing staged changes.
- [Gloock-OFL](https://doutorai.pt/en/components/skills__gloock-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [Gloock-Regular](https://doutorai.pt/en/components/skills__gloock-regular): GDEF5' D RGPOS 8 >L 6GSUB0c P OS/2W cmap h1 cvt f vfpgmb/ ~ 0@ gasp glyff u head#0, >P>?Q?@ @p@A AAB;C CDqE2EhEFFG,GGHNHHIDIIJmJK KKKKKL L=LLLM MZMMNONNO,OP PiP
- [HOOK_MECHANISMS](https://doutorai.pt/en/components/skills__hook-mechanisms): Technical deep dive into how the UserPromptSubmit and PreToolUse hooks work.
- [HOW_TO_USE](https://doutorai.pt/en/components/skills__how-to-use): Hey Claude - I just added the "app-store-optimization" skill. Can you help me optimize my app's presence on the App Store and Google Play?
- [HTML Injection Testing](https://doutorai.pt/en/components/skills__html-injection-testing): This skill should be used when the user asks to "test for HTML injection", "inject HTML into web pages", "perform HTML injection attacks", "deface web applicati
- [Hook Development](https://doutorai.pt/en/components/skills__hook-development): This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", 
- [IBMPlexMono-OFL](https://doutorai.pt/en/components/skills__ibmplexmono-ofl): Copyright © 2017 IBM Corp. with Reserved Font Name "Plex
- [IBMPlexMono-Regular](https://doutorai.pt/en/components/skills__ibmplexmono-regular): GDEF22w GPOS'? p GSUB !d OS/2 4 cmap+E | cvt 6 @fpgm Y7 H sgasp ! , glyf F ]T Nhead oec 6hhea F \ $hmtx1 d loca2 n X maxp B ́ postQ \ :t "prep'F i X Hn_ 2 + P B
- [IBMPlexSerif-Bold](https://doutorai.pt/en/components/skills__ibmplexserif-bold): GDEF/r/g GPOSs J :GSUBO OS/2p 8 cmap5 cvt Z | Dfpgm Y7 L sgasp ! , glyf: )0 ~&head 6hhea 9 \ $hmtxSa "t locaq! maxp [ q K l K b F R { 8 J p X K X ^ > : oP ; IBM
- [IBMPlexSerif-Italic](https://doutorai.pt/en/components/skills__ibmplexserif-italic): GDEF/_/ GPOSAw3 J GSUBO t OS/2mz D cmap5 cvt Nfpgm Y7 \ sgasp ! , glyf}KP head 5 6hhea [ \ $hmtxZO| " loca u maxp [ r ) I l K 2 L A D - / C T \ e 8 J p 8 X K X 
- [IDOR Vulnerability Testing](https://doutorai.pt/en/components/skills__idor-vulnerability-testing): This skill should be used when the user asks to "test for insecure direct object references," "find IDOR vulnerabilities," "exploit broken access control," "enu
- [IMPLEMENTATION_GUIDE](https://doutorai.pt/en/components/skills__implementation-guide): Use this checklist to ensure the Marketing Skill is properly deployed and ready for your team.
- [IMPLEMENTATION_GUIDE](https://doutorai.pt/en/components/skills__implementation-guide-2): Use this checklist to ensure the DevOps IaC Engineer Skill is properly deployed and ready for your team.
- [IMPLEMENTATION_SUMMARY](https://doutorai.pt/en/components/skills__implementation-summary): Successfully implemented a comprehensive clinical reports skill for the Claude Scientific Writer project.
- [IMPLEMENTATION_SUMMARY](https://doutorai.pt/en/components/skills__implementation-summary-2): Original SKILL.md preserved as _SKILL_old.md for reference.
- [INSTALLATION](https://doutorai.pt/en/components/skills__installation): Complete installation instructions for all platforms and use cases.
- [INSTALLATION_GUIDE](https://doutorai.pt/en/components/skills__installation-guide): This installs support for all file formats and features.
- [InstrumentSans-Bold](https://doutorai.pt/en/components/skills__instrumentsans-bold): M "6j] z }r:C,%#+ % eLA_3wb52(" + % eMltd K" 2 b w 3 %5! p Å #ll 2 = S5! 2 =ll 0 X e 7 ' O yO yO yOOO yO 2 t s S5! '"&54632 "&54632 2 ++ -- ++ -- =ll)" "))" ") 
- [InstrumentSans-BoldItalic](https://doutorai.pt/en/components/skills__instrumentsans-bolditalic): GDEF:2+ P fGPOS X t GSUB h T OS/2b2 STAT_ A Vcmap0- &gasp glyf eT 7| ehead&d 6hhea y ; F Q \ h ' 2 = H S ^ i u ) 4 ? J e p { & 1 = H S _ k v , 8 C m x 2 r r & a
- [InstrumentSans-Italic](https://doutorai.pt/en/components/skills__instrumentsans-italic): GDEF.4V T GPOS { 4 sGSUB h 4 OS/2a | STAT_ C1 Zcmap0- &gasp glyf 7\ ehead&Id 6hhea } I \ g u R z ! , 7 B M X B ~ @ K V a m , 7 B M X c n z # . 9 D O j u + 6 B M
- [InstrumentSans-OFL](https://doutorai.pt/en/components/skills__instrumentsans-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [InstrumentSerif-Italic](https://doutorai.pt/en/components/skills__instrumentserif-italic): GDEF! BGPOS;K 1 7 GSUBsIB \OS/2 cmap㷫 cvt h 4fpgmb.{ # gasp glyf / h head! 6hhea o D $hmtxf ( loca. X maxp k $ namew) postp[ prephFȜ 4 y ( T Rh ? y D D 3 ) _ >>
- [InstrumentSerif-Regular](https://doutorai.pt/en/components/skills__instrumentserif-regular): GDEF" >GPOS l 1 >FGSUBsIB \OS/2 cmap㷫 cvt h 4fpgmb.{ # gasp glyfr o head!? 6hhea D $hmtxS ! , locaI o maxp i $ namexG postph prephFȜ 4 y ( T >x y L L 3 ) )m _ %
- [Italiana-OFL](https://doutorai.pt/en/components/skills__italiana-ofl): Copyright (c) 2011, Santiago Orozco (hi@typemade.mx), with Reserved Font Name "Italiana".
- [Italiana-Regular](https://doutorai.pt/en/components/skills__italiana-regular): FL6M5e)' 76? d /! ; % & # P ( 'qn\H ( :XXj &8 '6 4&'&/ 7 .. ) d ZɣN' P ],[ g \ #5 '7#53'7 53 7 3 # ' v vv v v vv v \v v v vv v v v j ( 353 3 # #5#jPP F S ? #& -
- [JetBrainsMono-OFL](https://doutorai.pt/en/components/skills__jetbrainsmono-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [Jura-Light](https://doutorai.pt/en/components/skills__jura-light): GDEFvDu $ 4GPOSZpL [\ BGSUB [A OS/2¡W STATyi cmapa Zgasp glyf head ٮ 6hhea / 4J4V4b4n44445 5T556 6^6667C7w778(8G8x88939p9: :::; ;Z;;; >V>>?#???@F@A AiAABABBC CJ
- [Jura-Medium](https://doutorai.pt/en/components/skills__jura-medium): / Y e q 7>~>>?I??@ @j@AAAAB BdBC C4CmCCCCD DPDDDDDDDDE3EEF0F8FFG GvGH HKHHHHI I_IIIJ1JNJJJJK7K}L L^LLM MMMN N7NPNzNNO OaOOP P4PUPPPPQ:Q QR RsRRS SESST TVTTU:UUU
- [Jura-OFL](https://doutorai.pt/en/components/skills__jura-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [LCP](https://doutorai.pt/en/components/skills__lcp): Largest Contentful Paint (LCP) measures when the largest content element in the viewport becomes visible. This is typically:
- [LICENSE](https://doutorai.pt/en/components/skills__license): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-2): The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
- [LICENSE](https://doutorai.pt/en/components/skills__license-3): The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
- [LICENSE](https://doutorai.pt/en/components/skills__license-4): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-5): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-6): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-7): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-8): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-9): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-10): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-11): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-12): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-13): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-14): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-15): These n8n skills are sourced from the n8n-skills repository.
- [LICENSE](https://doutorai.pt/en/components/skills__license-16): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-17): The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
- [LICENSE](https://doutorai.pt/en/components/skills__license-18): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-19): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-20): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-21): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-22): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-23): The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
- [LICENSE](https://doutorai.pt/en/components/skills__license-24): The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
- [LICENSE](https://doutorai.pt/en/components/skills__license-25): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-26): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-27): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-28): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-29): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-30): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-31): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-32): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-33): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-34): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-35): © 2025 Anthropic, PBC. All rights reserved.
- [LICENSE](https://doutorai.pt/en/components/skills__license-36): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-37): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-38): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-39): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-40): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-41): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-42): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-43): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-44): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-45): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-46): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-47): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-48): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-49): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-50): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-51): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-52): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-53): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-54): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-55): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-56): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-57): The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
- [LICENSE](https://doutorai.pt/en/components/skills__license-58): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-59): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-60): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-61): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-62): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-63): The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
- [LICENSE](https://doutorai.pt/en/components/skills__license-64): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LICENSE](https://doutorai.pt/en/components/skills__license-65): License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
- [LibreBaskerville-OFL](https://doutorai.pt/en/components/skills__librebaskerville-ofl): Copyright 2012 The Libre Baskerville Project Authors (https://github.com/impallari/Libre-Baskerville) with Reserved Font Name Libre Baskerville.
- [LibreBaskerville-Regular](https://doutorai.pt/en/components/skills__librebaskerville-regular): 3 M u 0 (T()>)))))as +E++, ,(,:,L,X,d,p,,,,,,,- - -&-@-R-^-j----.W.i./1/=/O/[/m/y/0 0|0001 1 1 121>1J11212=2}2222223 3B33334 4 4 4,484L4X4d4z445 5 55556 667P7\7
- [Linux Privilege Escalation](https://doutorai.pt/en/components/skills__linux-privilege-escalation): This skill should be used when the user asks to "escalate privileges on Linux", "find privesc vectors on Linux systems", "exploit sudo misconfigurations", "abus
- [Linux Production Shell Scripts](https://doutorai.pt/en/components/skills__linux-production-shell-scripts): This skill should be used when the user asks to "create bash scripts", "automate Linux tasks", "monitor system resources", "backup files", "manage users", or "w
- [Lora-Bold](https://doutorai.pt/en/components/skills__lora-bold): ; E " - a l x $ 0 -J-.".~../ / /)/t/|//////0?0J0001;12 2\23 3[3424:4444456556?67 78 8]89B9: :m:;$;;<> )> >?)?~?@B@J@V@A AaAB BxBBBBBBBC CJCVCbCnCCCCCCCD DUDE0E{
- [Lora-BoldItalic](https://doutorai.pt/en/components/skills__lora-bolditalic): GDEFV 8 GPOSX2 P GSUBI8A 8 @OS/2l\O STAT @cmapEr-o gasp glyf5#l @ )head&ܛ 6hhea $ wght ital _ X K X ^ 2 , P K CYRE & . q r I V I 8 : [ ^ {
- [Lora-OFL](https://doutorai.pt/en/components/skills__lora-ofl): Copyright 2011 The Lora Project Authors (https://github.com/cyrealtype/Lora-Cyrillic), with Reserved Font Name "Lora".
- [MCP Integration](https://doutorai.pt/en/components/skills__mcp-integration): This skill should be used when the user asks to "add MCP server", "integrate MCP", "configure MCP in plugin", "use .mcp.json", "set up Model Context Protocol", 
- [MOTION_CORRECTION](https://doutorai.pt/en/components/skills__motion-correction): Mechanical drift during acute probe insertion is a major challenge for Neuropixels recordings. This guide covers detection, estimation, and correction of motion
- [Manifest](https://doutorai.pt/en/components/skills__manifest): Install and configure the Manifest observability plugin for your agents. Use when setting up telemetry, configuring API keys or endpoints, troubleshooting plugi
- [Metasploit Framework](https://doutorai.pt/en/components/skills__metasploit-framework): This skill should be used when the user asks to "use Metasploit for penetration testing", "exploit vulnerabilities with msfconsole", "create payloads with msfve
- [NOTICE](https://doutorai.pt/en/components/skills__notice): This skill includes material derived from the Microsoft playwright-cli repository.
- [NationalPark-OFL](https://doutorai.pt/en/components/skills__nationalpark-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [NationalPark-Regular](https://doutorai.pt/en/components/skills__nationalpark-regular): GDEF΁ ( GPOSA I 0GSUBi^8J " rOS/2W STATxl .cmap Tb gasp glyf䫣+ z4 Thead) 6hhea E | ( T J Z f r ~ ( 4 p ( 4 ? O Z , 8 H T l x + u , 8 D P \ l x W c J V b n z ' 3
- [Network 101](https://doutorai.pt/en/components/skills__network-101): This skill should be used when the user asks to "set up a web server", "configure HTTP or HTTPS", "perform SNMP enumeration", "configure SMB shares", "test netw
- [NothingYouCouldDo-OFL](https://doutorai.pt/en/components/skills__nothingyoucoulddo-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [NothingYouCouldDo-Regular](https://doutorai.pt/en/components/skills__nothingyoucoulddo-regular): PGDEF 8 OS/2g7y cmap / gasp glyfԊr ( hhead H 6hhea ( $ $hmtx loca&Q | rmaxp K name\œ{ postgZ 6preph 7 8 @ i 9M 8 H h_ Q C f . ! 0 # J " H s - $ / j H
- [OCR](https://doutorai.pt/en/components/skills__ocr): Extract text from scanned PDFs and image-based documents.
- [OPENROUTER_INTEGRATION](https://doutorai.pt/en/components/skills__openrouter-integration): This MarkItDown skill has been configured to use OpenRouter instead of direct OpenAI API access. OpenRouter provides a unified API gateway to access 100+ AI mod
- [OPERATION_PATTERNS](https://doutorai.pt/en/components/skills__operation-patterns): Common node configuration patterns organized by node type and operation.
- [Outfit-Bold](https://doutorai.pt/en/components/skills__outfit-bold): GDEFX{ $ GPOSΧ} % NnGSUBz T dOS/2sn STATykI cmapJ}V ( Lgasp glyfs t8 d(headZ 6hhea Y maxp name t post: 0 preph M % $ wght _ 6 . & b w Z o . & " R x
- [Outfit-OFL](https://doutorai.pt/en/components/skills__outfit-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [Outfit-Regular](https://doutorai.pt/en/components/skills__outfit-regular): GDEFUpu ( GPOSj[W %H N|GSUBC | \ 4OS/2rDn STATxl .cmapJ}V Lgasp glyfC5 s bhead)Z/ 6hhea X X K X ^ 2 frag x F > 6 . & F X J Z . & " 6 ( . & " . d 2
- [PATTERNS_LIBRARY](https://doutorai.pt/en/components/skills__patterns-library): Ready-to-use regex and glob patterns for skill triggers. Copy and customize for your skills.
- [PDF Processing Pro](https://doutorai.pt/en/components/skills__pdf-processing-pro): Production-ready PDF processing with forms, tables, OCR, validation, and batch operations. Use when working with complex PDF workflows in production environment
- [PREPROCESSING](https://doutorai.pt/en/components/skills__preprocessing): Comprehensive preprocessing techniques for Neuropixels neural recordings.
- [Pentest Checklist](https://doutorai.pt/en/components/skills__pentest-checklist): This skill should be used when the user asks to "plan a penetration test", "create a security assessment checklist", "prepare for penetration testing", "define 
- [Pentest Commands](https://doutorai.pt/en/components/skills__pentest-commands): This skill should be used when the user asks to "run pentest commands", "scan with nmap", "use metasploit exploits", "crack passwords with hydra or john", "scan
- [PixelifySans-Medium](https://doutorai.pt/en/components/skills__pixelifysans-medium): GDEF g $ GPOSǗ 0 #GSUBaBu l ~OS/2dUVP STATyj cmap gasp glyfw; Tx s head% 6hhea @ a c " & ( 1 " & ( . 7 C O [ g s " . : F R ^ } ! - 9 E \ " . : F R ^ j u % D k !
- [PixelifySans-OFL](https://doutorai.pt/en/components/skills__pixelifysans-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [Plugin Settings](https://doutorai.pt/en/components/skills__plugin-settings): This skill should be used when the user asks about "plugin settings", "store plugin configuration", "user-configurable plugin", ".local.md files", "plugin state
- [Plugin Structure](https://doutorai.pt/en/components/skills__plugin-structure): This skill should be used when the user asks to "create a plugin", "scaffold a plugin", "understand plugin structure", "organize plugin components", "set up plu
- [PoiretOne-OFL](https://doutorai.pt/en/components/skills__poiretone-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [PoiretOne-Regular](https://doutorai.pt/en/components/skills__poiretone-regular): pGDEF GPOSAM GSUB#v$ h .OS/2/g cmap&1= 0 dgasp glyf Ω 1 &heado 6hhea D $hmtx#Ps loca maxp ? $ name] M ( post. preph V } , e ; d ) Y ? P D k 4 k : c O x 4 b : p 
- [Privilege Escalation Methods](https://doutorai.pt/en/components/skills__privilege-escalation-methods): This skill should be used when the user asks to "escalate privileges", "get root access", "become administrator", "privesc techniques", "abuse sudo", "exploit S
- [QUALITY_METRICS](https://doutorai.pt/en/components/skills__quality-metrics): Comprehensive guide to unit quality assessment using SpikeInterface metrics and Allen/IBL standards.
- [README](https://doutorai.pt/en/components/skills__readme): Comprehensive skill for building on Shopify platform: apps, extensions, themes, and API integrations.
- [README](https://doutorai.pt/en/components/skills__readme-2): A manual-only skill for minimizing total codebase size by measuring success through final code amount, not effort. Biases toward deletion and aggressive simplif
- [README](https://doutorai.pt/en/components/skills__readme-3): Transform passive learning into active doing. This skill converts YouTube transcripts, articles, tutorials, and other educational content into actionable Ship-L
- [README](https://doutorai.pt/en/components/skills__readme-4): A systematic skill for transforming vague requirements into actionable Product Requirements Documents (PRDs) through focused dialogue and iterative clarificatio
- [README](https://doutorai.pt/en/components/skills__readme-5): A strategic product thinking skill that helps identify 10x opportunities and high-leverage improvements for your product.
- [README](https://doutorai.pt/en/components/skills__readme-6): A comprehensive skill for creating high-quality, production-ready git commits that are easy to review, safe to ship, and follow best practices.
- [README](https://doutorai.pt/en/components/skills__readme-7): Fixes performance bottlenecks. Tracks performance before and after to quantify improvement.
- [README](https://doutorai.pt/en/components/skills__readme-8): A skill for Claude Code that analyzes code naming conventions and suggests better variable, function, class, and other identifier names based on context and ind
- [README](https://doutorai.pt/en/components/skills__readme-9): A Claude Code skill that removes signs of AI-generated writing from text, making it sound more natural and human.
- [README](https://doutorai.pt/en/components/skills__readme-10): A skill for Claude Code that helps you write, update, and improve README files tailored to your specific project type and audience.
- [README](https://doutorai.pt/en/components/skills__readme-11): A comprehensive evaluation framework for assessing Agent Skill quality against official specifications and best practices. This skill provides multi-dimensional
- [README](https://doutorai.pt/en/components/skills__readme-12): The web UI runs skills in a sandbox without network access, which this skill requires for browser automation. You must use Claude Code locally on your machine.
- [README](https://doutorai.pt/en/components/skills__readme-13): Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming an
- [README](https://doutorai.pt/en/components/skills__readme-14): A Claude Code skill for frontend developers to document data needs and API requirements for backend developers in a collaborative, non-prescriptive way.
- [README](https://doutorai.pt/en/components/skills__readme-15): A Claude Code skill that generates comprehensive API handoff documentation for frontend developers after backend work is complete.
- [README](https://doutorai.pt/en/components/skills__readme-16): Generate polished daily standup updates through an interactive interview process, optionally enriched with data from GitHub, Git, and Jira integrations.
- [README](https://doutorai.pt/en/components/skills__readme-17): A skill for navigating difficult workplace conversations and delivering constructive feedback using proven frameworks.
- [README](https://doutorai.pt/en/components/skills__readme-18): A structured framework for navigating challenging workplace conversations with confidence and effectiveness. This skill provides comprehensive guidance for prep
- [README](https://doutorai.pt/en/components/skills__readme-19): The Session Handoff skill creates comprehensive handoff documents that enable fresh AI agents to seamlessly continue work with zero ambiguity. It solves the lon
- [README](https://doutorai.pt/en/components/skills__readme-20): A comprehensive guide for effective professional communication in software development contexts, covering email structure, team messaging etiquette, meeting com
- [README](https://doutorai.pt/en/components/skills__readme-21): A skill that applies William Strunk Jr.'s timeless writing principles to produce clearer, stronger, more professional prose while avoiding common AI writing pat
- [README](https://doutorai.pt/en/components/skills__readme-22): A comprehensive App Store Optimization (ASO) skill that provides complete capabilities for researching, optimizing, and tracking mobile app performance on the A
- [README](https://doutorai.pt/en/components/skills__readme-23): Proven architectural patterns for building n8n workflows.
- [README](https://doutorai.pt/en/components/skills__readme-24): Expert guidance for writing Python code in n8n Code nodes.
- [README](https://doutorai.pt/en/components/skills__readme-25): Expert guidance for writing JavaScript code in n8n Code nodes.
- [README](https://doutorai.pt/en/components/skills__readme-26): Expert guidance for interpreting and fixing n8n validation errors.
- [README](https://doutorai.pt/en/components/skills__readme-27): Expert guide for using n8n-mcp MCP tools effectively.
- [README](https://doutorai.pt/en/components/skills__readme-28): Expert guide for writing correct n8n expressions in workflows.
- [README](https://doutorai.pt/en/components/skills__readme-29): Expert guidance for operation-aware node configuration with property dependencies.
- [README](https://doutorai.pt/en/components/skills__readme-30): Natural language interaction with Jira for managing issues, sprints, and workflows. This skill enables Claude to view, create, update, and transition Jira ticke
- [README](https://doutorai.pt/en/components/skills__readme-31): This directory contains comprehensive reference materials for SAELens.
- [README](https://doutorai.pt/en/components/skills__readme-32): This directory contains comprehensive reference materials for pyvene.
- [README](https://doutorai.pt/en/components/skills__readme-33): A Claude Code skill for debugging and triaging with Datadog logs, metrics, and dashboards.
- [README](https://doutorai.pt/en/components/skills__readme-34): This directory contains comprehensive reference materials for nnsight.
- [README](https://doutorai.pt/en/components/skills__readme-35): See benchmarks/results/ for full methodology and solutions.
- [README](https://doutorai.pt/en/components/skills__readme-36): After timeout optimization, the multi-agent RARV pipeline matches single-agent performance.
- [README](https://doutorai.pt/en/components/skills__readme-37): This directory contains screenshots for the Loki Mode README.
- [README](https://doutorai.pt/en/components/skills__readme-38): Single script that handles everything: prerequisites, setup, Vibe Kanban monitoring, and autonomous execution with auto-resume.
- [README](https://doutorai.pt/en/components/skills__readme-39): Video demonstration of Loki Mode - Multi-agent autonomous startup system.
- [README](https://doutorai.pt/en/components/skills__readme-40): A comprehensive guide for using Perplexity AI tools effectively within Claude Code. This skill helps you choose the right search and research tool for your need
- [README](https://doutorai.pt/en/components/skills__readme-41): A comprehensive Claude Code skill for QA engineers to generate test plans, manual test cases, regression test suites, Figma design validations, and structured b
- [README](https://doutorai.pt/en/components/skills__readme-42): This directory contains comprehensive reference materials for TransformerLens.
- [README](https://doutorai.pt/en/components/skills__readme-43): Just as the master craftsman took rough timber and carved it into a puppet that came to life, Gepetto transforms your rough feature sketches into detailed, batt
- [README](https://doutorai.pt/en/components/skills__readme-44): Harness the power of Google's Gemini 3 Pro for comprehensive code analysis, plan reviews, and big context processing. This skill enables Claude Code to leverage
- [README](https://doutorai.pt/en/components/skills__readme-46): This directory contains official LaTeX templates for major machine learning and AI conferences.
- [README](https://doutorai.pt/en/components/skills__readme-47): I have completely merged the two AAAI 2026 versions (anonymous submission and camera-ready) into a single unified template file aaai2026-unified-template.tex.
- [README](https://doutorai.pt/en/components/skills__readme-48): This directory contains the latest LaTeX templates for ACL conferences.
- [README](https://doutorai.pt/en/components/skills__readme-50): This repository contains Postgres performance optimization rules optimized for AI agents and LLMs.
- [README](https://doutorai.pt/en/components/skills__readme-51): Nano Banana Pro creates publication-quality diagrams automatically - no coding, no templates, no manual drawing required.
- [README](https://doutorai.pt/en/components/skills__readme-52): Create professional, publication-ready research posters for conferences and academic presentations using LaTeX.
- [README](https://doutorai.pt/en/components/skills__readme-53): This skill provides real-time research information lookup using Perplexity's Sonar Pro Search model through OpenRouter.
- [README](https://doutorai.pt/en/components/skills__readme-54): Professional clinical decision support documents for medical professionals in pharmaceutical and clinical research settings.
- [README](https://doutorai.pt/en/components/skills__readme-55): Skill for generating concise, clinician-focused medical treatment plans across all clinical specialties. Provides LaTeX/PDF templates with SMART goal frameworks
- [README](https://doutorai.pt/en/components/skills__readme-56): Comprehensive skill for writing competitive research grant proposals focused on four major U.S. funding agencies: - NSF (National Science Foundation) - NIH (Nat
- [README](https://doutorai.pt/en/components/skills__readme-57): Comprehensive skill for writing clinical reports including case reports, diagnostic reports, clinical trial reports, and patient documentation. Provides full su
- [README](https://doutorai.pt/en/components/skills__readme-58): This skill provides comprehensive support for converting various file formats to Markdown using Microsoft's MarkItDown tool.
- [README](https://doutorai.pt/en/components/skills__readme-59): A Claude Code skill for creating professional, visually appealing Marp presentation slides with 7 pre-designed themes and built-in best practices.
- [README](https://doutorai.pt/en/components/skills__readme-60): A comprehensive Claude skill designed to help marketing teams plan, execute, and measure marketing campaigns across all channels.
- [README](https://doutorai.pt/en/components/skills__readme-61): A Claude Code skill for generating memes using the memegen.link API and textual meme formats.
- [README](https://doutorai.pt/en/components/skills__readme-62): Generate software architecture documentation using C4 model diagrams in Mermaid syntax.
- [README](https://doutorai.pt/en/components/skills__readme-63): A Claude Code skill for creating, editing, and managing draw.io diagrams with professional quality standards.
- [README](https://doutorai.pt/en/components/skills__readme-64): A comprehensive guide for creating professional software diagrams using Mermaid's text-based syntax. This skill enables you to visualize system architecture, do
- [README](https://doutorai.pt/en/components/skills__readme-65): A Claude Code skill for efficiently working with Excalidraw diagram files through intelligent subagent delegation.
- [README](https://doutorai.pt/en/components/skills__readme-66): These scripts help validate, test, and lint hook implementations before deployment.
- [README](https://doutorai.pt/en/components/skills__readme-67): A comprehensive skill for working with Material-UI v7 components, styling, and best practices in React applications.
- [README](https://doutorai.pt/en/components/skills__readme-68): A comprehensive skill for building and managing Claude Code plugins with proper structure, manifests, and marketplace integration.
- [README](https://doutorai.pt/en/components/skills__readme-69): Updates create new versions; the agent object is immutable per version.
- [README](https://doutorai.pt/en/components/skills__readme-70): Updates create new versions; the agent object is immutable per version.
- [README](https://doutorai.pt/en/components/skills__readme-71): If cache_read_input_tokens is zero across repeated identical-prefix requests, a silent invalidator is at work - Date.now() or a UUID in the system prompt, non-d
- [README](https://doutorai.pt/en/components/skills__readme-72): See shared/managed-agents-tools.md §Vaults for creating vaults and adding credentials.
- [README](https://doutorai.pt/en/components/skills__readme-73): The Claude Agent SDK provides a higher-level interface for building AI agents with built-in tools, safety features, and agentic capabilities.
- [README](https://doutorai.pt/en/components/skills__readme-74): Cache large context to reduce costs (up to 90% savings). Caching is a prefix match - any byte change anywhere in the prefix invalidates everything after it. For
- [README](https://doutorai.pt/en/components/skills__readme-75): See shared/managed-agents-tools.md §Vaults for creating vaults and adding credentials.
- [README](https://doutorai.pt/en/components/skills__readme-76): The Claude Agent SDK provides a higher-level interface for building AI agents with built-in tools, safety features, and agentic capabilities.
- [README](https://doutorai.pt/en/components/skills__readme-77): Updates create new versions; the agent object is immutable per version.
- [README](https://doutorai.pt/en/components/skills__readme-78): Updates create new versions; the agent object is immutable per version.
- [README](https://doutorai.pt/en/components/skills__readme-79): This repository contains Postgres performance optimization rules optimized for AI agents and LLMs.
- [README](https://doutorai.pt/en/components/skills__readme-80): A structured repository for creating and maintaining React Best Practices optimized for agents and LLMs.
- [README](https://doutorai.pt/en/components/skills__readme-81): Local simulator for Cloudflare Workers development/testing. Runs Workers in workerd sandbox implementing runtime APIs - no internet required.
- [README](https://doutorai.pt/en/components/skills__readme-82): Cache Reserve is Cloudflare's persistent, large-scale cache storage layer built on R2. It acts as the ultimate upper-tier cache, storing cacheable content for e
- [README](https://doutorai.pt/en/components/skills__readme-83): Expert guidance for Cloudflare Workers AI - serverless GPU-powered AI inference at the edge.
- [README](https://doutorai.pt/en/components/skills__readme-84): Expert guidance for implementing unlimited-cardinality analytics at scale using Cloudflare Workers Analytics Engine.
- [README](https://doutorai.pt/en/components/skills__readme-85): Expert guidance for building real-time audio/video/data applications using Cloudflare Realtime SFU (Selective Forwarding Unit).
- [README](https://doutorai.pt/en/components/skills__readme-86): Persistent storage API for Durable Objects with SQLite and KV backends, PITR, and automatic concurrency control.
- [README](https://doutorai.pt/en/components/skills__readme-87): Enterprise-grade bot detection, protection, and mitigation using ML/heuristics, bot scores, JavaScript detections, and verified bot handling.
- [README](https://doutorai.pt/en/components/skills__readme-89): Expert guidance for implementing Cloudflare AI Search (formerly AutoRAG), Cloudflare's managed semantic search and RAG service.
- [README](https://doutorai.pt/en/components/skills__readme-90): Expert guidance for implementing Cloudflare Turnstile - a smart CAPTCHA alternative that protects websites from bots without showing traditional CAPTCHA puzzles
- [README](https://doutorai.pt/en/components/skills__readme-91): Cloudflare Email Routing enables custom email addresses for your domain that route to verified destination addresses. It's free, privacy-focused (no storage/acc
- [README](https://doutorai.pt/en/components/skills__readme-92): Specialized Workers that consume execution events from producer Workers for logging, debugging, analytics, and observability.
- [README](https://doutorai.pt/en/components/skills__readme-93): Expert guidance for implementing Cloudflare TURN Service in WebRTC applications.
- [README](https://doutorai.pt/en/components/skills__readme-94): Connect Cloudflare Workers to private networks and internal infrastructure using TCP Sockets.
- [README](https://doutorai.pt/en/components/skills__readme-95): Expert guidance for implementing Cloudflare AI Gateway - a universal gateway for AI model providers with analytics, caching, rate limiting, and routing capabili
- [README](https://doutorai.pt/en/components/skills__readme-96): Expert guidance for Cloudflare R2 SQL - serverless distributed query engine for Apache Iceberg tables.
- [README](https://doutorai.pt/en/components/skills__readme-97): Accelerates database queries from Workers via connection pooling, edge setup, query caching.
- [README](https://doutorai.pt/en/components/skills__readme-98): Process incoming emails programmatically using Cloudflare Workers runtime.
- [README](https://doutorai.pt/en/components/skills__readme-99): Expert guidance for building, deploying, and optimizing Cloudflare Workers applications.
- [README](https://doutorai.pt/en/components/skills__readme-101): Expert guidance on Cloudflare Workers Bindings - the runtime APIs that connect Workers to Cloudflare platform resources.
- [README](https://doutorai.pt/en/components/skills__readme-102): Account-level encrypted secret management for Workers and AI Gateway.
- [README](https://doutorai.pt/en/components/skills__readme-104): Multi-tenant platform with isolated customer code execution at scale.
- [README](https://doutorai.pt/en/components/skills__readme-105): Expert guidance for Cloudflare R2 Data Catalog - Apache Iceberg catalog built into R2 buckets.
- [README](https://doutorai.pt/en/components/skills__readme-106): Automatic workload placement optimization to minimize latency by running Workers closer to backend infrastructure rather than end users.
- [README](https://doutorai.pt/en/components/skills__readme-107): Cloudflare Spectrum provides security and acceleration for ANY TCP or UDP-based application. It's a global Layer 4 (L4) reverse proxy running on Cloudflare's ed
- [README](https://doutorai.pt/en/components/skills__readme-108): V8-based JS/Wasm runtime powering Cloudflare Workers. Use as app server, dev tool, or HTTP proxy.
- [README](https://doutorai.pt/en/components/skills__readme-109): Globally distributed vector database for AI applications. Store and query vector embeddings for semantic search, recommendations, RAG, and classification.
- [README](https://doutorai.pt/en/components/skills__readme-110): Official CLI for scaffolding Cloudflare Workers and Pages projects with templates, TypeScript, and instant deployment.
- [README](https://doutorai.pt/en/components/skills__readme-111): Cloudflare Argo Smart Routing is a performance optimization service that detects real-time network issues and routes web traffic across the most efficient netwo
- [README](https://doutorai.pt/en/components/skills__readme-112): Use when working with Cloudflare Containers: deploying containerized apps on Workers platform, configuring container-enabled Durable Objects, managing container
- [README](https://doutorai.pt/en/components/skills__readme-113): Expert guidance for API Shield - comprehensive API security suite for discovery, protection, and monitoring.
- [README](https://doutorai.pt/en/components/skills__readme-114): ETL streaming platform for ingesting, transforming, and loading data into R2 with SQL transformations.
- [README](https://doutorai.pt/en/components/skills__readme-115): Official CLI for Cloudflare Workers - develop, manage, and deploy Workers from the command line.
- [README](https://doutorai.pt/en/components/skills__readme-116): Guide for working with Cloudflare's REST API - authentication, SDK usage, common patterns, and troubleshooting.
- [README](https://doutorai.pt/en/components/skills__readme-117): Expert guidance for deploying and configuring static assets with Cloudflare Workers. This skill covers configuration patterns, routing architectures, asset bind
- [README](https://doutorai.pt/en/components/skills__readme-118): Schedule Workers execution using cron expressions. Runs on Cloudflare's global network during underutilized periods.
- [README](https://doutorai.pt/en/components/skills__readme-119): Expert guidance for Cloudflare Pulumi Provider (@pulumi/cloudflare).
- [README](https://doutorai.pt/en/components/skills__readme-120): S3-compatible object storage with zero egress fees, optimized for large file storage and delivery.
- [README](https://doutorai.pt/en/components/skills__readme-121): Snippets execute synchronously in the request path - performance is critical.
- [README](https://doutorai.pt/en/components/skills__readme-122): Expert guidance for Cloudflare Zaraz - server-side tag manager for loading third-party tools at the edge.
- [README](https://doutorai.pt/en/components/skills__readme-123): Cloudflare WAF protects web applications from attacks through managed rulesets and custom rules.
- [README](https://doutorai.pt/en/components/skills__readme-124): Durable multi-step applications with automatic retries, state persistence, and long-running execution.
- [README](https://doutorai.pt/en/components/skills__readme-125): Secure outbound-only connections between infrastructure and Cloudflare's global network.
- [README](https://doutorai.pt/en/components/skills__readme-126): Serverless functions on Cloudflare Pages using Workers runtime. Full-stack dev with file-based routing.
- [README](https://doutorai.pt/en/components/skills__readme-127): Globally-distributed, eventually-consistent key-value store optimized for high read volume and low latency.
- [README](https://doutorai.pt/en/components/skills__readme-128): Cloudflare Agents SDK enables building AI-powered agents on Durable Objects with state, WebSockets, SQL, scheduling, and AI integration.
- [README](https://doutorai.pt/en/components/skills__readme-129): Flexible message queuing for async task processing with guaranteed at-least-once delivery and configurable batching.
- [README](https://doutorai.pt/en/components/skills__readme-130): Serverless live and on-demand video streaming platform with one API.
- [README](https://doutorai.pt/en/components/skills__readme-131): Cloudflare Workers Playground is a browser-based sandbox for instantly experimenting with, testing, and deploying Cloudflare Workers without authentication or s
- [README](https://doutorai.pt/en/components/skills__readme-132): Privacy-first web analytics providing Core Web Vitals, traffic metrics, and user insights without compromising visitor privacy.
- [README](https://doutorai.pt/en/components/skills__readme-133): Secure isolated code execution in containers on Cloudflare's edge. Run untrusted code, manage files, expose services, integrate with AI agents.
- [README](https://doutorai.pt/en/components/skills__readme-134): Expert guidance for building stateful applications with Cloudflare Durable Objects.
- [README](https://doutorai.pt/en/components/skills__readme-136): JAMstack platform for full-stack apps on Cloudflare's global network.
- [README](https://doutorai.pt/en/components/skills__readme-137): Expert guidance for building real-time video and audio applications using Cloudflare RealtimeKit - a comprehensive SDK suite for adding customizable live video 
- [README](https://doutorai.pt/en/components/skills__readme-138): Expert guidance for Cloudflare D1, a serverless SQLite database designed for horizontal scale-out across multiple databases.
- [README](https://doutorai.pt/en/components/skills__readme-139): Private, high-performance connectivity to Cloudflare's network. Enterprise-only.
- [README](https://doutorai.pt/en/components/skills__readme-140): Autonomous, always-on protection against DDoS attacks across L3/4 and L7.
- [README](https://doutorai.pt/en/components/skills__readme-141): A Claude Code skill that transforms bloated agent instruction files into clean, organized documentation using progressive disclosure principles.
- [README](https://doutorai.pt/en/components/skills__readme-142): A comprehensive Claude skill for IT operations professionals, covering infrastructure management, monitoring, incident response, automation, and disaster recove
- [README](https://doutorai.pt/en/components/skills__readme-143): A comprehensive guide teaching when to use useEffect in React, and more importantly, when NOT to use it. This skill is based on official React documentation and
- [README](https://doutorai.pt/en/components/skills__readme-144): Convert web pages to clean Markdown using local browser automation with Puppeteer and Readability.
- [README](https://doutorai.pt/en/components/skills__readme-145): Comprehensive guidance on creating Claude Code slash commands, including file format, frontmatter options, dynamic arguments, and best practices.
- [README](https://doutorai.pt/en/components/skills__readme-146): A comprehensive skill pack for security professionals implementing defense-in-depth security architectures, achieving compliance with industry frameworks, condu
- [README](https://doutorai.pt/en/components/skills__readme-147): A comprehensive skill for designing production-ready database schemas with built-in best practices for both SQL and NoSQL databases.
- [README](https://doutorai.pt/en/components/skills__readme-148): A comprehensive Claude skill designed to help DevOps teams implement infrastructure as code, manage cloud platforms, deploy containerized applications, and main
- [README](https://doutorai.pt/en/components/skills__readme-149): A comprehensive Claude Code skill for creating and evolving production-ready design systems with design tokens, component architecture, accessibility guidelines
- [README](https://doutorai.pt/en/components/skills__readme-150): Comprehensive guidance on Claude Code plugin architecture, directory layout, and best practices.
- [README](https://doutorai.pt/en/components/skills__readme-151): Use the SwarmVault skill when you want a local-first knowledge vault that compiles books, articles, notes, transcripts, chat exports, emails, calendars, dataset
- [README](https://doutorai.pt/en/components/skills__readme-152): A Claude Code skill that converts OpenAPI 3.0 specifications (JSON or YAML) into TypeScript interfaces and type guards.
- [README](https://doutorai.pt/en/components/skills__readme-153): Smart dependency management for any programming language with automatic detection and safe updates.
- [README](https://doutorai.pt/en/components/skills__readme-154): The react-dev skill provides comprehensive TypeScript patterns and best practices for building type-safe React applications. It serves as a complete reference f
- [README](https://doutorai.pt/en/components/skills__readme-155): The replay system records successful error resolutions for future reference, enabling faster problem-solving when similar errors occur.
- [README](https://doutorai.pt/en/components/skills__readme-157): A comprehensive skill for creating optimized, agent-executable slash commands in Claude Code. This skill guides you through the entire process of designing, imp
- [README](https://doutorai.pt/en/components/skills__readme-158): A comprehensive guide to modern Angular development (v20+) optimized for AI agents and LLMs.
- [REFERENCE](https://doutorai.pt/en/components/skills__reference): Complete reference for Google Analytics 4 (GA4) metrics and dimensions.
- [Red Team Tools and Methodology](https://doutorai.pt/en/components/skills__red-team-tools-and-methodology): This skill should be used when the user asks to "follow red team methodology", "perform bug bounty hunting", "automate reconnaissance", "hunt for XSS vulnerabil
- [RedHatMono-OFL](https://doutorai.pt/en/components/skills__redhatmono-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [SEARCH_GUIDE](https://doutorai.pt/en/components/skills__search-guide): Complete guide for finding and understanding n8n nodes.
- [SEO Optimizer](https://doutorai.pt/en/components/skills__seo-optimizer): Search Engine Optimization specialist for content strategy, technical SEO, keyword research, and ranking improvements. Use when optimizing website content, impr
- [SKILL](https://doutorai.pt/en/components/skills__skill): Enable with ALTER TABLE tbl ENABLE ROW LEVEL SECURITY. Create policies: CREATE POLICY user_access ON orders FOR SELECT TO app_users USING (user_id = current_use
- [SKILL](https://doutorai.pt/en/components/skills__skill-2): Apply the ScholarEval framework to systematically evaluate scholarly and research work. This skill provides structured evaluation methodology based on peer-revi
- [SKILL_RULES_REFERENCE](https://doutorai.pt/en/components/skills__skill-rules-reference): Complete schema and configuration reference for .claude/skills/skill-rules.json.
- [SKILL_SUMMARY](https://doutorai.pt/en/components/skills__skill-summary): A comprehensive skill for using Microsoft's MarkItDown tool has been created for the Claude Scientific Writer. This skill enables conversion of 15+ file formats
- [SMTP Penetration Testing](https://doutorai.pt/en/components/skills__smtp-penetration-testing): This skill should be used when the user asks to "perform SMTP penetration testing", "enumerate email users", "test for open mail relays", "grab SMTP banners", "
- [SPIKE_SORTING](https://doutorai.pt/en/components/skills__spike-sorting): Comprehensive guide to spike sorting Neuropixels data.
- [SQL Injection Testing](https://doutorai.pt/en/components/skills__sql-injection-testing): This skill should be used when the user asks to "test for SQL injection vulnerabilities", "perform SQLi attacks", "bypass authentication using SQL injection", "
- [SQLMap Database Penetration Testing](https://doutorai.pt/en/components/skills__sqlmap-database-penetration-testing): This skill should be used when the user asks to "automate SQL injection testing," "enumerate database structure," "extract database credentials using sqlmap," "
- [SSH Penetration Testing](https://doutorai.pt/en/components/skills__ssh-penetration-testing): This skill should be used when the user asks to "pentest SSH services", "enumerate SSH configurations", "brute force SSH credentials", "exploit SSH vulnerabilit
- [STANDARD_LIBRARY](https://doutorai.pt/en/components/skills__standard-library): Complete guide to available Python standard library modules in n8n Code nodes.
- [SUMMARY](https://doutorai.pt/en/components/skills__summary): This directory contains benchmark results for Loki Mode multi-agent system.
- [SUMMARY](https://doutorai.pt/en/components/skills__summary-2): This directory contains benchmark results for Loki Mode multi-agent system.
- [SUMMARY](https://doutorai.pt/en/components/skills__summary-3): This directory contains benchmark results for Loki Mode multi-agent system.
- [SUMMARY](https://doutorai.pt/en/components/skills__summary-4): This directory contains benchmark results for Loki Mode multi-agent system.
- [SUMMARY](https://doutorai.pt/en/components/skills__summary-5): This directory contains benchmark results for Loki Mode multi-agent system.
- [SUMMARY](https://doutorai.pt/en/components/skills__summary-6): This directory contains benchmark results for Loki Mode multi-agent system.
- [Security Scanning Tools](https://doutorai.pt/en/components/skills__security-scanning-tools): This skill should be used when the user asks to "perform vulnerability scanning", "scan networks for open ports", "assess web application security", "scan wirel
- [Shodan Reconnaissance and Pentesting](https://doutorai.pt/en/components/skills__shodan-reconnaissance-and-pentesting): This skill should be used when the user asks to "search for exposed devices on the internet," "perform Shodan reconnaissance," "find vulnerable services using S
- [Silkscreen-OFL](https://doutorai.pt/en/components/skills__silkscreen-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [Silkscreen-Regular](https://doutorai.pt/en/components/skills__silkscreen-regular): GDEF p T dGPOST| X tGSUBGb l NOS/2\ cmapA tcvt F Rfpgmb.| w ] q q q q q q q } q } X K X ^ 2 o UNKN !" H - / ; = A C R X i k x K RX Y cp B B# B' B B @ D$
- [SmoochSans-Medium](https://doutorai.pt/en/components/skills__smoochsans-medium): GDEF $ HGPOSu :x NGSUBY Z OS/2^m STATyj cmap5 gasp glyfL# s d nhead ƛ 6hhea @ @ B B D I K h j q s ! ! $ - 1 2 6 6 9 : ; s % 1 l x ! u
- [SmoochSans-OFL](https://doutorai.pt/en/components/skills__smoochsans-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [TABLES](https://doutorai.pt/en/components/skills__tables): Advanced table extraction strategies for production environments.
- [TASK_018_COMPLETION](https://doutorai.pt/en/components/skills__task-018-completion): Manual end-to-end verification of the Loki Mode test todo app is complete. The application is functionally complete with:
- [TESTING_DOCUMENTATION](https://doutorai.pt/en/components/skills__testing-documentation): This directory contains comprehensive testing and verification documentation for the Loki Mode autonomous Todo application project.
- [TEST_REPORT](https://doutorai.pt/en/components/skills__test-report): Estimated performance gain: 3x faster than using Sonnet for all tasks.
- [TRIGGER_TYPES](https://doutorai.pt/en/components/skills__trigger-types): Complete reference for configuring skill triggers in Claude Code's skill auto-activation system.
- [TROUBLESHOOTING](https://doutorai.pt/en/components/skills__troubleshooting): Complete debugging guide for skill activation problems.
- [TROUBLESHOOTING](https://doutorai.pt/en/components/skills__troubleshooting-2): The ClawHub skill does not bundle the CLI binary by itself. Install the published package and verify it:
- [Tektur-Medium](https://doutorai.pt/en/components/skills__tektur-medium): GDEFv v 8 6GPOSD _ 1GSUB•ʱ OS/2 I STATs @cmapU 9 x gasp glyfѡg Jhead$?xA 6hhea J V b n z . R Z z ! - 9 D P \ h t
- [Tektur-OFL](https://doutorai.pt/en/components/skills__tektur-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [Telegram Bot Builder](https://doutorai.pt/en/components/skills__telegram-bot-builder): This skill should be used when the user asks to "create a Telegram bot", "build a Telegram chatbot", "set up a Telegram webhook", "add inline keyboards to a bot
- [TodoForm](https://doutorai.pt/en/components/skills__todoform): return ( setTitle(e.target.value)} placeholder="Add a new todo..." disabled={isSubmitting} className="todo-input" /> {isSubmitting ? 'Adding...' : 'Add Todo'} )
- [Top 100 Web Vulnerabilities Reference](https://doutorai.pt/en/components/skills__top-100-web-vulnerabilities-reference): This skill should be used when the user asks to "identify web application vulnerabilities", "explain common security flaws", "understand vulnerability categorie
- [VALIDATION_GUIDE](https://doutorai.pt/en/components/skills__validation-guide): Complete guide for validating node configurations and workflows.
- [VERIFICATION_SUMMARY](https://doutorai.pt/en/components/skills__verification-summary): Backend: ✓ express: ^4.18.2 ✓ cors: ^2.8.5 ✓ better-sqlite3: ^9.0.0 ✓ typescript: ^5.3.0 ✓ @types/express: ^4.17.20 ✓ @types/node: ^20.10.0 ✓ @types/better-sqli
- [WORKFLOW_GUIDE](https://doutorai.pt/en/components/skills__workflow-guide): Complete guide for creating, updating, and managing n8n workflows.
- [Windows Privilege Escalation](https://doutorai.pt/en/components/skills__windows-privilege-escalation): This skill should be used when the user asks to "escalate privileges on Windows," "find Windows privesc vectors," "enumerate Windows for privilege escalation," 
- [Wireshark Network Traffic Analysis](https://doutorai.pt/en/components/skills__wireshark-network-traffic-analysis): This skill should be used when the user asks to "analyze network traffic with Wireshark", "capture packets for troubleshooting", "filter PCAP files", "follow TC
- [WordPress Penetration Testing](https://doutorai.pt/en/components/skills__wordpress-penetration-testing): This skill should be used when the user asks to "pentest WordPress sites", "scan WordPress for vulnerabilities", "enumerate WordPress users, themes, or plugins"
- [WorkSans-BoldItalic](https://doutorai.pt/en/components/skills__worksans-bolditalic): GDEFBA 8 GPOS 8lV st ;(GSUB 0 OS/2> p# STAT @cmapMW= gasp glyf>{ head )\ 6hhea dC_ D > 4 " r f L . 8 & 2 ( v , J . & 0
- [WorkSans-OFL](https://doutorai.pt/en/components/skills__worksans-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [Writing Hookify Rules](https://doutorai.pt/en/components/skills__writing-hookify-rules): This skill should be used when the user asks to "create a hookify rule", "write a hook rule", "configure hookify", "add a hookify rule", or needs guidance on ho
- [YoungSerif-OFL](https://doutorai.pt/en/components/skills__youngserif-ofl): This Font Software is licensed under the SIL Open Font License, Version 1.1. This license is copied below, and is also available with a FAQ at: https://openfont
- [YoungSerif-Regular](https://doutorai.pt/en/components/skills__youngserif-regular): GDEF-e/ P GPOS! G >FGSUB> OS/2}Wq cmapY Xcvt n fpgmb/ ~ ' gasp glyf:V 0 head&$# h 6hhea D $hmtxP loca G maxp $ namejn Hpost= 5 prepn w 0 e d ś Fg_ P j 1 K e 9 /
- [__init__](https://doutorai.pt/en/components/skills__init-2): print("✅ Environment ready! All dependencies isolated in .venv/")
- [_contributing](https://doutorai.pt/en/components/skills__contributing): This document provides guidelines for creating effective Postgres best practice rules that work well with AI agents and LLMs.
- [_contributing](https://doutorai.pt/en/components/skills__contributing-2): This document provides guidelines for creating effective Postgres best practice rules that work well with AI agents and LLMs.
- [_sections](https://doutorai.pt/en/components/skills__sections): This file defines all sections, their ordering, impact levels, and descriptions. The section ID (in parentheses) is the filename prefix used to group rules.
- [_sections](https://doutorai.pt/en/components/skills__sections-2): This file defines the rule categories for Postgres best practices. Rules are automatically assigned to sections based on their filename prefix.
- [_sections](https://doutorai.pt/en/components/skills__sections-3): This file defines the rule categories for Postgres best practices. Rules are automatically assigned to sections based on their filename prefix.
- [_sections](https://doutorai.pt/en/components/skills__sections-4): This file defines all sections, their ordering, impact levels, and descriptions. The section ID (in parentheses) is the filename prefix used to group rules.
- [_template](https://doutorai.pt/en/components/skills__template): Brief explanation of the rule and why it matters. This should be clear and concise, explaining the performance implications.
- [_template](https://doutorai.pt/en/components/skills__template-4): Brief explanation of the rule and why it matters. This should be clear and concise, explaining the performance implications.
- [aaai2026](https://doutorai.pt/en/components/skills__aaai2026): %% %% This is file aaai2026.bst', %% generated with the docstrip utility. %% %% The original source files were: %% %% merlin.mbs (with options: head,ay,nat,ed-a
- [aaai2026](https://doutorai.pt/en/components/skills__aaai2026-2): \AtBeginDocument{% \@ifpackageloaded{natbib}% {% % When natbib is in use, set the proper style and fix a few things \let\cite\citep \let\shortcite\citeyearpar \
- [aaai2026-unified-supp](https://doutorai.pt/en/components/skills__aaai2026-unified-supp): % These are recommended to typeset algorithms but not required. \usepackage{algorithm} \usepackage{algorithmic}
- [aaai2026-unified-template](https://doutorai.pt/en/components/skills__aaai2026-unified-template): % % These are recommended to typeset algorithms but not required. See the subsubsection on algorithms. Remove them if you don't have algorithms in your paper. \
- [ab-test-setup](https://doutorai.pt/en/components/skills__ab-test-setup): When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this 
- [academic-cv-builder](https://doutorai.pt/en/components/skills__academic-cv-builder): Format CVs for academic positions, including publications, grants, teaching, and research experience. Use when the user mentions academic CV, faculty job, tenur
- [accessibility](https://doutorai.pt/en/components/skills__accessibility-2): Audit and improve web accessibility following WCAG 2.1 guidelines. Use when asked to "improve accessibility", "a11y audit", "WCAG compliance", "screen reader su
- [acl](https://doutorai.pt/en/components/skills__acl): % This is the LaTex style file for ACL. % The official sources can be found at % % https://github.com/acl-org/acl-style-files/ % % This package is activated by 
- [acl_latex](https://doutorai.pt/en/components/skills__acl-latex): % Change "review" to "final" to generate the final (sometimes called camera-ready) version. % Change to "preprint" to generate a non-anonymous version with page
- [acl_lualatex](https://doutorai.pt/en/components/skills__acl-lualatex): % Change "review" to "final" to generate the final (sometimes called camera-ready) version. % Change to "preprint" to generate a non-anonymous version with page
- [acl_natbib](https://doutorai.pt/en/components/skills__acl-natbib): %%% Modification of BibTeX style file acl_natbib_nourl.bst %%% ... by urlbst, version 0.9.1 (marked with "% urlbst") %%% See and repository %%% Modifications Co
- [action-point-template](https://doutorai.pt/en/components/skills__action-point-template): Each action point lives in its own file at blueprint/action-points/AP-NN- .md. The numbering is global and stable - once an AP gets a number, that number doesn'
- [adapter-system](https://doutorai.pt/en/components/skills__adapter-system): NeMo Evaluator uses an adapter system to process requests and responses between the evaluation engine and model endpoints. The nemo-evaluator core library provi
- [adaptyv](https://doutorai.pt/en/components/skills__adaptyv): Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays
- [adding-reference-mindsets](https://doutorai.pt/en/components/skills__adding-reference-mindsets): Reference mindsets are philosophical foundations for simplicity. They explain why less is more, giving agents deeper calibration beyond the mechanical checkpoin
- [additional_apis](https://doutorai.pt/en/components/skills__additional-apis): Beyond patent search, PEDS, and trademarks, USPTO provides specialized APIs for citations, office actions, assignments, litigation, and other patent data.
- [additional_features](https://doutorai.pt/en/components/skills__additional-features): This document covers additional protocols.io API features including user profiles, recently published protocols, experiment records, and notifications.
- [addon-structure](https://doutorai.pt/en/components/skills__addon-structure): Addons and Gamemodes have a nearly identical folder structure to the garrysmod folder. When making an Addon, all folders are optional and only those that your A
- [address-github-comments](https://doutorai.pt/en/components/skills__address-github-comments): Use when you need to address review or issue comments on an open GitHub Pull Request using the gh CLI.
- [advanced](https://doutorai.pt/en/components/skills__advanced-2): 输出如下。通过观察输出我们可以得到模型的 chat_template。除此以外也可以通过 huggingface repo 来获取模型的 template.
- [advanced](https://doutorai.pt/en/components/skills__advanced-4): For time-periodic Hamiltonians H(t + T) = H(t).
- [advanced](https://doutorai.pt/en/components/skills__advanced-5): This reference covers advanced OMERO operations including permissions, deletion, filesets, and administrative tasks.
- [advanced](https://doutorai.pt/en/components/skills__advanced-6): This reference covers advanced hook patterns and techniques for sophisticated automation workflows.
- [advanced-architecture](https://doutorai.pt/en/components/skills__advanced-architecture): This file is read when the user wants the blueprint workspace to be more than a static markdown wiki - when they want a living, self-improving system with multi
- [advanced-attacks](https://doutorai.pt/en/components/skills__advanced-attacks): When a user authenticates to a computer with unconstrained delegation, their TGT is saved to memory.
- [advanced-cloud-scripts](https://doutorai.pt/en/components/skills__advanced-cloud-scripts): Navigate to https://microsoft.com/devicelogin and enter the code.
- [advanced-event-handler-refs](https://doutorai.pt/en/components/skills__advanced-event-handler-refs): Store callbacks in refs when used in effects that shouldn't re-subscribe on callback changes.
- [advanced-event-handler-refs](https://doutorai.pt/en/components/skills__advanced-event-handler-refs-2): Store callbacks in refs when used in effects that shouldn't re-subscribe on callback changes.
- [advanced-features](https://doutorai.pt/en/components/skills__advanced-features): Advanced features of Marp Core and Marpit.
- [advanced-features](https://doutorai.pt/en/components/skills__advanced-features-2): Advanced configuration, styling, theming, and other powerful features for creating professional diagrams.
- [advanced-full-text-search](https://doutorai.pt/en/components/skills__advanced-full-text-search): LIKE with wildcards can't use indexes. Full-text search with tsvector is orders of magnitude faster.
- [advanced-full-text-search](https://doutorai.pt/en/components/skills__advanced-full-text-search-2): LIKE with wildcards can't use indexes. Full-text search with tsvector is orders of magnitude faster.
- [advanced-jsonb-indexing](https://doutorai.pt/en/components/skills__advanced-jsonb-indexing): JSONB queries without indexes scan the entire table. Use GIN indexes for containment queries.
- [advanced-jsonb-indexing](https://doutorai.pt/en/components/skills__advanced-jsonb-indexing-2): JSONB queries without indexes scan the entire table. Use GIN indexes for containment queries.
- [advanced-patterns](https://doutorai.pt/en/components/skills__advanced-patterns): Research-backed patterns from 2025-2026 literature for enhanced multi-agent orchestration.
- [advanced-patterns](https://doutorai.pt/en/components/skills__advanced-patterns-2): This guide covers advanced patterns for documenting complex architectures including microservices, event-driven systems, deployments, and API documentation.
- [advanced-plugin](https://doutorai.pt/en/components/skills__advanced-plugin): A complex, enterprise-grade plugin with MCP integration and advanced organization.
- [advanced-topics](https://doutorai.pt/en/components/skills__advanced-topics): This document covers SymPy's advanced mathematical capabilities including geometry, number theory, combinatorics, logic and sets, statistics, polynomials, and s
- [advanced-usage](https://doutorai.pt/en/components/skills__advanced-usage-7): Modal automatically handles zero-downtime deployments: 1. New containers are built and started 2. Traffic gradually shifts to new version 3. Old containers drai
- [advanced-usage](https://doutorai.pt/en/components/skills__advanced-usage-10): Qdrant uses Raft consensus for distributed coordination.
- [advanced-usage](https://doutorai.pt/en/components/skills__advanced-usage-15): AWQ (Activation-aware Weight Quantization) is based on the key insight that not all weights in an LLM are equally important. The algorithm:
- [advanced-use-latest](https://doutorai.pt/en/components/skills__advanced-use-latest): Access latest values in callbacks without adding them to dependency arrays. Prevents effect re-runs while avoiding stale closures.
- [advanced-use-latest](https://doutorai.pt/en/components/skills__advanced-use-latest-2): Access latest values in callbacks without adding them to dependency arrays. Prevents effect re-runs while avoiding stale closures.
- [advanced-workflows](https://doutorai.pt/en/components/skills__advanced-workflows): Multi-step command sequences and composition patterns for complex workflows.
- [advanced_features](https://doutorai.pt/en/components/skills__advanced-features-3): Thinking is on by default for gemini-3.1-pro-preview and gemini-3-flash-preview. It can be adjusted by using the thinking_level parameter.
- [advanced_features](https://doutorai.pt/en/components/skills__advanced-features-4): FluidSim supports several forcing mechanisms to maintain turbulence or drive specific dynamics.
- [aeon](https://doutorai.pt/en/components/skills__aeon): This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation,
- [agent-creation-prompt](https://doutorai.pt/en/components/skills__agent-creation-prompt): Use this template to generate agents using Claude with the agent creation system prompt.
- [agent-creation-system-prompt](https://doutorai.pt/en/components/skills__agent-creation-system-prompt): This is the exact system prompt used by Claude Code's agent generation feature, refined through extensive production use.
- [agent-design](https://doutorai.pt/en/components/skills__agent-design): This file covers decision heuristics for building agents on the Claude API: which primitives to reach for, how to design your tool surface, and how to manage co
- [agent-evaluation](https://doutorai.pt/en/components/skills__agent-evaluation): Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring-where even top agents ac
- [agent-management](https://doutorai.pt/en/components/skills__agent-management): Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent", 
- [agent-manager-skill](https://doutorai.pt/en/components/skills__agent-manager-skill): Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
- [agent-md-refactor](https://doutorai.pt/en/components/skills__agent-md-refactor): Refactor bloated AGENTS.md, CLAUDE.md, or similar agent instruction files to follow progressive disclosure principles. Splits monolithic files into organized, l
- [agent-memory-mcp](https://doutorai.pt/en/components/skills__agent-memory-mcp): A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
- [agent-memory-systems](https://doutorai.pt/en/components/skills__agent-memory-systems): Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term 
- [agent-messaging](https://doutorai.pt/en/components/skills__agent-messaging): Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user asks to "send a message to an a
- [agent-tool-builder](https://doutorai.pt/en/components/skills__agent-tool-builder): Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, o
- [agent-types](https://doutorai.pt/en/components/skills__agent-types): Complete definitions and capabilities for all 37 specialized agent types.
- [agentic_system_design](https://doutorai.pt/en/components/skills__agentic-system-design): World-class agentic system design for senior prompt engineer.
- [agents](https://doutorai.pt/en/components/skills__agents-4): Complete guide to building agents with ReAct, tool calling, and streaming.
- [agents](https://doutorai.pt/en/components/skills__agents-5): Building agents with tools and RAG capabilities.
- [agents](https://doutorai.pt/en/components/skills__agents-6): Complete specifications for all 37 specialized agent types in the Loki Mode multi-agent system.
- [aggregate_benchmark](https://doutorai.pt/en/components/skills__aggregate-benchmark): Returns dict keyed by config name (e.g. "with_skill"/"without_skill", or "new_skill"/"old_skill"), each containing a list of run results. """ # Support both lay
- [aggregate_benchmark](https://doutorai.pt/en/components/skills__aggregate-benchmark-2): Returns dict keyed by config name (e.g. "with_skill"/"without_skill", or "new_skill"/"old_skill"), each containing a list of run results. """ # Support both lay
- [aggregate_benchmark](https://doutorai.pt/en/components/skills__aggregate-benchmark-3): Returns dict keyed by config name (e.g. "with_skill"/"without_skill", or "new_skill"/"old_skill"), each containing a list of run results. """ # Support both lay
- [agile-product-owner](https://doutorai.pt/en/components/skills__agile-product-owner): Agile product ownership toolkit for Senior Product Owner including INVEST-compliant user story generation, sprint planning, backlog management, and velocity tra
- [agirails-agent-payments](https://doutorai.pt/en/components/skills__agirails-agent-payments): You are a payments engineer for the AI agent economy. Your job is to onboard agents onto the AGIRAILS network - an open settlement layer where AI agents earn an
- [ai-agents-architect](https://doutorai.pt/en/components/skills__ai-agents-architect): Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build age
- [ai-product](https://doutorai.pt/en/components/skills__ai-product): Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration
- [ai-wrapper-product](https://doutorai.pt/en/components/skills__ai-wrapper-product): Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that
- [ai_agent_workflow](https://doutorai.pt/en/components/skills__ai-agent-workflow): Use search_templates({query: "ai agent"}) to find more!
- [algolia-search](https://doutorai.pt/en/components/skills__algolia-search): Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning Use when: adding search to, algolia, instantse
- [algorithm-comparison](https://doutorai.pt/en/components/skills__algorithm-comparison): Complete guide to RL algorithms in OpenRLHF: PPO, REINFORCE++, GRPO, RLOO, and their variants.
- [algorithmic-art](https://doutorai.pt/en/components/skills__algorithmic-art): Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generati
- [algorithms](https://doutorai.pt/en/components/skills__algorithms): BPE vs Unigram comparison and subword regularization.
- [algorithms](https://doutorai.pt/en/components/skills__algorithms-2): Comprehensive explanation of BPE, WordPiece, and Unigram algorithms.
- [algorithms](https://doutorai.pt/en/components/skills__algorithms-3): This document provides detailed characteristics of all RL algorithms in Stable Baselines3 to help select the right algorithm for specific tasks.
- [algorithms](https://doutorai.pt/en/components/skills__algorithms-4): Qiskit supports a wide range of quantum algorithms for optimization, chemistry, machine learning, and physics simulations.
- [algorithms](https://doutorai.pt/en/components/skills__algorithms-6): Arboreto provides two algorithms for gene regulatory network (GRN) inference, both based on the multiple regression approach.
- [algorithms](https://doutorai.pt/en/components/skills__algorithms-7): Comprehensive reference for optimization algorithms available in pymoo.
- [alignment](https://doutorai.pt/en/components/skills__alignment): Bio.Align provides tools for pairwise sequence alignment using various algorithms, while Bio.AlignIO handles reading and writing multiple sequence alignment fil
- [alignment_files](https://doutorai.pt/en/components/skills__alignment-files): Pysam provides the AlignmentFile class for reading and writing SAM/BAM/CRAM formatted files containing aligned sequence data. BAM/CRAM files support compression
- [alloydb-basics](https://doutorai.pt/en/components/skills__alloydb-basics): Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB MCP tools for automated database operations including AI-powere
- [alphafold-database](https://doutorai.pt/en/components/skills__alphafold-database): Access AlphaFold's 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), 
- [analysis](https://doutorai.pt/en/components/skills__analysis-2): Useful for testing and Monte Carlo simulations.
- [analysis-workflow](https://doutorai.pt/en/components/skills__analysis-workflow): To ensure your template works correctly, you must validate that the target page actually contains the data you want to extract.
- [analysis_modules](https://doutorai.pt/en/components/skills__analysis-modules): This reference documents pymatgen's extensive analysis capabilities for materials characterization, property prediction, and computational analysis.
- [analysis_template](https://doutorai.pt/en/components/skills__analysis-template): This template provides a complete workflow for single-cell RNA-seq analysis using scanpy, from data loading through clustering and cell type annotation.
- [analysis_template](https://doutorai.pt/en/components/skills__analysis-template-2): Complete analysis workflow from raw data to curated units. Copy and customize this template for your analysis.
- [analytical-equipment](https://doutorai.pt/en/components/skills__analytical-equipment): PyLabRobot integrates with analytical equipment including plate readers, scales, and other measurement devices. This allows automated workflows that combine liq
- [analytics-tracking](https://doutorai.pt/en/components/skills__analytics-tracking): When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics
- [analyze_results](https://doutorai.pt/en/components/skills__analyze-results): This script analyzes DiffDock prediction results, extracting confidence scores, ranking predictions, and generating summary reports.
- [analyzer](https://doutorai.pt/en/components/skills__analyzer): Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
- [analyzer](https://doutorai.pt/en/components/skills__analyzer-2): Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
- [analyzer](https://doutorai.pt/en/components/skills__analyzer-3): Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
- [android-cicd](https://doutorai.pt/en/components/skills__android-cicd): Automated Android CI/CD pipeline to Google Play - supports TWA, React Native, Flutter, and native Android. Run npx android-cicd to set up keystore generation, G
- [angular](https://doutorai.pt/en/components/skills__angular): Modern Angular (v20+) expert with deep knowledge of Signals, Standalone Components, Zoneless applications, SSR/Hydration, and reactive patterns.
- [animal_veterinary](https://doutorai.pt/en/components/skills__animal-veterinary): This reference covers FDA animal and veterinary medicine API endpoints accessible through openFDA.
- [animations](https://doutorai.pt/en/components/skills__animations): Animations are transformations applied to mobjects over time. Manim provides a rich set of built-in animations for creating smooth, professional-looking transit
- [animations](https://doutorai.pt/en/components/skills__animations-2): Fundamental animation skills for Remotion
- [animations](https://doutorai.pt/en/components/skills__animations-3): Motion Canvas uses a property-based animation system where you tween individual properties over time using generator functions.
- [animations](https://doutorai.pt/en/components/skills__animations-4): Fundamental animation skills for Remotion
- [anndata](https://doutorai.pt/en/components/skills__anndata): This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurement
- [annotation-validation](https://doutorai.pt/en/components/skills__annotation-validation): This document covers data curation, validation, schema management, and annotation best practices in LaminDB.
- [anomaly_detection](https://doutorai.pt/en/components/skills__anomaly-detection): Aeon provides anomaly detection methods for identifying unusual patterns in time series at both series and collection levels.
- [anthology.bib](https://doutorai.pt/en/components/skills__anthology-bib): For citing papers in the ACL Anthology, we provide a single consolidated BibTeX file containing all of its papers. The bibkeys in these papers are designed to b
- [anthropic-best-practices](https://doutorai.pt/en/components/skills__anthropic-best-practices): Good Skills are concise, well-structured, and tested with real usage. This guide provides practical authoring decisions to help you write Skills that Claude can
- [anthropic-best-practices](https://doutorai.pt/en/components/skills__anthropic-best-practices-2): Good Skills are concise, well-structured, and tested with real usage. This guide provides practical authoring decisions to help you write Skills that Claude can
- [api](https://doutorai.pt/en/components/skills__api): The core class representing a Sparse Autoencoder.
- [api](https://doutorai.pt/en/components/skills__api-2): The core class that wraps PyTorch models for intervention.
- [api](https://doutorai.pt/en/components/skills__api-3): Run a command inside a folder, with Modal Volume reloading before and commit on success.
- [api](https://doutorai.pt/en/components/skills__api-4): Main class for wrapping language models with intervention capabilities.
- [api](https://doutorai.pt/en/components/skills__api-5): The core class for mechanistic interpretability, wrapping transformer models with hooks on every activation.
- [api](https://doutorai.pt/en/components/skills__api-7): See configuration.md for all constructor options.
- [api](https://doutorai.pt/en/components/skills__api-8): Cache Reserve is a zone-level configuration, not a per-request API. It works automatically when enabled for the zone:
- [api](https://doutorai.pt/en/components/skills__api-10): Fire-and-forget (returns void, not Promise). Writes happen asynchronously.
- [api](https://doutorai.pt/en/components/skills__api-13): See patterns.md for Workers examples: mobile app allowlisting, corporate proxy exemption, datacenter detection, conditional delay, and more.
- [api](https://doutorai.pt/en/components/skills__api-14): Query existing Cloudflare resources to reference in your configurations.
- [api](https://doutorai.pt/en/components/skills__api-15): Requires Service API token with "AI Search - Read" permission.
- [api](https://doutorai.pt/en/components/skills__api-16): The Turnstile JavaScript API is available at window.turnstile after loading the script.
- [api](https://doutorai.pt/en/components/skills__api-17): Outbound email API for transactional messages.
- [api](https://doutorai.pt/en/components/skills__api-19): Complete API documentation for Cloudflare TURN service credentials and key management.
- [api](https://doutorai.pt/en/components/skills__api-20): Complete API reference for the Cloudflare Workers TCP Sockets API (cloudflare:sockets).
- [api](https://doutorai.pt/en/components/skills__api-21): SQL syntax, functions, operators, and data types for R2 SQL queries.
- [api](https://doutorai.pt/en/components/skills__api-22): See README.md for overview, configuration.md for setup.
- [api](https://doutorai.pt/en/components/skills__api-23): Complete API reference for Cloudflare Email Workers runtime.
- [api](https://doutorai.pt/en/components/skills__api-26): Cloudflare generates binding types via npx wrangler types. This creates .wrangler/types/runtime.d.ts with your Env interface.
- [api](https://doutorai.pt/en/components/skills__api-30): R2 Data Catalog exposes standard Apache Iceberg REST Catalog API.
- [api](https://doutorai.pt/en/components/skills__api-33): See patterns.md for usage examples, configuration.md for config details.
- [api](https://doutorai.pt/en/components/skills__api-34): Requires metadata index. Filter operators:
- [api](https://doutorai.pt/en/components/skills__api-37): Returns when process starts, NOT when ports ready. Use for fire-and-forget.
- [api](https://doutorai.pt/en/components/skills__api-40): Node.js APIs for testing and development.
- [api](https://doutorai.pt/en/components/skills__api-41): All SDKs support automatic pagination for list operations.
- [api](https://doutorai.pt/en/components/skills__api-42): The ASSETS binding provides access to static assets via the Fetcher interface.
- [api](https://doutorai.pt/en/components/skills__api-47): Client-side JavaScript API for tracking events, setting properties, and managing consent.
- [api](https://doutorai.pt/en/components/skills__api-48): Skip rules bypass subsequent rule evaluation. Two skip types:
- [api](https://doutorai.pt/en/components/skills__api-49): Units: second, minute, hour, day, week, month, year. Max: 365 days. Sleeping instances don't count toward concurrency.
- [api](https://doutorai.pt/en/components/skills__api-50): Token-based tunnels store config in Cloudflare dashboard instead of local files.
- [api](https://doutorai.pt/en/components/skills__api-51): See configuration.md for wrangler.jsonc examples.
- [api](https://doutorai.pt/en/components/skills__api-52): See gotchas.md for detailed error patterns and solutions.
- [api](https://doutorai.pt/en/components/skills__api-53): For AI chat with auto-streaming, message history, tools, resumable streaming.
- [api](https://doutorai.pt/en/components/skills__api-55): Upload, playback, live streaming, and management APIs.
- [api](https://doutorai.pt/en/components/skills__api-57): Each session maintains own shell state, env vars, cwd, process namespace.
- [api](https://doutorai.pt/en/components/skills__api-58): See DO Storage for complete storage API reference.
- [api](https://doutorai.pt/en/components/skills__api-60): Automatically optimizes function execution location based on traffic patterns.
- [api](https://doutorai.pt/en/components/skills__api-61): Complete API reference for Meeting object, REST endpoints, and SDK methods.
- [api](https://doutorai.pt/en/components/skills__api-62): Long-running sessions for operations exceeding 30s timeout (up to 15 min).
- [api](https://doutorai.pt/en/components/skills__api-63): Use networkInterconnects namespace for all new code.
- [api](https://doutorai.pt/en/components/skills__api-64): See patterns.md for complete implementation patterns.
- [api-design-principles](https://doutorai.pt/en/components/skills__api-design-principles): Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers and stand the test of time.
- [api-documentation-generator](https://doutorai.pt/en/components/skills__api-documentation-generator): Generate comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and best practices
- [api-evaluation](https://doutorai.pt/en/components/skills__api-evaluation): Guide to evaluating OpenAI, Anthropic, and other API-based language models.
- [api-live](https://doutorai.pt/en/components/skills__api-live): Live input creation, status checking, simulcast, and WebRTC streaming.
- [api-patterns](https://doutorai.pt/en/components/skills__api-patterns): API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
- [api-reference](https://doutorai.pt/en/components/skills__api-reference-2): miles uses the same Sample dataclass as slime with the rollout_routed_experts field for MoE routing replay.
- [api-reference](https://doutorai.pt/en/components/skills__api-reference-3): Base class for Forge actors with configurable resource attributes.
- [api-reference](https://doutorai.pt/en/components/skills__api-reference-5): The central controller for the training loop. Manages resource allocation and coordinates worker groups.
- [api-security-best-practices](https://doutorai.pt/en/components/skills__api-security-best-practices): Implement secure API design patterns including authentication, authorization, input validation, rate limiting, and protection against common API vulnerabilities
- [api-security-testing](https://doutorai.pt/en/components/skills__api-security-testing): API security testing workflow for REST and GraphQL APIs covering authentication, authorization, rate limiting, input validation, and security best practices.
- [api_basics](https://doutorai.pt/en/components/skills__api-basics): This reference provides comprehensive information about using the openFDA API, including authentication, rate limits, query syntax, and best practices.
- [api_design_patterns](https://doutorai.pt/en/components/skills__api-design-patterns): This reference guide provides comprehensive information for senior backend.
- [api_endpoints](https://doutorai.pt/en/components/skills__api-endpoints): Comprehensive documentation of all 17 API endpoint categories available in the Ensembl REST API (Release 115, September 2025).
- [api_endpoints](https://doutorai.pt/en/components/skills__api-endpoints-2): Replace {tenant} with your Benchling tenant name.
- [api_examples](https://doutorai.pt/en/components/skills__api-examples): Practical code examples for interacting with the UniProt REST API in multiple languages.
- [api_fields](https://doutorai.pt/en/components/skills__api-fields): Complete list of available fields for customizing UniProt API queries. Use these fields with the fields parameter to retrieve only the data you need.
- [api_guide](https://doutorai.pt/en/components/skills__api-guide): Comprehensive reference for all medchem modules and functions.
- [api_operations](https://doutorai.pt/en/components/skills__api-operations): Guide for operating CocoIndex flows programmatically using Python APIs.
- [api_quick_reference](https://doutorai.pt/en/components/skills__api-quick-reference): This document provides quick reference for common COBRApy functions, signatures, and usage patterns.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-6): Complete API documentation for all NotebookLM skill modules.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-7): This is a placeholder for detailed reference documentation. Replace with actual reference content or delete if not needed.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-8): This is a placeholder for detailed reference documentation. Replace with actual reference content or delete if not needed.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-9): This is a placeholder for detailed reference documentation. Replace with actual reference content or delete if not needed.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-10): This is a placeholder for detailed reference documentation. Replace with actual reference content or delete if not needed.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-11): This is a placeholder for detailed reference documentation. Replace with actual reference content or delete if not needed.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-12): This is a placeholder for detailed reference documentation. Replace with actual reference content or delete if not needed.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-13): This is a placeholder for detailed reference documentation. Replace with actual reference content or delete if not needed.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-14): This is a placeholder for detailed reference documentation. Replace with actual reference content or delete if not needed.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-15): This is a placeholder for detailed reference documentation. Replace with actual reference content or delete if not needed.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-16): This is a placeholder for detailed reference documentation. Replace with actual reference content or delete if not needed.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-17): This is a placeholder for detailed reference documentation. Replace with actual reference content or delete if not needed.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-18): The ClinicalTrials.gov API v2 is a modern REST API that provides programmatic access to the ClinicalTrials.gov database, which contains information about clinic
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-19): This is a placeholder for detailed reference documentation. Replace with actual reference content or delete if not needed.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-20): Retrieve metabolite structure and identification data.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-21): Comprehensive reference for the GWAS Catalog REST APIs, including endpoint specifications, query parameters, response formats, and advanced usage patterns.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-22): This document provides a comprehensive reference for DeepChem's core APIs, organized by functionality.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-23): This document provides detailed information about the RCSB Protein Data Bank APIs, including advanced usage patterns, data schemas, and best practices.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-24): To obtain API access: 1. Contact support@adaptyvbio.com 2. Request API access during alpha/beta period 3. Receive your personal access token
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-25): ChEMBL is a manually curated database of bioactive molecules with drug-like properties maintained by the European Bioinformatics Institute (EBI). It contains in
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-26): This reference provides a concise overview of commonly used Zarr functions, parameters, and patterns for quick lookup during development.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-27): The bioRxiv API provides programmatic access to preprint metadata from the bioRxiv server. The API returns JSON-formatted data with comprehensive metadata about
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-28): Always use the latest stable API version for new protocols.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-29): Comprehensive API documentation for the biomni framework.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-30): PubChem is the world's largest freely available chemical database maintained by the National Center for Biotechnology Information (NCBI). It contains over 110 m
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-32): Complete reference documentation for the ClinPGx REST API.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-33): Comprehensive reference for the European Nucleotide Archive REST APIs.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-34): Complete technical reference for programmatic access to the ZINC database, covering API endpoints, query syntax, parameters, response formats, and advanced usag
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-35): FlowIO is a Python library for reading and writing Flow Cytometry Standard (FCS) files. It supports FCS versions 2.0, 3.0, and 3.1 with minimal dependencies.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-36): This document provides comprehensive reference information for Reactome's REST APIs.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-37): This document provides detailed API information, advanced examples, and troubleshooting guidance for working with scikit-bio.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-38): ETE (Environment for Tree Exploration) is a Python toolkit for phylogenetic tree manipulation, analysis, and visualization. This reference covers the main class
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-39): Retrieve user ID and notebook access information.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-40): This document provides comprehensive API reference for PyDESeq2 classes, methods, and utilities.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-41): This document provides comprehensive technical documentation for programmatic access to the AlphaFold Protein Structure Database.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-42): Quick reference for commonly used scanpy functions organized by module.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-43): This document provides detailed API documentation for accessing NCBI Gene database programmatically.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-44): This document provides detailed reference information for the BRENDA (BRaunschweig ENzyme DAtabase) SOAP API and the Python client implementation. BRENDA is the
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-45): Find low-dimensional embedding that approximates the underlying manifold of the data.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-46): This document provides a quick reference for the most commonly used matplotlib classes and methods.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-47): The NCBI E-utilities provide programmatic access to PubMed and other Entrez databases through a REST API. The base URL for all E-utilities is:
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-48): No authentication is required for the GraphQL API. All data is freely accessible.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-49): This document provides a comprehensive reference for RDKit's Python API, organized by functionality.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-50): Calculators are callable objects that convert individual molecules into feature vectors. They accept either RDKit Chem.Mol objects or SMILES strings as input.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-51): Quick reference for neuropixels_analysis functions organized by module.
- [api_reference](https://doutorai.pt/en/components/skills__api-reference-52): The main class for converting files to Markdown.
- [api_types](https://doutorai.pt/en/components/skills__api-types): The root object for incoming updates. Contains update_id and exactly ONE of:
- [app-builder](https://doutorai.pt/en/components/skills__app-builder): Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates
- [app-development](https://doutorai.pt/en/components/skills__app-development): Guide for building Shopify apps with OAuth, GraphQL/REST APIs, webhooks, and billing.
- [app-development](https://doutorai.pt/en/components/skills__app-development-2): Apps and applets are executable programs that run on the DNAnexus platform. They can be written in Python or Bash and are deployed with all necessary dependenci
- [app-store-optimization](https://doutorai.pt/en/components/skills__app-store-optimization): Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store
- [application-security](https://doutorai.pt/en/components/skills__application-security): This comprehensive application security guide provides developers with the knowledge and code examples needed to build secure applications following industry be
- [applications](https://doutorai.pt/en/components/skills__applications): Practical applications and use cases for CLIP.
- [apply-guide](https://doutorai.pt/en/components/skills__apply-guide): How to apply extracted design tokens to different frontend stacks.
- [approachable-concurrency](https://doutorai.pt/en/components/skills__approachable-concurrency): Use this reference when the project has opted into the Swift 6.2 approachable concurrency settings (default actor isolation / main-actor-by-default).
- [arboreto](https://doutorai.pt/en/components/skills__arboreto): Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA
- [architecture](https://doutorai.pt/en/components/skills__architecture): NanoGPT implements a clean GPT-2 architecture in minimal code for educational purposes.
- [architecture](https://doutorai.pt/en/components/skills__architecture-2): Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing sys
- [architecture-decision-records](https://doutorai.pt/en/components/skills__architecture-decision-records): Comprehensive patterns for creating, maintaining, and managing Architecture Decision Records (ADRs) that capture the context and rationale behind significant te
- [architecture-details](https://doutorai.pt/en/components/skills__architecture-details): Mamba's core innovation is the Selective SSM (S6) layer that makes state space model parameters input-dependent.
- [architecture-details](https://doutorai.pt/en/components/skills__architecture-details-2): RWKV alternates between Time-Mixing (sequence processing) and Channel-Mixing (feature processing) blocks.
- [architecture-overview](https://doutorai.pt/en/components/skills__architecture-overview): Complete guide to the layered architecture pattern used in backend microservices.
- [architecture-patterns](https://doutorai.pt/en/components/skills__architecture-patterns): Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and 
- [architecture_decision_records](https://doutorai.pt/en/components/skills__architecture-decision-records-2): Architecture Decision Records capture important architectural decisions made along with their context and consequences. They help maintain institutional knowled
- [architecture_patterns](https://doutorai.pt/en/components/skills__architecture-patterns-2): This reference guide provides comprehensive information for senior architect.
- [architecture_patterns](https://doutorai.pt/en/components/skills__architecture-patterns-3): This reference guide provides comprehensive information for senior fullstack.
- [architectures](https://doutorai.pt/en/components/skills__architectures): Comprehensive guide to different Mixture of Experts architectures and their design patterns.
- [arctic-frost](https://doutorai.pt/en/components/skills__arctic-frost): A cool and crisp winter-inspired theme that conveys clarity, precision, and professionalism.
- [arrays](https://doutorai.pt/en/components/skills__arrays): Dask Array implements NumPy's ndarray interface using blocked algorithms. It coordinates many NumPy arrays arranged into a grid to enable computation on dataset
- [art-of-readme](https://doutorai.pt/en/components/skills__art-of-readme): The nomenclature dates back to at least the 1970s and the PDP-10, though it may even harken back to the days of informative paper notes placed atop stacks of pu
- [artifacts](https://doutorai.pt/en/components/skills__artifacts): Complete guide to data versioning and model management with W&B Artifacts.
- [artifacts](https://doutorai.pt/en/components/skills__artifacts-2): SwarmVault is save-first. The files on disk are the product.
- [artifacts-builder](https://doutorai.pt/en/components/skills__artifacts-builder): Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for
- [ask_question](https://doutorai.pt/en/components/skills__ask-question): if not auth.is_authenticated(): print("⚠️ Not authenticated. Run: python auth_manager.py setup") return None
- [assertions](https://doutorai.pt/en/components/skills__assertions): Playwright's assertThat(locator) polls automatically (up to defaultTimeout). Always prefer these over locator.isVisible() + assertTrue.
- [assets](https://doutorai.pt/en/components/skills__assets): Importing images, videos, audio, and fonts into Remotion
- [assets](https://doutorai.pt/en/components/skills__assets-3): Importing images, videos, audio, and fonts into Remotion
- [assets](https://doutorai.pt/en/components/skills__assets-4): Uploading images, videos, and audio for use in HeyGen video generation
- [assumptions_and_diagnostics](https://doutorai.pt/en/components/skills__assumptions-and-diagnostics): This document provides comprehensive guidance on checking and validating statistical assumptions for various analyses.
- [astro](https://doutorai.pt/en/components/skills__astro): Build content-focused websites with Astro - zero JS by default, islands architecture, multi-framework components, and Markdown/MDX support.
- [astro-static](https://doutorai.pt/en/components/skills__astro-static): Astro static site template principles. Content-focused websites, blogs, documentation.
- [astropy](https://doutorai.pt/en/components/skills__astropy): Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, phys
- [astropy__astropy-12907](https://doutorai.pt/en/components/skills__astropy-astropy-12907): Based on my knowledge of the astropy codebase and the issue described, I can generate the patch. The bug is in the _cstack function in astropy/modeling/separabl
- [astropy__astropy-12907](https://doutorai.pt/en/components/skills__astropy-astropy-12907-2): Based on my knowledge of the astropy codebase and this bug, I can provide the fix. The issue is in the _cstack function in astropy/modeling/separable.py. When h
- [astropy__astropy-12907](https://doutorai.pt/en/components/skills__astropy-astropy-12907-3): Based on the architect's analysis, I need to generate a patch for the astropy repository's separable.py file. The issue is in the _cstack function where it uses
- [astropy__astropy-14182](https://doutorai.pt/en/components/skills__astropy-astropy-14182): Based on my knowledge of the astropy codebase and the error message in the issue, I can provide the patch. The issue is that the RST class (which outputs reStru
- [astropy__astropy-14182](https://doutorai.pt/en/components/skills__astropy-astropy-14182-2): Based on my knowledge of the astropy codebase and the issue description, I can create the patch. The issue is that the RST (RestructuredText) writer doesn't sup
- [astropy__astropy-14365](https://doutorai.pt/en/components/skills__astropy-astropy-14365): Based on the GitHub issue description and the attached patch mentioned, I can deduce what the fix needs to be. The issue states:
- [astropy__astropy-14365](https://doutorai.pt/en/components/skills__astropy-astropy-14365-2): Based on the issue description and the hint about a patch, the problem is clear: the regex patterns for QDP commands are not case insensitive. The issue mention
- [astropy__astropy-14365](https://doutorai.pt/en/components/skills__astropy-astropy-14365-3): Based on the issue description and the architect's analysis, I need to generate a patch that makes the QDP command parsing case-insensitive. The issue is that r
- [astropy__astropy-14995](https://doutorai.pt/en/components/skills__astropy-astropy-14995): Based on my analysis of the issue, I now have enough information to provide the correct patch. The bug is in astropy/nddata/mixins/ndarithmetic.py where the con
- [astropy__astropy-14995](https://doutorai.pt/en/components/skills__astropy-astropy-14995-2): Based on my analysis, I have the correct fix for the issue. The bug is in astropy/nddata/mixins/ndarithmetic.py where the condition elif operand is None: should
- [astropy__astropy-14995](https://doutorai.pt/en/components/skills__astropy-astropy-14995-3): The issue is for astropy/astropy repository, but the current working directory is a different project. Based on the architect's analysis, I need to generate a p
- [astropy__astropy-6938](https://doutorai.pt/en/components/skills__astropy-astropy-6938): This is a benchmark result file that already contains the expected patch. Let me verify this is the correct fix by understanding the issue better.
- [astropy__astropy-6938](https://doutorai.pt/en/components/skills__astropy-astropy-6938-2): The fix is consistent across patches. The issue is that replace on a numpy chararray returns a copy and doesn't modify in-place, so the result needs to be assig
- [astropy__astropy-7746](https://doutorai.pt/en/components/skills__astropy-astropy-7746): Based on the error traceback provided in the issue, I can understand the code structure. Let me analyze the problem:
- [astropy__astropy-7746](https://doutorai.pt/en/components/skills__astropy-astropy-7746-2): Now I have the information needed to generate a proper patch. Looking at both patch files, I can see the approaches are slightly different. The first one (1-25-
- [astropy__astropy-7746](https://doutorai.pt/en/components/skills__astropy-astropy-7746-3): Based on the issue description and the architect's analysis, I have enough context to generate the patch. The issue is in the _array_converter method in astropy
- [async-and-errors](https://doutorai.pt/en/components/skills__async-and-errors): Complete guide to async/await patterns and custom error handling.
- [async-api-routes](https://doutorai.pt/en/components/skills__async-api-routes): In API routes and Server Actions, start independent operations immediately, even if you don't await them yet.
- [async-api-routes](https://doutorai.pt/en/components/skills__async-api-routes-2): In API routes and Server Actions, start independent operations immediately, even if you don't await them yet.
- [async-defer-await](https://doutorai.pt/en/components/skills__async-defer-await): Move await operations into the branches where they're actually used to avoid blocking code paths that don't need them.
- [async-defer-await](https://doutorai.pt/en/components/skills__async-defer-await-2): Move await operations into the branches where they're actually used to avoid blocking code paths that don't need them.
- [async-dependencies](https://doutorai.pt/en/components/skills__async-dependencies): For operations with partial dependencies, use better-all to maximize parallelism. It automatically starts each task at the earliest possible moment.
- [async-dependencies](https://doutorai.pt/en/components/skills__async-dependencies-2): For operations with partial dependencies, use better-all to maximize parallelism. It automatically starts each task at the earliest possible moment.
- [async-parallel](https://doutorai.pt/en/components/skills__async-parallel): When async operations have no interdependencies, execute them concurrently using Promise.all().
- [async-parallel](https://doutorai.pt/en/components/skills__async-parallel-2): When async operations have no interdependencies, execute them concurrently using Promise.all().
- [async-python-patterns](https://doutorai.pt/en/components/skills__async-python-patterns): Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-perfo
- [async-suspense-boundaries](https://doutorai.pt/en/components/skills__async-suspense-boundaries): Instead of awaiting data in async components before returning JSX, use Suspense boundaries to show the wrapper UI faster while data loads.
- [async-suspense-boundaries](https://doutorai.pt/en/components/skills__async-suspense-boundaries-2): Instead of awaiting data in async components before returning JSX, use Suspense boundaries to show the wrapper UI faster while data loads.
- [audio](https://doutorai.pt/en/components/skills__audio): Using audio and sound in Remotion - importing, trimming, volume, speed, pitch
- [audio](https://doutorai.pt/en/components/skills__audio-2): Using audio and sound in Remotion - importing, trimming, volume, speed, pitch
- [audiocraft-audio-generation](https://doutorai.pt/en/components/skills__audiocraft-audio-generation): PyTorch library for audio generation including text-to-music (MusicGen) and text-to-sound (AudioGen). Use when you need to generate music from text descriptions
- [audit](https://doutorai.pt/en/components/skills__audit): / Roier SEO - Lighthouse Audit Script Runs a Lighthouse audit on a given URL and outputs structured results. Usage: node audit.js [--output=json|summary] [--sav
- [audit-api](https://doutorai.pt/en/components/skills__audit-api): / Roier SEO - PageSpeed Insights API Audit Uses Google's PageSpeed Insights API (no local Chrome needed). Works for any publicly accessible URL. Usage: node aud
- [auth](https://doutorai.pt/en/components/skills__auth-2): Authenticate with Cloudflare before deploying Workers or Pages.
- [auth-methods](https://doutorai.pt/en/components/skills__auth-methods): Complete reference for authentication methods, session management, and error handling.
- [authentication](https://doutorai.pt/en/components/skills__authentication-2): Benchling supports three authentication methods, each suited for different use cases.
- [authentication](https://doutorai.pt/en/components/skills__authentication-3): The protocols.io API supports two types of access tokens for authentication, enabling access to both public and private content.
- [authentication](https://doutorai.pt/en/components/skills__authentication-4): Complete guide to authentication methods for MCP servers in Claude Code plugins.
- [authentication](https://doutorai.pt/en/components/skills__authentication-5): API key setup, X-Api-Key header, and authentication patterns for HeyGen
- [authentication_guide](https://doutorai.pt/en/components/skills__authentication-guide): API access requires an Enterprise LabArchives license. Contact your LabArchives administrator or sales@labarchives.com to: - Verify your institution has Enterpr
- [authorization](https://doutorai.pt/en/components/skills__authorization): Authorization controls what authenticated users can do with your FastMCP server. While authentication verifies identity (who you are), authorization determines 
- [autogpt-agents](https://doutorai.pt/en/components/skills__autogpt-agents): Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or 
- [automation](https://doutorai.pt/en/components/skills__automation): Comprehensive guide to IT automation, scripting, configuration management, orchestration tools, and reducing operational toil.
- [autonomous-agent-patterns](https://doutorai.pt/en/components/skills__autonomous-agent-patterns): Design patterns for building autonomous coding agents. Covers tool integration, permission systems, browser automation, and human-in-the-loop workflows. Use whe
- [autonomous-agents](https://doutorai.pt/en/components/skills__autonomous-agents): Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The chal
- [available_featurizers](https://doutorai.pt/en/components/skills__available-featurizers): This document provides a comprehensive catalog of all featurizers available in molfeat, organized by category.
- [avalonia-layout-zafiro](https://doutorai.pt/en/components/skills__avalonia-layout-zafiro): Guidelines for modern Avalonia UI layout using Zafiro.Avalonia, emphasizing shared styles, generic components, and avoiding XAML redundancy.
- [avalonia-viewmodels-zafiro](https://doutorai.pt/en/components/skills__avalonia-viewmodels-zafiro): Optimal ViewModel and Wizard creation patterns for Avalonia using Zafiro and ReactiveUI.
- [avalonia-zafiro-development](https://doutorai.pt/en/components/skills__avalonia-zafiro-development): Mandatory skills, conventions, and behavioral rules for Avalonia UI development using the Zafiro toolkit.
- [avatars](https://doutorai.pt/en/components/skills__avatars): Listing avatars, avatar styles, and avatar_id selection for HeyGen
- [awq-quantization](https://doutorai.pt/en/components/skills__awq-quantization): Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GP
- [aws-serverless](https://doutorai.pt/en/components/skills__aws-serverless): Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, S
- [axolotl](https://doutorai.pt/en/components/skills__axolotl): Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support
- [azure-functions](https://doutorai.pt/en/components/skills__azure-functions): Expert patterns for Azure Functions development including isolated worker model, Durable Functions orchestration, cold start optimization, and production patter
- [backend-architect](https://doutorai.pt/en/components/skills__backend-architect): Expert backend architect specializing in scalable API design, microservices architecture, and distributed systems.
- [backend-dev-guidelines](https://doutorai.pt/en/components/skills__backend-dev-guidelines): Comprehensive backend development guide for Node.js/Express/TypeScript microservices. Use when creating routes, controllers, services, repositories, middleware,
- [backend-patterns](https://doutorai.pt/en/components/skills__backend-patterns): Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.
- [backend-to-frontend-handoff-docs](https://doutorai.pt/en/components/skills__backend-to-frontend-handoff-docs): Create API handoff documentation for frontend developers. Use when backend work is complete and needs to be documented for frontend integration, or user says 'c
- [backend_security_practices](https://doutorai.pt/en/components/skills__backend-security-practices): This reference guide provides comprehensive information for senior backend.
- [backends](https://doutorai.pt/en/components/skills__backends): Complete guide to configuring Outlines with different model backends.
- [backends](https://doutorai.pt/en/components/skills__backends-2): Complete guide to configuring Guidance with different LLM backends.
- [backends](https://doutorai.pt/en/components/skills__backends-3): Qiskit is backend-agnostic and supports execution on simulators and real quantum hardware from multiple providers.
- [background-tasks](https://doutorai.pt/en/components/skills__background-tasks): FastMCP implements the MCP background task protocol (SEP-1686), giving your servers a production-ready distributed task scheduler with a single decorator change
- [backgrounds](https://doutorai.pt/en/components/skills__backgrounds): Solid colors, images, and video backgrounds for HeyGen videos
- [backup-recovery](https://doutorai.pt/en/components/skills__backup-recovery): Comprehensive guide to backup strategies, disaster recovery planning, business continuity, and data protection for IT operations.
- [bags](https://doutorai.pt/en/components/skills__bags): Dask Bag implements functional operations including map, filter, fold, and groupby on generic Python objects. It processes data in parallel while maintaining a 
- [baposter_template](https://doutorai.pt/en/components/skills__baposter-template): \headerbox{Introduction}{name=intro,column=0,row=0}{ extbf{Background} Brief context establishing the importance of your research area (1-2 sentences). \vspace{
- [base-vs-radix](https://doutorai.pt/en/components/skills__base-vs-radix): API differences between base and radix. Check the base field from npx shadcn@latest info.
- [bases-workflow](https://doutorai.pt/en/components/skills__bases-workflow): The user maintains "Bases" in Templates/Bases/.base which define the schema and properties for different types of notes (e.g., Recipes, Clippings, People).
- [bash-linux](https://doutorai.pt/en/components/skills__bash-linux): Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.
- [bash-pro](https://doutorai.pt/en/components/skills__bash-pro): Master of defensive Bash scripting for production automation, CI/CD
- [basic_grn_inference](https://doutorai.pt/en/components/skills__basic-grn-inference): This script demonstrates the standard workflow for inferring gene regulatory networks from expression data using GRNBoost2.
- [basic_grpo_training](https://doutorai.pt/en/components/skills__basic-grpo-training): A minimal, production-ready template for GRPO training with TRL. Adapt this for your specific task by modifying: 1. Dataset loading (get_dataset function) 2. Re
- [basic_inference](https://doutorai.pt/en/components/skills__basic-inference): If not provided, all genes are considered potential regulators.
- [basic_protocol_template](https://doutorai.pt/en/components/skills__basic-protocol-template): This template provides a minimal starting point for creating Opentrons protocols. Replace the placeholder values and add your specific protocol logic.
- [basic_simulation_template](https://doutorai.pt/en/components/skills__basic-simulation-template): This template provides a starting point for building SimPy simulations. Customize the process functions and parameters for your specific use case.
- [batch_convert](https://doutorai.pt/en/components/skills__batch-convert): This script demonstrates how to efficiently convert multiple files in a directory to Markdown format.
- [batch_gene_lookup](https://doutorai.pt/en/components/skills__batch-gene-lookup): This script efficiently processes multiple gene queries with proper rate limiting and error handling.
- [batch_id_converter](https://doutorai.pt/en/components/skills__batch-id-converter): This script converts multiple identifiers between biological databases using UniProt's mapping service. Supports batch processing with automatic chunking and er
- [batch_sequence_analysis](https://doutorai.pt/en/components/skills__batch-sequence-analysis): for i, seq_data in enumerate(sequences): print(f" {i+1}. Predicting structure for {seq_data['id']}...") try: structure_dir = output_path / f"structure_{seq_data
- [batches](https://doutorai.pt/en/components/skills__batches): The Batches API (POST /v1/messages/batches) processes Messages API requests asynchronously at 50% of standard prices.
- [batches](https://doutorai.pt/en/components/skills__batches-2): The Batches API (POST /v1/messages/batches) processes Messages API requests asynchronously at 50% of standard prices.
- [bayesian_statistics](https://doutorai.pt/en/components/skills__bayesian-statistics): This document provides guidance on conducting and interpreting Bayesian statistical analyses, which offer an alternative framework to frequentist (classical) st
- [beamer_guide](https://doutorai.pt/en/components/skills__beamer-guide): Beamer is a LaTeX document class for creating presentations with professional, consistent formatting. It's particularly well-suited for scientific presentations
- [beamer_template_defense](https://doutorai.pt/en/components/skills__beamer-template-defense): % Title page information itle[Dissertation Defense]{Title of Your Dissertation:\\Comprehensive and Descriptive} \subtitle{Dissertation Defense} \author[Your Nam
- [beamer_template_seminar](https://doutorai.pt/en/components/skills__beamer-template-seminar): % Title information itle[Short Title for Footer]{Full Title of Your Research:\\Comprehensive and Descriptive} \subtitle{Research Seminar Presentation} \author[Y
- [beamerposter_academic](https://doutorai.pt/en/components/skills__beamerposter-academic): % Introduction/Background \begin{block}{Introduction} \begin{itemize} \item extbf{Background:} Provide context for your research. What is the broader problem or
- [bedspace](https://doutorai.pt/en/components/skills__bedspace): BEDspace applies the StarSpace model to genomic data, enabling simultaneous training of numerical embeddings for both region sets and their metadata labels in a
- [behavioral-modes](https://doutorai.pt/en/components/skills__behavioral-modes): AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
- [behaviors](https://doutorai.pt/en/components/skills__behaviors): To keep XAML clean and maintainable, minimize logic in views and avoid excessive use of converters.
- [benchling-integration](https://doutorai.pt/en/components/skills__benchling-integration): Benchling R&D platform integration. Access registry (DNA, proteins), inventory, ELN entries, workflows via API, build Benchling Apps, query Data Warehouse, for 
- [benchmark-guide](https://doutorai.pt/en/components/skills__benchmark-guide): Complete guide to all 60+ evaluation tasks in lm-evaluation-harness, what they measure, and how to interpret results.
- [benchmark_evaluation](https://doutorai.pt/en/components/skills__benchmark-evaluation): This script demonstrates how to use TDC benchmark groups for systematic model evaluation following the required 5-seed protocol.
- [benchmark_model](https://doutorai.pt/en/components/skills__benchmark-model): This script provides a simple way to benchmark different GNN architectures on common datasets and compare their performance.
- [benchmarks](https://doutorai.pt/en/components/skills__benchmarks): Comprehensive guide to all benchmarks supported by BigCode Evaluation Harness.
- [benchmarks](https://doutorai.pt/en/components/skills__benchmarks-4): Performance metrics and benchmarks for Megatron-Core across different model sizes and hardware configurations.
- [best-practices](https://doutorai.pt/en/components/skills__best-practices): These alternatives often provide better returns than distributed systems and should be exhausted before scaling to parallel computing.
- [best-practices](https://doutorai.pt/en/components/skills__best-practices-2): Guidelines for creating "cool" high-quality slides.
- [best-practices](https://doutorai.pt/en/components/skills__best-practices-3): Apply modern web development best practices for security, compatibility, and code quality. Use when asked to "apply best practices", "security audit", "moderniz
- [best-practices](https://doutorai.pt/en/components/skills__best-practices-4): This document provides quality guidelines, writing style recommendations, common pitfalls, and a detailed template structure for creating effective slash comman
- [best_practices](https://doutorai.pt/en/components/skills__best-practices-5): This guide provides publication standards, accessibility guidelines, and best practices for creating high-quality scientific diagrams that meet journal requirem
- [best_practices](https://doutorai.pt/en/components/skills__best-practices-7): Comprehensive guide to writing efficient Polars code and avoiding common pitfalls.
- [best_practices](https://doutorai.pt/en/components/skills__best-practices-8): Guidelines for efficient and effective use of AnnData.
- [bibtex_formatting](https://doutorai.pt/en/components/skills__bibtex-formatting): Comprehensive guide to BibTeX entry types, required fields, formatting conventions, and best practices.
- [bibtex_template](https://doutorai.pt/en/components/skills__bibtex-template): @article{Watson1953, author = {Watson, James D. and Crick, Francis H. C.}, title = {Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic Ac
- [bigquery-basics](https://doutorai.pt/en/components/skills__bigquery-basics): Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use for SQL queri
- [bio_module](https://doutorai.pt/en/components/skills__bio-module): The Bio module provides unified functions for processing and analyzing multiple physiological signals simultaneously. It acts as a wrapper that coordinates sign
- [bioactivity_query](https://doutorai.pt/en/components/skills__bioactivity-query): This script provides functions for retrieving biological activity data from PubChem for compounds and assays.
- [bioinformatics_genomics_formats](https://doutorai.pt/en/components/skills__bioinformatics-genomics-formats): This reference covers file formats used in genomics, transcriptomics, sequence analysis, and related bioinformatics applications.
- [biomarker_classification](https://doutorai.pt/en/components/skills__biomarker-classification): Biomarkers are measurable indicators of biological state or condition. In clinical decision support, biomarkers guide diagnosis, prognosis, treatment selection,
- [biomarker_report_template](https://doutorai.pt/en/components/skills__biomarker-report-template): extit{Note: Trial availability changes frequently. Search ClinicalTrials.gov for current options.}
- [biomni](https://doutorai.pt/en/components/skills__biomni): Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use thi
- [biopython](https://doutorai.pt/en/components/skills__biopython): Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, 
- [biorxiv-database](https://doutorai.pt/en/components/skills__biorxiv-database): Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or cate
- [biorxiv_search](https://doutorai.pt/en/components/skills__biorxiv-search): Note: This tool is focused exclusively on bioRxiv (life sciences preprints).
- [bioservices](https://doutorai.pt/en/components/skills__bioservices): Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for 
- [blast](https://doutorai.pt/en/components/skills__blast): Bio.Blast provides tools for running BLAST searches (both locally and via NCBI web services) and parsing BLAST results in various formats. The module handles th
- [bleu](https://doutorai.pt/en/components/skills__bleu): Use this skill whenever a developer wants to turn an idea into a complete, production-ready, end-to-end system plan BEFORE writing any code. Trigger on 'plan th
- [blip-2-vision-language](https://doutorai.pt/en/components/skills__blip-2-vision-language): Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrie
- [blockrun](https://doutorai.pt/en/components/skills__blockrun): Use when user needs capabilities Claude lacks (image generation, real-time X/Twitter data) or explicitly requests external models ("blockrun", "use grok", "use 
- [blog-image-style-guide](https://doutorai.pt/en/components/skills__blog-image-style-guide): Used when generating hero images (via Nano Banana Pro / Gemini 3 Pro Image, or an equivalent text-to-image model) for blog articles.
- [blog-technical-requirements](https://doutorai.pt/en/components/skills__blog-technical-requirements): Universal technical specification for adding a blog to a Next.js + Sanity website. Opinionated, concise, decision-ready. Suitable for a corporate blog with orga
- [blueprint-spec](https://doutorai.pt/en/components/skills__blueprint-spec): Complete reference for render.yaml Blueprint files. Blueprints define your infrastructure as code for reproducible deployments on Render.
- [botanical-garden](https://doutorai.pt/en/components/skills__botanical-garden): A fresh and organic theme featuring vibrant garden-inspired colors for lively presentations.
- [bounding_box](https://doutorai.pt/en/components/skills__bounding-box): Detect and localize objects within images or videos using bounding boxes. The model returns coordinates in the format [y_min, x_min, y_max, x_max], normalized f
- [brainstorming](https://doutorai.pt/en/components/skills__brainstorming): You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirem
- [brainstorming](https://doutorai.pt/en/components/skills__brainstorming-2): You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirem
- [brainstorming_methods](https://doutorai.pt/en/components/skills__brainstorming-methods): This reference document provides detailed descriptions of structured brainstorming frameworks that can be applied to scientific ideation. Consult these when sta
- [branches](https://doutorai.pt/en/components/skills__branches): This document outlines the rules for managing branches in a Neon project using the Neon API.
- [brand](https://doutorai.pt/en/components/skills__brand): Your brand voice should be consistent across all channels. Use this framework to maintain consistency:
- [brand-guidelines](https://doutorai.pt/en/components/skills__brand-guidelines): Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand color
- [brand-guidelines](https://doutorai.pt/en/components/skills__brand-guidelines-2): Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand color
- [brand-guidelines](https://doutorai.pt/en/components/skills__brand-guidelines-3): Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand color
- [brenda-database](https://doutorai.pt/en/components/skills__brenda-database): Access BRENDA enzyme database via SOAP API. Retrieve kinetic parameters (Km, kcat), reaction equations, organism data, and substrate-specific enzyme information
- [brenda_queries](https://doutorai.pt/en/components/skills__brenda-queries): This module provides high-level functions for querying and analyzing enzyme data from the BRENDA database using the SOAP API.
- [brenda_visualization](https://doutorai.pt/en/components/skills__brenda-visualization): This module provides visualization functions for BRENDA enzyme data, including kinetic parameters, environmental conditions, and pathway analysis.
- [bright-data-best-practices](https://doutorai.pt/en/components/skills__bright-data-best-practices): Build production-ready Bright Data integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Curso
- [brightdata-local-search](https://doutorai.pt/en/components/skills__brightdata-local-search): Set up and run local web searches using Bright Data SERP API with the unfancy-search pipeline (query expansion, SERP retrieval, RRF reranking).
- [broader_impacts](https://doutorai.pt/en/components/skills__broader-impacts): NSF evaluates broader impacts across five main areas. You don't need to address all five, but you should address at least one substantively with concrete activi
- [browser-api](https://doutorai.pt/en/components/skills__browser-api): Bright Data Browser API (also called Scraping Browser) is a managed cloud browser service. It handles proxy management, fingerprinting, CAPTCHA solving, and bot
- [browser-automation](https://doutorai.pt/en/components/skills__browser-automation): Browser automation powers web testing, scraping, and AI agent interactions. The difference between a flaky script and a reliable system comes down to understand
- [browser-extension-builder](https://doutorai.pt/en/components/skills__browser-extension-builder): Expert in building browser extensions that solve real problems - Chrome, Firefox, and cross-browser extensions. Covers extension architecture, manifest v3, cont
- [browser_session](https://doutorai.pt/en/components/skills__browser-session): Each session gets its own Page (tab) within a shared BrowserContext, allowing for contextual conversations where NotebookLM remembers previous messages.
- [browser_utils](https://doutorai.pt/en/components/skills__browser-utils): @staticmethod def launch_persistent_context( playwright: Playwright, headless: bool = True, user_data_dir: str = str(BROWSER_PROFILE_DIR) ) -> BrowserContext: "
- [budget_justification_template](https://doutorai.pt/en/components/skills__budget-justification-template): A budget justification provides detailed explanation for each budget line item, demonstrating that costs are necessary, reasonable, and directly related to the 
- [bug_report_templates](https://doutorai.pt/en/components/skills__bug-report-templates): Standard templates for clear, reproducible, actionable bug documentation.
- [build_decision_tree](https://doutorai.pt/en/components/skills__build-decision-tree): Generates LaTeX/TikZ code for clinical decision algorithms from simple text or YAML descriptions.
- [building](https://doutorai.pt/en/components/skills__building): This guide covers circuit construction in Cirq, including qubits, gates, operations, and circuit patterns.
- [building-blog](https://doutorai.pt/en/components/skills__building-blog): Use when adding a blog to a Next.js + Sanity site, building a blog section from scratch, integrating Sanity CMS for editorial content, or setting up an SEO-opti
- [bullmq-specialist](https://doutorai.pt/en/components/skills__bullmq-specialist): BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue,
- [bun-development](https://doutorai.pt/en/components/skills__bun-development): Modern JavaScript/TypeScript development with Bun runtime. Covers package management, bundling, testing, and migration from Node.js. Use when working with Bun, 
- [bundle-barrel-imports](https://doutorai.pt/en/components/skills__bundle-barrel-imports): Popular icon and component libraries can have up to 10,000 re-exports in their entry file. For many React packages, it takes 200-800ms just to import them, affe
- [bundle-barrel-imports](https://doutorai.pt/en/components/skills__bundle-barrel-imports-2): Popular icon and component libraries can have up to 10,000 re-exports in their entry file. For many React packages, it takes 200-800ms just to import them, affe
- [bundle-conditional](https://doutorai.pt/en/components/skills__bundle-conditional): Load large data or modules only when a feature is activated.
- [bundle-conditional](https://doutorai.pt/en/components/skills__bundle-conditional-2): Load large data or modules only when a feature is activated.
- [bundle-defer-third-party](https://doutorai.pt/en/components/skills__bundle-defer-third-party): Analytics, logging, and error tracking don't block user interaction. Load them after hydration.
- [bundle-defer-third-party](https://doutorai.pt/en/components/skills__bundle-defer-third-party-2): Analytics, logging, and error tracking don't block user interaction. Load them after hydration.
- [bundle-dynamic-imports](https://doutorai.pt/en/components/skills__bundle-dynamic-imports): Use next/dynamic to lazy-load large components not needed on initial render.
- [bundle-dynamic-imports](https://doutorai.pt/en/components/skills__bundle-dynamic-imports-2): Use next/dynamic to lazy-load large components not needed on initial render.
- [bundle-preload](https://doutorai.pt/en/components/skills__bundle-preload): Preload heavy bundles before they're needed to reduce perceived latency.
- [bundle-preload](https://doutorai.pt/en/components/skills__bundle-preload-2): Preload heavy bundles before they're needed to reduce perceived latency.
- [business-ops](https://doutorai.pt/en/components/skills__business-ops): Workflows and procedures for business swarm agents.
- [busybox-on-windows](https://doutorai.pt/en/components/skills__busybox-on-windows): How to use a Win32 build of BusyBox to run many of the standard UNIX command line tools on Windows.
- [c-pro](https://doutorai.pt/en/components/skills__c-pro): Write efficient C code with proper memory management, pointer
- [c4-architecture](https://doutorai.pt/en/components/skills__c4-architecture): Generate architecture documentation using C4 model Mermaid diagrams. Use when asked to create architecture diagrams, document system architecture, visualize sof
- [c4-diagrams](https://doutorai.pt/en/components/skills__c4-diagrams): The C4 model provides a hierarchical way to visualize software architecture at different levels of abstraction: Context, Containers, Components, and Code.
- [c4-syntax](https://doutorai.pt/en/components/skills__c4-syntax): Complete syntax reference for Mermaid C4 diagrams. Compatible with PlantUML C4 syntax.
- [calculate-metadata](https://doutorai.pt/en/components/skills__calculate-metadata): Dynamically set composition duration, dimensions, and props
- [calculate-metadata](https://doutorai.pt/en/components/skills__calculate-metadata-2): Dynamically set composition duration, dimensions, and props
- [calculate_cac](https://doutorai.pt/en/components/skills__calculate-cac): Calculate blended and channel-specific CAC for marketing campaigns. Supports multiple time periods and channel breakdowns.
- [calculate_scores](https://doutorai.pt/en/components/skills__calculate-scores): Calculate aggregate evaluation scores from dimension-level ratings. Supports weighted averaging, threshold analysis, and score visualization.
- [calibration](https://doutorai.pt/en/components/skills__calibration): Complete guide to calibration data selection and quantization process.
- [callbacks](https://doutorai.pt/en/components/skills__callbacks): Callbacks add functionality to training without modifying the LightningModule. They capture non-essential logic like checkpointing, early stopping, and logging.
- [callbacks](https://doutorai.pt/en/components/skills__callbacks-2): This document provides comprehensive information about the callback system in Stable Baselines3 for monitoring and controlling training.
- [callbacks](https://doutorai.pt/en/components/skills__callbacks-3): Callbacks enable adding arbitrary self-contained programs to training without cluttering your LightningModule research code. They execute custom logic at specif
- [campaigns](https://doutorai.pt/en/components/skills__campaigns): When evaluating competitor campaigns: - What channels are they using? - What is their core message? - What audience are they targeting? - What is their call-to-
- [can-decode](https://doutorai.pt/en/components/skills__can-decode): Check if a video can be decoded by the browser using Mediabunny
- [can-decode](https://doutorai.pt/en/components/skills__can-decode-2): Check if a video can be decoded by the browser using Mediabunny
- [canvas-design](https://doutorai.pt/en/components/skills__canvas-design): Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, d
- [capa-officer](https://doutorai.pt/en/components/skills__capa-officer): Senior CAPA Officer specialist for managing Corrective and Preventive Actions within Quality Management Systems. Provides CAPA process management, root cause an
- [captions](https://doutorai.pt/en/components/skills__captions): Auto-generated captions and subtitle options for HeyGen videos
- [capture-guide](https://doutorai.pt/en/components/skills__capture-guide): Or just use two Agent tool calls in the same message.
- [career-changer-translator](https://doutorai.pt/en/components/skills__career-changer-translator): Translate skills from one industry to another and identify transferable skills for career pivots. Use when the user mentions changing careers, switching industr
- [case_report_guidelines](https://doutorai.pt/en/components/skills__case-report-guidelines): The CARE guidelines provide a framework for transparent and complete reporting of clinical cases. The CARE checklist ensures that case reports contain all neces
- [case_report_template](https://doutorai.pt/en/components/skills__case-report-template): Written informed consent was obtained from the patient for publication of this case report and any accompanying images. A copy of the written consent is availab
- [cc-skill-continuous-learning](https://doutorai.pt/en/components/skills__cc-skill-continuous-learning): Development skill from everything-claude-code
- [cc-skill-project-guidelines-example](https://doutorai.pt/en/components/skills__cc-skill-project-guidelines-example): Project Guidelines Skill (Example)
- [cc-skill-strategic-compact](https://doutorai.pt/en/components/skills__cc-skill-strategic-compact): Development skill from everything-claude-code
- [cellxgene-census](https://doutorai.pt/en/components/skills__cellxgene-census): Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single
- [census_schema](https://doutorai.pt/en/components/skills__census-schema): The CZ CELLxGENE Census is a versioned collection of single-cell data built on the TileDB-SOMA framework. This reference documents the data structure, available
- [ceo-advisor](https://doutorai.pt/en/components/skills__ceo-advisor): Executive leadership guidance for strategic decision-making, organizational development, and stakeholder management. Includes strategy analyzer, financial scena
- [cf-crawl](https://doutorai.pt/en/components/skills__cf-crawl): Crawl entire websites using Cloudflare Browser Rendering /crawl API. Initiates async crawl jobs, polls for completion, and saves results as markdown files. Usef
- [chain-of-thought](https://doutorai.pt/en/components/skills__chain-of-thought): Chain-of-Thought (CoT) prompting elicits step-by-step reasoning from LLMs, dramatically improving performance on complex reasoning, math, and logic tasks.
- [changelog-generator](https://doutorai.pt/en/components/skills__changelog-generator): Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, 
- [chart-structure](https://doutorai.pt/en/components/skills__chart-structure): Complete guide to Helm chart organization, file conventions, and best practices.
- [chart-types](https://doutorai.pt/en/components/skills__chart-types): Comprehensive guide to chart types organized by category.
- [charts](https://doutorai.pt/en/components/skills__charts): Chart and data visualization patterns for Remotion. Use when creating bar charts, pie charts, histograms, progress bars, or any data-driven animations.
- [charts](https://doutorai.pt/en/components/skills__charts-2): Chart and data visualization patterns for Remotion. Use when creating bar charts, pie charts, histograms, progress bars, or any data-driven animations.
- [charts](https://doutorai.pt/en/components/skills__charts-3): No,Data Type,Keywords,Best Chart Type,Secondary Options,Color Guidance,Performance Impact,Accessibility Notes,Library Recommendation,Interactive Level 1,Trend O
- [check_bounding_boxes](https://doutorai.pt/en/components/skills__check-bounding-boxes): if __name__ == "__main__": if len(sys.argv) != 2: print("Usage: check_bounding_boxes.py [fields.json]") sys.exit(1) # Input file should be in the fields.json fo
- [check_deidentification](https://doutorai.pt/en/components/skills__check-deidentification): Scans text for 18 HIPAA identifiers and flags potential privacy violations.
- [check_staleness](https://doutorai.pt/en/components/skills__check-staleness): Combines three sources: 1. Working-tree changes (unstaged + staged) via git diff --name-only HEAD 2. Untracked new files via git ls-files --others --exclude-sta
- [checklists](https://doutorai.pt/en/components/skills__checklists): This reference documents the mandatory checklist requirements for major ML/AI conferences. All major venues now require paper checklists - missing them results 
- [checkpoint](https://doutorai.pt/en/components/skills__checkpoint): TorchTitan uses PyTorch Distributed Checkpoint (DCP) for fault-tolerant, interoperable checkpointing.
- [chembl-database](https://doutorai.pt/en/components/skills__chembl-database): Query ChEMBL's bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, per
- [chemistry_molecular_formats](https://doutorai.pt/en/components/skills__chemistry-molecular-formats): This reference covers file formats commonly used in computational chemistry, cheminformatics, molecular modeling, and related fields.
- [chroma](https://doutorai.pt/en/components/skills__chroma): Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function AP
- [chrome-extension](https://doutorai.pt/en/components/skills__chrome-extension): Chrome Extension template principles. Manifest V3, React, TypeScript.
- [chrome-extension-developer](https://doutorai.pt/en/components/skills__chrome-extension-developer): Expert in building Chrome Extensions using Manifest V3. Covers background scripts, service workers, content scripts, and cross-context communication.
- [chronic_disease_management_plan](https://doutorai.pt/en/components/skills__chronic-disease-management-plan): extbf{HIPAA Notice}: De-identify all protected health information before sharing.
- [cicd_pipeline_guide](https://doutorai.pt/en/components/skills__cicd-pipeline-guide): This reference guide provides comprehensive information for senior devops.
- [cinematic-shots](https://doutorai.pt/en/components/skills__cinematic-shots): Use these for filmic, mood-forward clips. Keep one subject, one action, one camera move.
- [cirq](https://doutorai.pt/en/components/skills__cirq): Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum c
- [citation-management](https://doutorai.pt/en/components/skills__citation-management): Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generat
- [citation-workflow](https://doutorai.pt/en/components/skills__citation-workflow): This reference provides a complete workflow for managing citations programmatically, preventing AI-generated citation hallucinations, and maintaining clean bibl
- [citation_checklist](https://doutorai.pt/en/components/skills__citation-checklist): Use this checklist to ensure your citations are accurate, complete, and properly formatted before final submission.
- [citation_styles](https://doutorai.pt/en/components/skills__citation-styles): Citation styles provide standardized formats for acknowledging sources in scientific writing. Different disciplines prefer different styles, and journals typica
- [citation_styles](https://doutorai.pt/en/components/skills__citation-styles-2): This document provides detailed guidelines for formatting citations in various academic styles commonly used in literature reviews.
- [citation_validation](https://doutorai.pt/en/components/skills__citation-validation): Comprehensive guide to validating citation accuracy, completeness, and formatting in BibTeX files.
- [classification](https://doutorai.pt/en/components/skills__classification): Aeon provides 13 categories of time series classifiers with scikit-learn compatible APIs.
- [claude-api](https://doutorai.pt/en/components/skills__claude-api): Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API
- [claude-api](https://doutorai.pt/en/components/skills__claude-api-2): ContentBlock narrowing: .thinking() / .text() return Optional - use .ifPresent(...) or .stream().flatMap(...). Alternative: isThinking() / asThinking() boolean+
- [claude-api](https://doutorai.pt/en/components/skills__claude-api-3): For fine-grained control over the agentic loop, define tools with ToolParam, check StopReason, execute tools yourself, and feed tool_result blocks back. This is
- [claude-api](https://doutorai.pt/en/components/skills__claude-api-4): Alternative to TryPick: .Select(b => b.Value).OfType () (same LINQ pattern as the Basic Message example).
- [claude-api](https://doutorai.pt/en/components/skills__claude-api-5): Tools are passed as arrays. The SDK uses camelCase keys (inputSchema, toolUseID, stopReason) and auto-maps to the API's snake_case on the wire - since v0.5.0. S
- [claude-api](https://doutorai.pt/en/components/skills__claude-api-6): The Ruby SDK supports tool use via raw JSON schema definitions and also provides a beta tool runner for automatic tool execution.
- [claude-code-integration](https://doutorai.pt/en/components/skills__claude-code-integration): This file is read when the user is running the skill inside Claude Code and wants the blueprint workspace to be a living, automated system rather than a folder 
- [claude-code-sessions](https://doutorai.pt/en/components/skills__claude-code-sessions): Search, analyze, and manage Claude Code session history. Use when the user wants to find past sessions, check token usage, review tool breakdowns, resume previo
- [claude-opus-4-5-migration](https://doutorai.pt/en/components/skills__claude-opus-4-5-migration): Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to
- [claude_digest](https://doutorai.pt/en/components/skills__claude-digest): for i, s in enumerate(sessions, 1): lines.append(f"### {i}. {s.title}") lines.append(f" Session: {s.id}") if s.branch: lines.append(f" Branch: {s.branch}") if s
- [clean-code](https://doutorai.pt/en/components/skills__clean-code): Pragmatic coding standards - concise, direct, no over-engineering, no unnecessary comments
- [cleanup_manager](https://doutorai.pt/en/components/skills__cleanup-manager): print(" ⚠️ WARNING: This will delete the files shown above!") print(" Note: .venv is preserved (part of skill infrastructure)") response = input("Are you sure? 
- [clerk-auth](https://doutorai.pt/en/components/skills__clerk-auth): Expert patterns for Clerk auth implementation, middleware, organizations, webhooks, and user sync Use when: adding authentication, clerk auth, user authenticati
- [cli](https://doutorai.pt/en/components/skills__cli): Configuration is read from components.json.
- [cli](https://doutorai.pt/en/components/skills__cli-2): This file contains the command catalog for the bundled video generation CLI. Keep SKILL.md overview-first; put verbose CLI details here.
- [cli](https://doutorai.pt/en/components/skills__cli-3): Gtars provides a comprehensive CLI for genomic interval analysis directly from the terminal.
- [cli](https://doutorai.pt/en/components/skills__cli-4): This file contains the "command catalog" for the bundled speech generation CLI. Keep SKILL.md as overview-first; put verbose CLI details here.
- [cli](https://doutorai.pt/en/components/skills__cli-5): This file contains the “command catalog” for the bundled image generation CLI. Keep SKILL.md as overview-first; put verbose CLI details here.
- [cli](https://doutorai.pt/en/components/skills__cli-6): User-scoped skills install under $CODEX_HOME/skills (default: ~/.codex/skills).
- [cli-commands](https://doutorai.pt/en/components/skills__cli-commands): Quick reference for common Netlify CLI commands used in deployments.
- [cli-reference](https://doutorai.pt/en/components/skills__cli-reference): Even with an override, fall back through the chain for unsupported operations (e.g., cluster creation always requires gcloud or MCP).
- [cli-tool](https://doutorai.pt/en/components/skills__cli-tool): Node.js CLI tool template principles. Commander.js, interactive prompts.
- [cli-usage](https://doutorai.pt/en/components/skills__cli-usage): AlloyDB resources are managed using the gcloud alloydb command group.
- [cli-usage](https://doutorai.pt/en/components/skills__cli-usage-2): The gcloud sql command group is used to manage Cloud SQL instances and related resources.
- [cli-usage](https://doutorai.pt/en/components/skills__cli-usage-3): The bq command-line tool is used to interact with BigQuery for managing resources and running jobs.
- [cli-usage](https://doutorai.pt/en/components/skills__cli-usage-4): Use the gcloud run command to manage your Cloud Run applications.
- [cli-usage](https://doutorai.pt/en/components/skills__cli-usage-5): The Firebase CLI (firebase-tools) is the primary tool for managing Firebase projects and resources from the command line.
- [cli_operations](https://doutorai.pt/en/components/skills__cli-operations): Complete guide for operating CocoIndex flows using the CLI.
- [clickhouse-io](https://doutorai.pt/en/components/skills__clickhouse-io): ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
- [client-event-listeners](https://doutorai.pt/en/components/skills__client-event-listeners): Use useSWRSubscription() to share global event listeners across component instances.
- [client-event-listeners](https://doutorai.pt/en/components/skills__client-event-listeners-2): Use useSWRSubscription() to share global event listeners across component instances.
- [client-library-usage](https://doutorai.pt/en/components/skills__client-library-usage): Google Cloud provides various ways to connect to AlloyDB idiomatically from different programming languages. We optionally provide Client Libraries and Connecto
- [client-library-usage](https://doutorai.pt/en/components/skills__client-library-usage-2): Google Cloud provides client libraries and connectors to simplify connecting to Cloud SQL from various programming languages.
- [client-library-usage](https://doutorai.pt/en/components/skills__client-library-usage-3): Google Cloud client libraries provide an idiomatic way to interact with BigQuery from your preferred programming language.
- [client-library-usage](https://doutorai.pt/en/components/skills__client-library-usage-4): Google Cloud client libraries provide an idiomatic way to manage Cloud Run resources programmatically.
- [client-library-usage](https://doutorai.pt/en/components/skills__client-library-usage-5): To interact with the GKE (Kubernetes) API programmatically, use the official Kubernetes client libraries.
- [client-library-usage](https://doutorai.pt/en/components/skills__client-library-usage-6): Firebase provides SDKs for both client-side application development and server-side administrative tasks.
- [client-logging](https://doutorai.pt/en/components/skills__client-logging): Server logging allows MCP tools to send debug, info, warning, and error messages back to the client. Unlike standard Python logging, MCP server logging sends me
- [client-swr-dedup](https://doutorai.pt/en/components/skills__client-swr-dedup): SWR enables request deduplication, caching, and revalidation across component instances.
- [client-swr-dedup](https://doutorai.pt/en/components/skills__client-swr-dedup-2): SWR enables request deduplication, caching, and revalidation across component instances.
- [clinical-decision-support](https://doutorai.pt/en/components/skills__clinical-decision-support): Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-
- [clinical-reports](https://doutorai.pt/en/components/skills__clinical-reports): Write comprehensive clinical reports including case reports (CARE guidelines), diagnostic reports (radiology/pathology/lab), clinical trial reports (ICH-E3, SAE
- [clinical_decision_algorithms](https://doutorai.pt/en/components/skills__clinical-decision-algorithms): Clinical decision algorithms provide systematic, step-by-step guidance for diagnosis, treatment selection, and patient management. This guide covers algorithm d
- [clinical_pathway_template](https://doutorai.pt/en/components/skills__clinical-pathway-template): extbf{Validation}: Pathway validated at [institution] with [X\%] adherence rate and [outcome metrics].
- [clinical_significance](https://doutorai.pt/en/components/skills__clinical-significance): ClinVar uses standardized terminology to describe the clinical significance of genetic variants. Understanding these classifications is critical for interpretin
- [clinical_trial_csr_template](https://doutorai.pt/en/components/skills__clinical-trial-csr-template): Informed consent was obtained from all participants before any study-specific procedures. The informed consent process included: - Written information about stu
- [clinical_trial_reporting](https://doutorai.pt/en/components/skills__clinical-trial-reporting): The International Council for Harmonisation (ICH) E3 guideline defines the structure and content of clinical study reports (CSRs) for regulatory submission.
- [clinicaltrials-database](https://doutorai.pt/en/components/skills__clinicaltrials-database): Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical r
- [clinpgx-database](https://doutorai.pt/en/components/skills__clinpgx-database): Access ClinPGx pharmacogenomics data (successor to PharmGKB). Query gene-drug interactions, CPIC guidelines, allele functions, for precision medicine and genoty
- [clinvar-database](https://doutorai.pt/en/components/skills__clinvar-database): Query NCBI ClinVar for variant clinical significance. Search by gene/position, interpret pathogenicity classifications, access via E-utilities API or FTP, annot
- [clip](https://doutorai.pt/en/components/skills__clip): OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-tex
- [cloud-architect](https://doutorai.pt/en/components/skills__cloud-architect): Expert cloud architect specializing in AWS/Azure/GCP multi-cloud infrastructure design, advanced IaC (Terraform/OpenTofu/CDK), FinOps cost optimization, and mod
- [cloud-devops](https://doutorai.pt/en/components/skills__cloud-devops): Cloud infrastructure and DevOps workflow covering AWS, Azure, GCP, Kubernetes, Terraform, CI/CD, monitoring, and cloud-native development.
- [cloud-nat-analysis](https://doutorai.pt/en/components/skills__cloud-nat-analysis): Use Cloud NAT logs (compute.googleapis.com/nat_flows) to audit traffic going through NAT gateways or troubleshoot port exhaustion.
- [cloud-run-basics](https://doutorai.pt/en/components/skills__cloud-run-basics): Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or sche
- [cloud-sql-basics](https://doutorai.pt/en/components/skills__cloud-sql-basics): Creates and manages Cloud SQL instances for MySQL, PostgreSQL, and SQL Server. Handles backups, high availability, and secure connectivity for relational databa
- [cloudflare-deploy](https://doutorai.pt/en/components/skills__cloudflare-deploy): Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or se
- [clustering](https://doutorai.pt/en/components/skills__clustering): Aeon provides clustering algorithms adapted for temporal data with specialized distance metrics and averaging methods.
- [cobrapy](https://doutorai.pt/en/components/skills__cobrapy): Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.
- [cocoindex](https://doutorai.pt/en/components/skills__cocoindex): Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or
- [code-generation-printing](https://doutorai.pt/en/components/skills__code-generation-printing): This document covers SymPy's capabilities for generating executable code in various languages, converting expressions to different output formats, and customizi
- [code-quality-reviewer-prompt](https://doutorai.pt/en/components/skills__code-quality-reviewer-prompt): Use this template when dispatching a code quality reviewer subagent.
- [code-quality-reviewer-prompt](https://doutorai.pt/en/components/skills__code-quality-reviewer-prompt-2): Use this template when dispatching a code quality reviewer subagent.
- [code-review](https://doutorai.pt/en/components/skills__code-review): Perform code reviews following Sentry engineering practices. Use when reviewing pull requests, examining code changes, or providing feedback on code quality. Co
- [code-review-checklist](https://doutorai.pt/en/components/skills__code-review-checklist): Comprehensive checklist for conducting thorough code reviews covering functionality, security, performance, and maintainability
- [code-review-excellence](https://doutorai.pt/en/components/skills__code-review-excellence): Transform code reviews from gatekeeping to knowledge sharing through constructive feedback, systematic analysis, and collaborative improvement.
- [code-reviewer](https://doutorai.pt/en/components/skills__code-reviewer): You are reviewing code changes for production readiness.
- [code-reviewer](https://doutorai.pt/en/components/skills__code-reviewer-2): Comprehensive code review skill for TypeScript, JavaScript, Python, Swift, Kotlin, Go. Includes automated code analysis, best practice checking, security scanni
- [code-simplifier](https://doutorai.pt/en/components/skills__code-simplifier): Simplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. Use when asked to "simplify code", "clean up code"
- [code_review_checklist](https://doutorai.pt/en/components/skills__code-review-checklist-2): This reference guide provides comprehensive information for code reviewer.
- [codebase-analysis](https://doutorai.pt/en/components/skills__codebase-analysis): Use this reference for framework-specific detection and build/start command selection when preparing a Render deployment.
- [codex](https://doutorai.pt/en/components/skills__codex): Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing. Uses GPT-5.2
- [codex-network](https://doutorai.pt/en/components/skills__codex-network): This guidance is intentionally isolated from SKILL.md because it can vary by environment and may become stale. Prefer the defaults in your environment when in d
- [codex-network](https://doutorai.pt/en/components/skills__codex-network-2): This guidance is intentionally isolated from SKILL.md because it can vary by environment and may become stale. Prefer the defaults in your environment when in d
- [codex-network](https://doutorai.pt/en/components/skills__codex-network-3): This guidance is intentionally isolated from SKILL.md because it can vary by environment and may become stale. Prefer the defaults in your environment when in d
- [codex-review](https://doutorai.pt/en/components/skills__codex-review): Professional code review with auto CHANGELOG generation, integrated with Codex AI
- [coding-standards](https://doutorai.pt/en/components/skills__coding-standards): Universal coding standards, best practices, and patterns for TypeScript, JavaScript, React, and Node.js development.
- [coding_standards](https://doutorai.pt/en/components/skills__coding-standards-2): This reference guide provides comprehensive information for code reviewer.
- [cohort_analysis_template](https://doutorai.pt/en/components/skills__cohort-analysis-template): extbf{Interpretation}: After adjusting for age, performance status, and disease stage, [biomarker status] remained an independent predictor of [PFS/OS] (HR X.XX
- [colm2025_conference](https://doutorai.pt/en/components/skills__colm2025-conference): % Authors must not appear in the submitted version. They should be hidden % as long as the \colmfinalcopy macro remains commented out below. % Non-anonymous sub
- [colm2025_conference](https://doutorai.pt/en/components/skills__colm2025-conference-2): %PDF-1.5 % 11 0 obj > endobj 12 0 obj > /W [ 1 3 1 ] /Index [ 11 164 ] /Info 9 0 R /Root 13 0 R /Size 175 /Prev 122302 /ID [ ] >> stream xcbdgb8 "HZ}0{1 /A$Y i 
- [colm2025_conference](https://doutorai.pt/en/components/skills__colm2025-conference-3): %% File: iclr2024.bst' %% A copy of iclm2010.bst, which is a modification of plainnl.bst' for use with natbib package %% %% Copyright 2010 Hal Daum\'e III %% Mo
- [colm2025_conference](https://doutorai.pt/en/components/skills__colm2025-conference-4): % This file can be used with Latex2e whether running in main mode, or % 2.09 compatibility mode. % % If using main mode, you need to include the commands % \doc
- [color_palettes](https://doutorai.pt/en/components/skills__color-palettes): This module provides carefully curated color palettes optimized for scientific publications and accessibility.
- [color_palettes](https://doutorai.pt/en/components/skills__color-palettes-2): Color choice in scientific visualization is critical for accessibility, clarity, and accurate data representation. This reference provides colorblind-friendly p
- [color_schemes](https://doutorai.pt/en/components/skills__color-schemes): % Example 1: Strong recommendation box % \begin{tcolorbox}[enhanced,colback=stronggreen!10,colframe=stronggreen, % title={ extbf{STRONG RECOMMENDATION} \hfill e
- [colour-schemes](https://doutorai.pt/en/components/skills__colour-schemes): Comprehensive guide to colour selection in data visualisation with d3.js.
- [command-creator](https://doutorai.pt/en/components/skills__command-creator): This skill should be used when creating a Claude Code slash command. Use when users ask to "create a command", "make a slash command", "add a command", or want 
- [comment](https://doutorai.pt/en/components/skills__comment): Text should be pre-escaped XML (e.g., &amp; for &, &#x2019; for smart quotes).
- [comment](https://doutorai.pt/en/components/skills__comment-2): Text should be pre-escaped XML (e.g., &amp; for &, &#x2019; for smart quotes).
- [commit](https://doutorai.pt/en/components/skills__commit): Create commit messages following Sentry conventions. Use when committing code changes, writing commit messages, or formatting git history. Follows conventional 
- [commit-smart](https://doutorai.pt/en/components/skills__commit-smart): Analyze staged/unstaged changes and create semantic conventional commits with context about WHY, not just WHAT. Auto-detects commit type and scope from the diff
- [commit-work](https://doutorai.pt/en/components/skills__commit-work): Create high-quality git commits: review/stage intended changes, split into logical commits, and write clear commit messages (including Conventional Commits). Us
- [common-mistakes](https://doutorai.pt/en/components/skills__common-mistakes-2): Reference guide for common mistakes when using @neondatabase/neon-js.
- [common-mistakes](https://doutorai.pt/en/components/skills__common-mistakes-3): Reference guide for common mistakes when using @neondatabase/auth or @neondatabase/neon-js.
- [common-mistakes](https://doutorai.pt/en/components/skills__common-mistakes-4): This guide documents frequent anti-patterns and errors when creating C4 architecture diagrams, with examples of what to do instead.
- [common-patterns](https://doutorai.pt/en/components/skills__common-patterns-3): Frequently used patterns for forms, authentication, DataGrid, dialogs, and other common UI elements.
- [common_antipatterns](https://doutorai.pt/en/components/skills__common-antipatterns): This reference guide provides comprehensive information for code reviewer.
- [common_issues](https://doutorai.pt/en/components/skills__common-issues): This document catalogs frequent issues encountered during peer review, organized by category. Use this as a reference to identify potential problems and provide
- [common_issues](https://doutorai.pt/en/components/skills__common-issues-2): Troubleshooting guide for frequently encountered matplotlib problems.
- [common_patterns](https://doutorai.pt/en/components/skills__common-patterns-4): Use when exploring available data without loading expression matrices.
- [common_queries](https://doutorai.pt/en/components/skills__common-queries): This document provides practical examples for common research queries using OpenAlex.
- [common_queries](https://doutorai.pt/en/components/skills__common-queries-2): This reference provides practical examples of common PubMed search patterns for various research scenarios.
- [common_tags](https://doutorai.pt/en/components/skills__common-tags): This document provides a comprehensive list of commonly used DICOM tags organized by category. Tags can be accessed in pydicom using attribute notation (e.g., d
- [common_workflows](https://doutorai.pt/en/components/skills__common-workflows): This document provides practical examples of common bioinformatics workflows using pysam, demonstrating how to combine different file types and operations.
- [common_workflows](https://doutorai.pt/en/components/skills__common-workflows-2): This document provides examples of common workflows and use cases for working with NCBI Gene database.
- [company-newsletter](https://doutorai.pt/en/components/skills__company-newsletter): Ideally it includes the following attributes: - Lots of links: pulling documents from Google Drive that are very relevant, linking to prominent Slack messages i
- [company-newsletter](https://doutorai.pt/en/components/skills__company-newsletter-2): Ideally it includes the following attributes: - Lots of links: pulling documents from Google Drive that are very relevant, linking to prominent Slack messages i
- [comparator](https://doutorai.pt/en/components/skills__comparator): Compare two outputs WITHOUT knowing which skill produced them.
- [comparator](https://doutorai.pt/en/components/skills__comparator-2): Compare two outputs WITHOUT knowing which skill produced them.
- [comparator](https://doutorai.pt/en/components/skills__comparator-3): Compare two outputs WITHOUT knowing which skill produced them.
- [competing-risks](https://doutorai.pt/en/components/skills__competing-risks): Competing risks occur when subjects can experience one of several mutually exclusive events (event types). When one event occurs, it prevents ("competes with") 
- [competitive-ads-extractor](https://doutorai.pt/en/components/skills__competitive-ads-extractor): Extracts and analyzes competitors' ads from ad libraries (Facebook, LinkedIn, etc.) to understand what messaging, problems, and creative approaches are working.
- [competitor-alternatives](https://doutorai.pt/en/components/skills__competitor-alternatives): When the user wants to create competitor comparison or alternative pages for SEO and sales enablement. Also use when the user mentions 'alternative page,' 'vs p
- [complete-agent-examples](https://doutorai.pt/en/components/skills__complete-agent-examples): Full, production-ready agent examples for common use cases. Use these as templates for your own agents.
- [complete-examples](https://doutorai.pt/en/components/skills__complete-examples): Full working examples combining all modern patterns: React.FC, lazy loading, Suspense, useSuspenseQuery, styling, routing, and error handling.
- [complete-examples](https://doutorai.pt/en/components/skills__complete-examples-2): Real-world examples showing complete implementation patterns.
- [complexity](https://doutorai.pt/en/components/skills__complexity): Complexity measures quantify the irregularity, unpredictability, and multiscale structure of time series signals. NeuroKit2 provides comprehensive entropy, frac
- [compliance-frameworks](https://doutorai.pt/en/components/skills__compliance-frameworks): SOC 2 is an auditing standard developed by the American Institute of CPAs (AICPA) for service organizations. It evaluates a company's information systems based 
- [compliance_requirements](https://doutorai.pt/en/components/skills__compliance-requirements): This reference guide provides comprehensive information for senior secops.
- [component-examples](https://doutorai.pt/en/components/skills__component-examples): Detailed implementation examples for common design system components.
- [component-patterns](https://doutorai.pt/en/components/skills__component-patterns): Modern React component architecture for the application emphasizing type safety, lazy loading, and Suspense boundaries.
- [component-patterns](https://doutorai.pt/en/components/skills__component-patterns-2): Advanced patterns for organizing plugin components effectively.
- [component-template](https://doutorai.pt/en/components/skills__component-template): / Component Name Brief description of what this component does and when to use it. @example /
- [components](https://doutorai.pt/en/components/skills__components): Reducing nesting and complexity is achieved by breaking down views into generic, reusable components.
- [composition](https://doutorai.pt/en/components/skills__composition): Never render items directly inside the content container.
- [composition](https://doutorai.pt/en/components/skills__composition-2): Ensuring your ViewModels are correctly instantiated and mapped to their corresponding Views is crucial for a maintainable application.
- [compositions](https://doutorai.pt/en/components/skills__compositions): Defining compositions, stills, folders, default props and dynamic metadata
- [compositions](https://doutorai.pt/en/components/skills__compositions-2): Defining compositions, stills, folders, default props and dynamic metadata
- [compound_search](https://doutorai.pt/en/components/skills__compound-search): This script provides functions for searching and retrieving compound information from PubChem using the PubChemPy library.
- [computer-use-agents](https://doutorai.pt/en/components/skills__computer-use-agents): Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Us
- [computer_vision_architectures](https://doutorai.pt/en/components/skills__computer-vision-architectures): World-class computer vision architectures for senior computer vision engineer.
- [concatenation](https://doutorai.pt/en/components/skills__concatenation): Combine multiple AnnData objects along either observations or variables axis.
- [concise-planning](https://doutorai.pt/en/components/skills__concise-planning): Use when a user asks for a plan for a coding task, to generate a clear, actionable, and atomic checklist.
- [condition-based-waiting](https://doutorai.pt/en/components/skills__condition-based-waiting): Flaky tests often guess at timing with arbitrary delays. This creates race conditions where tests pass on fast machines but fail under load or in CI.
- [conferences_formatting](https://doutorai.pt/en/components/skills__conferences-formatting): Comprehensive formatting requirements and submission guidelines for major academic conferences across disciplines.
- [confidence_and_limitations](https://doutorai.pt/en/components/skills__confidence-and-limitations): This document provides detailed guidance on interpreting DiffDock confidence scores and understanding the tool's limitations.
- [config_template](https://doutorai.pt/en/components/skills__config-template): Generate 5 distinct observations about patterns that distinguish between the two classes. Focus on specific, testable characteristics.
- [configuration](https://doutorai.pt/en/components/skills__configuration): NeMo Evaluator uses Hydra for configuration management with a hierarchical override system.
- [configuration](https://doutorai.pt/en/components/skills__configuration-2): This guide covers configuring apps through dxapp.json metadata and managing dependencies including system packages, Python libraries, and Docker containers.
- [configuration](https://doutorai.pt/en/components/skills__configuration-4): Cache Reserve works automatically with Workers deployed via Wrangler. No special wrangler.jsonc configuration needed - enable Cache Reserve via Dashboard or API
- [configuration](https://doutorai.pt/en/components/skills__configuration-9): Enterprise plans get granular 1-99 scores for custom thresholds.
- [configuration](https://doutorai.pt/en/components/skills__configuration-10): Complete resource configurations for Cloudflare infrastructure.
- [configuration](https://doutorai.pt/en/components/skills__configuration-11): Dashboard shows: files indexed, status, last index time, storage usage.
- [configuration](https://doutorai.pt/en/components/skills__configuration-14): Tail Worker receives TWO TraceItem elements per request: 1. Dynamic dispatch Worker event 2. User Worker event
- [configuration](https://doutorai.pt/en/components/skills__configuration-15): Setup and configuration for Cloudflare TURN service in Workers and applications.
- [configuration](https://doutorai.pt/en/components/skills__configuration-16): Setup and configuration for TCP Sockets in Cloudflare Workers.
- [configuration](https://doutorai.pt/en/components/skills__configuration-18): Setup and configuration for R2 SQL queries.
- [configuration](https://doutorai.pt/en/components/skills__configuration-22): Tail Workers receive logs/traces from other Workers for filtering, transformation, or export.
- [configuration](https://doutorai.pt/en/components/skills__configuration-25): Variants are named presets for transformations.
- [configuration](https://doutorai.pt/en/components/skills__configuration-26): Deploy HTML/CSS/images with Workers. See api.md for upload process.
- [configuration](https://doutorai.pt/en/components/skills__configuration-27): How to enable R2 Data Catalog and configure authentication.
- [configuration](https://doutorai.pt/en/components/skills__configuration-28): Smart Placement is fundamentally limited to Workers with default fetch handlers. This is a key architectural constraint.
- [configuration](https://doutorai.pt/en/components/skills__configuration-29): Use when origin is a single server with static IP.
- [configuration](https://doutorai.pt/en/components/skills__configuration-30): Version = max compat date. Update carefully after testing.
- [configuration](https://doutorai.pt/en/components/skills__configuration-34): Custom envVars from Container class are merged with these. Custom vars override runtime vars if names conflict.
- [configuration](https://doutorai.pt/en/components/skills__configuration-35): Critical for BOLA Detection, Sequence Mitigation, and analytics. Configure header/cookie that uniquely IDs API users.
- [configuration](https://doutorai.pt/en/components/skills__configuration-37): Configuration reference for wrangler.jsonc (recommended).
- [configuration](https://doutorai.pt/en/components/skills__configuration-39): Controls which requests invoke Worker before checking assets.
- [configuration](https://doutorai.pt/en/components/skills__configuration-43): Snippets use Cloudflare's Ruleset Engine expression language to determine when to execute.
- [configuration](https://doutorai.pt/en/components/skills__configuration-44): Fires on pushState, replaceState, hash changes. No manual tracking needed.
- [configuration](https://doutorai.pt/en/components/skills__configuration-45): WAF configuration is zone-level (not Worker-specific). Configuration methods: - Dashboard UI - Cloudflare API via SDK - Terraform/Pulumi (IaC)
- [configuration](https://doutorai.pt/en/components/skills__configuration-46): Each class extends WorkflowEntrypoint with its own Params type.
- [configuration](https://doutorai.pt/en/components/skills__configuration-47): Rules evaluated top to bottom, first match wins.
- [configuration](https://doutorai.pt/en/components/skills__configuration-48): Creates worker-configuration.d.ts with typed Env interface based on your bindings.
- [configuration](https://doutorai.pt/en/components/skills__configuration-50): Helper routes requests to agents automatically based on URL patterns.
- [configuration](https://doutorai.pt/en/components/skills__configuration-52): Setup, environment variables, and wrangler configuration.
- [configuration](https://doutorai.pt/en/components/skills__configuration-53): Example: https://workers.cloudflare.com/playground#abc123...
- [configuration](https://doutorai.pt/en/components/skills__configuration-54): Or use environment-specific tokens via env vars.
- [configuration](https://doutorai.pt/en/components/skills__configuration-57): No wrangler config needed. Get API token with "Browser Rendering - Edit" permission.
- [configuration](https://doutorai.pt/en/components/skills__configuration-58): Point types in functions/tsconfig.json to generated file.
- [configuration](https://doutorai.pt/en/components/skills__configuration-59): Configuration guide for RealtimeKit setup, client SDKs, and wrangler integration.
- [configuration](https://doutorai.pt/en/components/skills__configuration-60): File structure: migrations/0001_initial_schema.sql, 0002_add_posts.sql, etc.
- [configuration](https://doutorai.pt/en/components/skills__configuration-61): For design patterns, HA architecture, and security best practices, see patterns.md.
- [configuration](https://doutorai.pt/en/components/skills__configuration-62): Configure by targeting specific adaptive rule IDs via API (see api.md#typed-override-examples).
- [configuration](https://doutorai.pt/en/components/skills__configuration-63): Complete guide to managing configuration in backend microservices.
- [configuration-guide](https://doutorai.pt/en/components/skills__configuration-guide): Common configuration patterns, best practices, and troubleshooting for Render deployments.
- [conformers_module](https://doutorai.pt/en/components/skills__conformers-module): The datamol.conformers module provides tools for generating and analyzing 3D molecular conformations.
- [conn-idle-timeout](https://doutorai.pt/en/components/skills__conn-idle-timeout): Idle connections waste resources. Configure timeouts to automatically reclaim them.
- [conn-idle-timeout](https://doutorai.pt/en/components/skills__conn-idle-timeout-2): Idle connections waste resources. Configure timeouts to automatically reclaim them.
- [conn-limits](https://doutorai.pt/en/components/skills__conn-limits): Too many connections exhaust memory and degrade performance. Set limits based on available resources.
- [conn-limits](https://doutorai.pt/en/components/skills__conn-limits-2): Too many connections exhaust memory and degrade performance. Set limits based on available resources.
- [conn-pooling](https://doutorai.pt/en/components/skills__conn-pooling): Postgres connections are expensive (1-3MB RAM each). Without pooling, applications exhaust connections under load.
- [conn-pooling](https://doutorai.pt/en/components/skills__conn-pooling-2): Postgres connections are expensive (1-3MB RAM each). Without pooling, applications exhaust connections under load.
- [conn-prepared-statements](https://doutorai.pt/en/components/skills__conn-prepared-statements): Prepared statements are tied to individual database connections. In transaction-mode pooling, connections are shared, causing conflicts.
- [conn-prepared-statements](https://doutorai.pt/en/components/skills__conn-prepared-statements-2): Prepared statements are tied to individual database connections. In transaction-mode pooling, connections are shared, causing conflicts.
- [connection](https://doutorai.pt/en/components/skills__connection): This reference covers establishing and managing connections to OMERO servers using BlitzGateway.
- [connection-methods](https://doutorai.pt/en/components/skills__connection-methods): Guide to selecting the optimal connection method for your Neon Postgres database based on deployment platform and runtime environment.
- [connections](https://doutorai.pt/en/components/skills__connections): else: raise ValueError(f"Unsupported transport type: {transport}. Use 'stdio', 'sse', or 'http'")
- [connectivity-tests](https://doutorai.pt/en/components/skills__connectivity-tests): Use Connectivity Tests to identify firewall or routing blocks along a network path.
- [consensus_peaks](https://doutorai.pt/en/components/skills__consensus-peaks): Geniml provides tools for building genomic "universes" - standardized reference sets of consensus peaks from collections of BED files. These universes represent
- [constitutional-ai](https://doutorai.pt/en/components/skills__constitutional-ai): Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from 
- [constraints](https://doutorai.pt/en/components/skills__constraints): Guide to regex constraints, grammar-based generation, and token healing in Guidance.
- [constraints_mcdm](https://doutorai.pt/en/components/skills__constraints-mcdm): Reference for constraint handling and multi-criteria decision making in pymoo.
- [containers](https://doutorai.pt/en/components/skills__containers): Using the right container for the data type simplifies XAML and improves maintainability. Zafiro.Avalonia provides specialized controls for common layout patter
- [content](https://doutorai.pt/en/components/skills__content): Test with minimum 1,000 people per variation for statistical significance. Run for at least 2-3 days to account for time-of-day effects.
- [content-creator](https://doutorai.pt/en/components/skills__content-creator): Create SEO-optimized marketing content with consistent brand voice. Includes brand voice analyzer, SEO optimizer, content frameworks, and social media templates
- [content-research-writer](https://doutorai.pt/en/components/skills__content-research-writer): Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on ea
- [context-window-management](https://doutorai.pt/en/components/skills__context-window-management): Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context 
- [context7-auto-research](https://doutorai.pt/en/components/skills__context7-auto-research): Automatically fetch latest library/framework documentation for Claude Code via Context7 API
- [conversation-framework](https://doutorai.pt/en/components/skills__conversation-framework): Complete guide to the three-phase framework for challenging workplace conversations.
- [conversation-memory](https://doutorai.pt/en/components/skills__conversation-memory): Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persist
- [convert_literature](https://doutorai.pt/en/components/skills__convert-literature): This script is specifically designed for converting academic papers, organizing them, and preparing them for literature review workflows.
- [convert_with_ai](https://doutorai.pt/en/components/skills__convert-with-ai): Popular Models (use with --model): anthropic/claude-sonnet-4.5 - Recommended for scientific vision anthropic/claude-opus-4.5 - Advanced vision model openai/gpt-
- [convex](https://doutorai.pt/en/components/skills__convex): Convex reactive backend expert: schema design, TypeScript functions, real-time subscriptions, auth, file storage, scheduling, and deployment.
- [coordinates](https://doutorai.pt/en/components/skills__coordinates): The astropy.coordinates package provides tools for representing celestial coordinates and transforming between different coordinate systems.
- [copy-editing](https://doutorai.pt/en/components/skills__copy-editing): When the user wants to edit, review, or improve existing marketing copy. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'p
- [copywriting](https://doutorai.pt/en/components/skills__copywriting): When the user wants to write, rewrite, or improve marketing copy for any page - including homepage, landing pages, pricing pages, feature pages, about pages, or
- [core-capabilities](https://doutorai.pt/en/components/skills__core-capabilities): This document covers SymPy's fundamental operations: symbolic computation basics, algebra, calculus, simplification, and equation solving.
- [core-components](https://doutorai.pt/en/components/skills__core-components): Core component library and design system patterns. Use when building UI, using design tokens, or working with the component library.
- [core-concepts](https://doutorai.pt/en/components/skills__core-concepts): AlloyDB for PostgreSQL is a fully managed, PostgreSQL-compatible database service designed for high performance, scale, and availability. It is built on top of 
- [core-concepts](https://doutorai.pt/en/components/skills__core-concepts-2): Cloud SQL provides managed relational databases, abstracting the underlying infrastructure while offering standard database engines.
- [core-concepts](https://doutorai.pt/en/components/skills__core-concepts-3): BigQuery is a fully managed, AI-ready data platform that helps you manage and analyze your data with built-in features like machine learning, search, geospatial
- [core-concepts](https://doutorai.pt/en/components/skills__core-concepts-4): This document covers the fundamental concepts and building blocks of LaminDB: Artifacts, Records, Runs, Transforms, Features, and data lineage tracking.
- [core-concepts](https://doutorai.pt/en/components/skills__core-concepts-5): Cloud Run is a fully managed application platform for running your code, function, or container on top of Google's highly scalable infrastructure. On Cloud Run,
- [core-concepts](https://doutorai.pt/en/components/skills__core-concepts-6): Google Kubernetes Engine (GKE) is a managed Kubernetes platform for deploying, managing, and scaling containerized applications on Google Cloud infrastructure. 
- [core-concepts](https://doutorai.pt/en/components/skills__core-concepts-7): Firebase is a platform of services for mobile and web applications. It offers products for managed backend infrastructure (BaaS), building AI-powered experience
- [core-technical-skills](https://doutorai.pt/en/components/skills__core-technical-skills): The developer must possess strong expertise in: - C# and modern .NET: Utilizing the latest features of the language and framework. - Avalonia UI: For cross-plat
- [core-web-vitals](https://doutorai.pt/en/components/skills__core-web-vitals): Optimize Core Web Vitals (LCP, INP, CLS) for better page experience and search ranking. Use when asked to "improve Core Web Vitals", "fix LCP", "reduce CLS", "o
- [core-workflow](https://doutorai.pt/en/components/skills__core-workflow): Full RARV cycle, CONTINUITY.md template, and autonomy rules.
- [core_api](https://doutorai.pt/en/components/skills__core-api): This document covers the main functions available in the datamol namespace.
- [core_classes](https://doutorai.pt/en/components/skills__core-classes): This reference documents the fundamental classes in pymatgen.core that form the foundation for materials analysis.
- [core_concepts](https://doutorai.pt/en/components/skills__core-concepts-9): Expressions are the foundation of Polars' API. They are composable units that describe data transformations without executing them immediately.
- [core_concepts](https://doutorai.pt/en/components/skills__core-concepts-10): This reference covers TorchDrug's fundamental architecture, design principles, and technical implementation details.
- [core_dataframes](https://doutorai.pt/en/components/skills__core-dataframes): This reference covers Vaex DataFrame basics, loading data from various sources, and understanding the DataFrame structure.
- [cosmic-database](https://doutorai.pt/en/components/skills__cosmic-database): Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision onco
- [cosmic_data_reference](https://doutorai.pt/en/components/skills__cosmic-data-reference): COSMIC (Catalogue of Somatic Mutations in Cancer) is the world's largest and most comprehensive resource for exploring the impact of somatic mutations in human 
- [cosmology](https://doutorai.pt/en/components/skills__cosmology): The astropy.cosmology subpackage provides tools for cosmological calculations based on various cosmological models.
- [cover-letter-generator](https://doutorai.pt/en/components/skills__cover-letter-generator): Create personalized, compelling cover letters from a resume and job description. Use when the user asks for a cover letter, application letter, or motivation le
- [coverage](https://doutorai.pt/en/components/skills__coverage): The uniwig module generates coverage tracks from sequencing data, providing efficient conversion of genomic intervals to coverage profiles.
- [cox-models](https://doutorai.pt/en/components/skills__cox-models): Cox proportional hazards models are semi-parametric models that relate covariates to the time of an event. The hazard function for individual i is expressed as:
- [cpp-pro](https://doutorai.pt/en/components/skills__cpp-pro): Write idiomatic C++ code with modern features, RAII, smart pointers, and STL algorithms. Handles templates, move semantics, and performance optimization.
- [crafting-effective-readmes](https://doutorai.pt/en/components/skills__crafting-effective-readmes): Use when writing or improving README files. Not all READMEs are the same - provides templates and guidance matched to your audience and project type.
- [create-plan](https://doutorai.pt/en/components/skills__create-plan): Create a concise plan. Use when a user explicitly asks for a plan related to a coding task.
- [create-pr](https://doutorai.pt/en/components/skills__create-pr): Create pull requests following Sentry conventions. Use when opening PRs, writing PR descriptions, or preparing changes for review. Follows Sentry's code review 
- [create-settings-command](https://doutorai.pt/en/components/skills__create-settings-command): Create plugin settings file with user preferences
- [create_bug_report](https://doutorai.pt/en/components/skills__create-bug-report): prompt_input "Console errors (paste if any):" CONSOLE_ERRORS false prompt_input "Frequency (Always/Sometimes/Rare):" FREQUENCY false prompt_input "How many user
- [create_gnn_template](https://doutorai.pt/en/components/skills__create-gnn-template): This script creates ready-to-use GNN model templates with training loops, evaluation metrics, and proper data handling.
- [create_handoff](https://doutorai.pt/en/components/skills__create-handoff): print(f"Created handoff document: {filepath}") print(f" Next steps:") print(f"1. Open {filepath}") print(f"2. Replace [TODO: ...] placeholders with actual conte
- [create_plugin](https://doutorai.pt/en/components/skills__create-plugin): See workflows.md for development guidelines.
- [create_validation_image](https://doutorai.pt/en/components/skills__create-validation-image): img = Image.open(input_path) draw = ImageDraw.Draw(img) num_boxes = 0 for field in data["form_fields"]: if field["page_number"] == page_number: entry_box = fiel
- [creative-portfolio-resume](https://doutorai.pt/en/components/skills__creative-portfolio-resume): Balance visual design with ATS compatibility for creative roles. Use when the user is in design, art, writing, marketing creative, or any visual/creative field 
- [crewai](https://doutorai.pt/en/components/skills__crewai): Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition,
- [crewai-multi-agent](https://doutorai.pt/en/components/skills__crewai-multi-agent): Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you n
- [crs-management](https://doutorai.pt/en/components/skills__crs-management): A coordinate reference system defines how coordinates relate to locations on Earth.
- [cryptography_implementation](https://doutorai.pt/en/components/skills__cryptography-implementation): This reference guide provides comprehensive information for senior security.
- [csharp-pro](https://doutorai.pt/en/components/skills__csharp-pro): Write modern C# code with advanced features like records, pattern matching, and async/await. Optimizes .NET applications, implements enterprise patterns, and en
- [css-extraction](https://doutorai.pt/en/components/skills__css-extraction): After scraping the raw HTML, extract the design tokens systematically. Work through these layers in order.
- [cto-advisor](https://doutorai.pt/en/components/skills__cto-advisor): Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Includes tech debt analyzer, team scaling calculator, engi
- [custom](https://doutorai.pt/en/components/skills__custom): % Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").
- [custom](https://doutorai.pt/en/components/skills__custom-2): Custom providers let you source components from anywhere - databases, APIs, configuration systems, or dynamic runtime logic. If you can write Python code to fet
- [custom-benchmarks](https://doutorai.pt/en/components/skills__custom-benchmarks): NeMo Evaluator supports adding custom benchmarks through Framework Definition Files (FDFs) and custom containers.
- [custom-models](https://doutorai.pt/en/components/skills__custom-models): Guide to implementing custom model architectures in LitGPT.
- [custom-models](https://doutorai.pt/en/components/skills__custom-models-2): This guide explains how to add a new model to TorchTitan following the established patterns.
- [custom-plugins](https://doutorai.pt/en/components/skills__custom-plugins): Accelerate allows creating custom plugins to extend distributed training strategies beyond built-in options (DDP, FSDP, DeepSpeed).
- [custom-rewards](https://doutorai.pt/en/components/skills__custom-rewards): Complete guide to implementing custom reward functions and agent RLHF in OpenRLHF.
- [custom-tasks](https://doutorai.pt/en/components/skills__custom-tasks): Guide to implementing custom evaluation tasks for code generation models.
- [custom-tasks](https://doutorai.pt/en/components/skills__custom-tasks-2): Complete guide to creating domain-specific evaluation tasks in lm-evaluation-harness.
- [custom_environments](https://doutorai.pt/en/components/skills__custom-environments): This guide provides comprehensive information for creating custom Gymnasium environments compatible with Stable Baselines3.
- [custom_functions](https://doutorai.pt/en/components/skills__custom-functions): Complete guide for creating custom functions in CocoIndex.
- [customization](https://doutorai.pt/en/components/skills__customization): Components reference semantic CSS variable tokens. Change the variables to change every component.
- [d3-patterns](https://doutorai.pt/en/components/skills__d3-patterns): This reference provides detailed code patterns for common d3.js visualisation types.
- [d3-viz](https://doutorai.pt/en/components/skills__d3-viz): Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations
- [daily-meeting-update](https://doutorai.pt/en/components/skills__daily-meeting-update): Interactive daily standup/meeting update generator. Use when user says 'daily', 'standup', 'scrum update', 'status update', 'what did I do yesterday', 'prepare 
- [darpa_guidelines](https://doutorai.pt/en/components/skills__darpa-guidelines): ✅ Contact PM early - Before writing, discuss your idea ✅ Attend Proposers Day - Essential for understanding program ✅ Form strong team - Complementary expertise
- [dashboard-agents](https://doutorai.pt/en/components/skills__dashboard-agents): PNG IHDR H I% IDATx I 7uE;?rq v R ;PK7 !MT}n]@7 B ! B !ƉD ! B ! =! B ! bĘH? \~o%B ! B !7 =>_~2yse݄̎K B ! B ! M~̔UbؙGMdD;C>C:e ?y ~ (/=R8ԪZW x ' \D] YYZH B ! BH>
- [dashboard-tasks](https://doutorai.pt/en/components/skills__dashboard-tasks): Z }Yr0{WZ}B# ]H%V g,ܲpZ ke ffʐ͏DEG ɵ2De'% T(SPϒVș̓u+ Qb߱TKw(N;777Cn?/d]Ν ,zJq_ - 5 ճx /;ܻˀYC~k 3/ j咸WSJqgʕ̏y_A g] UWo=H[8 ssyw'Yp Ʀ6<>akJ okn3Wǀ AaxM ӿl w pw 8u
- [dask](https://doutorai.pt/en/components/skills__dask): Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets a
- [data-access](https://doutorai.pt/en/components/skills__data-access-3): Specify region in API requests when applicable.
- [data-api](https://doutorai.pt/en/components/skills__data-api): Complete reference for PostgREST-style database queries using @neondatabase/neon-js.
- [data-batch-inserts](https://doutorai.pt/en/components/skills__data-batch-inserts): Individual INSERT statements have high overhead. Batch multiple rows in single statements or use COPY.
- [data-batch-inserts](https://doutorai.pt/en/components/skills__data-batch-inserts-2): Individual INSERT statements have high overhead. Batch multiple rows in single statements or use COPY.
- [data-engineer](https://doutorai.pt/en/components/skills__data-engineer): Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platf
- [data-feeds](https://doutorai.pt/en/components/skills__data-feeds): Extract structured data from 40+ websites including Amazon, LinkedIn, Instagram, TikTok, Facebook, YouTube, and more. Uses Bright Data's Web Data APIs with auto
- [data-fetching](https://doutorai.pt/en/components/skills__data-fetching): Modern data fetching using TanStack Query with Suspense boundaries, cache-first strategies, and centralized API services.
- [data-handling](https://doutorai.pt/en/components/skills__data-handling): scikit-survival primarily handles right-censored data.
- [data-management](https://doutorai.pt/en/components/skills__data-management): This document covers querying, searching, filtering, and streaming data in LaminDB, as well as best practices for organizing and accessing datasets.
- [data-management](https://doutorai.pt/en/components/skills__data-management-2): Represents a file in Latch's cloud storage system.
- [data-n-plus-one](https://doutorai.pt/en/components/skills__data-n-plus-one): N+1 queries execute one query per item in a loop. Batch them into a single query using arrays or JOINs.
- [data-n-plus-one](https://doutorai.pt/en/components/skills__data-n-plus-one-2): N+1 queries execute one query per item in a loop. Batch them into a single query using arrays or JOINs.
- [data-operations](https://doutorai.pt/en/components/skills__data-operations): DNAnexus provides comprehensive data management capabilities for files, records, databases, and other data objects. All data operations can be performed via the
- [data-over-abstractions](https://doutorai.pt/en/components/skills__data-over-abstractions): 100 functions on one data structure beats 10 functions on 10 structures. Generic data and operations enable composition.
- [data-pagination](https://doutorai.pt/en/components/skills__data-pagination): OFFSET-based pagination scans all skipped rows, getting slower on deeper pages. Cursor pagination is O(1).
- [data-pagination](https://doutorai.pt/en/components/skills__data-pagination-2): OFFSET-based pagination scans all skipped rows, getting slower on deeper pages. Cursor pagination is O(1).
- [data-scientist](https://doutorai.pt/en/components/skills__data-scientist): Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intel
- [data-structures](https://doutorai.pt/en/components/skills__data-structures): A GeoSeries is a vector where each entry is a set of shapes corresponding to one observation (similar to a pandas Series but with geometric data).
- [data-upsert](https://doutorai.pt/en/components/skills__data-upsert): Using separate SELECT-then-INSERT/UPDATE creates race conditions. Use INSERT ... ON CONFLICT for atomic upserts.
- [data-upsert](https://doutorai.pt/en/components/skills__data-upsert-2): Using separate SELECT-then-INSERT/UPDATE creates race conditions. Use INSERT ... ON CONFLICT for atomic upserts.
- [data_access](https://doutorai.pt/en/components/skills__data-access-4): This reference covers navigating OMERO's hierarchical data structure and retrieving objects.
- [data_analysis_patterns](https://doutorai.pt/en/components/skills__data-analysis-patterns): Templates and frameworks for conducting rigorous market analysis.
- [data_formats](https://doutorai.pt/en/components/skills__data-formats): ClinVar provides bulk data downloads in multiple formats to support different research workflows. Data is distributed via FTP and updated on regular schedules.
- [data_management](https://doutorai.pt/en/components/skills__data-management-3): PathML provides efficient data management solutions for handling large-scale pathology datasets through HDF5 storage, tile management strategies, and optimized 
- [data_modeling_patterns](https://doutorai.pt/en/components/skills__data-modeling-patterns): World-class data modeling patterns for senior data engineer.
- [data_module](https://doutorai.pt/en/components/skills__data-module): A LightningDataModule is a reusable, shareable class that encapsulates all data processing steps in PyTorch Lightning. It solves the problem of scattered data p
- [data_pipeline_architecture](https://doutorai.pt/en/components/skills__data-pipeline-architecture): World-class data pipeline architecture for senior data engineer.
- [data_presentation](https://doutorai.pt/en/components/skills__data-presentation): This reference provides comprehensive guidance for presenting clinical data in tables and figures following best practices and publication standards. Use these 
- [data_processing](https://doutorai.pt/en/components/skills__data-processing): This reference covers filtering, selections, virtual columns, expressions, aggregations, groupby operations, and data transformations in Vaex.
- [data_structure](https://doutorai.pt/en/components/skills__data-structure): The AnnData object stores a data matrix with associated annotations, providing a flexible framework for managing experimental data and metadata.
- [data_structures](https://doutorai.pt/en/components/skills__data-structures-2): PyOpenMS uses C++ objects with Python bindings. Understanding these core data structures is essential for effective data manipulation.
- [data_visualization_slides](https://doutorai.pt/en/components/skills__data-visualization-slides): Effective data visualization in presentations differs fundamentally from journal figures. While publications prioritize comprehensive detail, presentation slide
- [database](https://doutorai.pt/en/components/skills__database-2): Common database errors across PostgreSQL, MySQL, MongoDB, Redis, and SQLite.
- [database-architect](https://doutorai.pt/en/components/skills__database-architect): Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures.
- [database-design](https://doutorai.pt/en/components/skills__database-design): Database design principles and decision-making. Schema design, indexing strategy, ORM selection, serverless databases.
- [database-migration](https://doutorai.pt/en/components/skills__database-migration): Master database schema and data migrations across ORMs (Sequelize, TypeORM, Prisma), including rollback strategies and zero-downtime deployments.
- [database-optimizer](https://doutorai.pt/en/components/skills__database-optimizer): Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures.
- [database-patterns](https://doutorai.pt/en/components/skills__database-patterns): Complete guide to database access patterns using Prisma in backend microservices.
- [database-schema-designer](https://doutorai.pt/en/components/skills__database-schema-designer): Design robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint des
- [database_info](https://doutorai.pt/en/components/skills__database-info): Overview of databases queried by gget modules, including update frequencies and important considerations.
- [database_operations](https://doutorai.pt/en/components/skills__database-operations): Use search_templates({query: "database"}) to find more!
- [database_optimization_guide](https://doutorai.pt/en/components/skills__database-optimization-guide): This reference guide provides comprehensive information for senior backend.
- [database_strategies](https://doutorai.pt/en/components/skills__database-strategies): This document provides comprehensive guidance for searching multiple literature databases systematically and effectively.
- [databases](https://doutorai.pt/en/components/skills__databases): Bio.Entrez provides programmatic access to NCBI's Entrez databases, including PubMed, GenBank, Gene, Protein, Nucleotide, and many others. It handles all the co
- [datacommons-client](https://doutorai.pt/en/components/skills__datacommons-client): Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic da
- [datadog-cli](https://doutorai.pt/en/components/skills__datadog-cli): Datadog CLI for searching logs, querying metrics, tracing requests, and managing dashboards. Use this when debugging production issues or working with Datadog o
- [dataframes](https://doutorai.pt/en/components/skills__dataframes): Dask DataFrames enable parallel processing of large tabular data by distributing work across multiple pandas DataFrames. As described in the documentation, "Das
- [datamol](https://doutorai.pt/en/components/skills__datamol): Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descripto
- [dataops_best_practices](https://doutorai.pt/en/components/skills__dataops-best-practices): World-class dataops best practices for senior data engineer.
- [dataset-formats](https://doutorai.pt/en/components/skills__dataset-formats): Axolotl is a training framework that aims to make the process convenient yet flexible to users by simply passing a config yaml file.
- [datasets](https://doutorai.pt/en/components/skills__datasets-2): Complete guide to preference datasets for SimPO training.
- [datasets](https://doutorai.pt/en/components/skills__datasets-3): This document provides a comprehensive catalog of all available datasets in the Therapeutics Data Commons, organized by task category.
- [datasets](https://doutorai.pt/en/components/skills__datasets-4): Converts raw datasets into task-specific formatted samples.
- [datasets](https://doutorai.pt/en/components/skills__datasets-5): TorchDrug provides 40+ curated datasets across multiple domains: molecular property prediction, protein modeling, knowledge graph reasoning, and retrosynthesis.
- [datasets_benchmarking](https://doutorai.pt/en/components/skills__datasets-benchmarking): Aeon provides comprehensive tools for loading datasets and benchmarking time series algorithms.
- [datasets_reference](https://doutorai.pt/en/components/skills__datasets-reference): This document provides a comprehensive catalog of all datasets available in torch_geometric.datasets.
- [ddd-deliverables](https://doutorai.pt/en/components/skills__ddd-deliverables): Use this checklist to keep DDD adoption practical and measurable.
- [deadline-prep](https://doutorai.pt/en/components/skills__deadline-prep): Generate a structured demo outline from your session's change log and git history. Reads .claude/critical_log_changes.csv and git log to produce presentation-re
- [debugger](https://doutorai.pt/en/components/skills__debugger): Debugging specialist for errors, test failures, and unexpected
- [debugging-strategies](https://doutorai.pt/en/components/skills__debugging-strategies): Transform debugging from frustrating guesswork into systematic problem-solving with proven strategies, powerful tools, and methodical approaches.
- [deduplication](https://doutorai.pt/en/components/skills__deduplication): Complete guide to exact, fuzzy, and semantic deduplication.
- [deep-research](https://doutorai.pt/en/components/skills__deep-research): Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.
- [deep-research-notebooklm](https://doutorai.pt/en/components/skills__deep-research-notebooklm): Deep research skill powered by NotebookLM MCP. Conducts structured multi-source research (market analysis, competitive intel, trend analysis, prospect research)
- [deepchem](https://doutorai.pt/en/components/skills__deepchem): Molecular machine learning toolkit. Property prediction (ADMET, toxicity), GNNs (GCN, MPNN), MoleculeNet benchmarks, pretrained models, featurization, for drug 
- [deepspeed](https://doutorai.pt/en/components/skills__deepspeed): Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention
- [deeptools](https://doutorai.pt/en/components/skills__deeptools): NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualizati
- [defense-in-depth](https://doutorai.pt/en/components/skills__defense-in-depth): When you fix a bug caused by invalid data, adding validation at one place feels sufficient. But that single check can be bypassed by different code paths, refac
- [delivery-scripts](https://doutorai.pt/en/components/skills__delivery-scripts): Ready-to-use scripts, phrases, and techniques for navigating challenging moments.
- [denario](https://doutorai.pt/en/components/skills__denario): Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when gene
- [dependabot-review](https://doutorai.pt/en/components/skills__dependabot-review): Review and manage Dependabot PRs. Categorizes by risk, checks CI status, auto-merges safe updates, and reports issues. Use when the user says "review dependabot
- [dependency-injection](https://doutorai.pt/en/components/skills__dependency-injection): FastMCP uses dependency injection to provide runtime values to your tools, resources, and prompts. Instead of passing context through every layer of your code, 
- [dependency-updater](https://doutorai.pt/en/components/skills__dependency-updater): Smart dependency management for any language. Auto-detects project type, applies safe updates automatically, prompts for major versions, diagnoses and fixes dep
- [deploy](https://doutorai.pt/en/components/skills__deploy): echo "Deployment started. Waiting for build to complete..." >&2 echo "Preview URL: $PREVIEW_URL" >&2
- [deployment](https://doutorai.pt/en/components/skills__deployment): Complete guide to deploying SGLang in production environments.
- [deployment](https://doutorai.pt/en/components/skills__deployment-2): Infrastructure provisioning and deployment instructions for all supported platforms.
- [deployment](https://doutorai.pt/en/components/skills__deployment-3): Production deployment patterns for Pinecone.
- [deployment](https://doutorai.pt/en/components/skills__deployment-4): Complete guide to deploying MLflow models to production environments.
- [deployment-details](https://doutorai.pt/en/components/skills__deployment-details): Use this reference for service discovery, configuration patterns, quick commands, and common issues.
- [deployment-patterns](https://doutorai.pt/en/components/skills__deployment-patterns): Common deployment scenarios and best practices for the Netlify skill.
- [deployment-procedures](https://doutorai.pt/en/components/skills__deployment-procedures): Production deployment principles and decision-making. Safe deployment workflows, rollback strategies, and verification. Teaches thinking, not scripts.
- [deployment_strategies](https://doutorai.pt/en/components/skills__deployment-strategies): This reference guide provides comprehensive information for senior devops.
- [descriptors_reference](https://doutorai.pt/en/components/skills__descriptors-reference): Complete reference for molecular descriptors available in RDKit's Descriptors module.
- [descriptors_viz](https://doutorai.pt/en/components/skills__descriptors-viz): The descriptors module provides tools for computing molecular properties and descriptors.
- [desert-rose](https://doutorai.pt/en/components/skills__desert-rose): A soft and sophisticated theme with dusty, muted tones perfect for elegant presentations.
- [design-is-taking-apart](https://doutorai.pt/en/components/skills__design-is-taking-apart): Design is taking things apart, not adding features. Separate concerns, remove dependencies, compose simple pieces.
- [design-mirror](https://doutorai.pt/en/components/skills__design-mirror): Replicate the visual style of any website and apply it to your existing codebase. Use this skill whenever the user wants to match a site's design, mirror a UI a
- [design-system-checklist](https://doutorai.pt/en/components/skills__design-system-checklist): Use this comprehensive checklist when creating or auditing a design system.
- [design-system-starter](https://doutorai.pt/en/components/skills__design-system-starter): Create and evolve design systems with design tokens, component architecture, accessibility guidelines, and documentation templates. Ensures consistent, scalable
- [design-to-code](https://doutorai.pt/en/components/skills__design-to-code): Pixel-perfect Figma to React conversion using coderio. Generates production-ready code (TypeScript, Vite, TailwindCSS V4) with high visual fidelity. Features ro
- [deslop](https://doutorai.pt/en/components/skills__deslop): Remove AI-generated code slop from a branch. Use when cleaning up AI-generated code, removing unnecessary comments, defensive checks, or type casts. Checks diff
- [detect_resources](https://doutorai.pt/en/components/skills__detect-resources): Detects available compute resources including CPU, GPU, memory, and disk space. Outputs a JSON file that Claude Code can use to make informed decisions about co
- [developer-growth-analysis](https://doutorai.pt/en/components/skills__developer-growth-analysis): Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from
- [development_workflows](https://doutorai.pt/en/components/skills__development-workflows): This reference guide provides comprehensive information for senior fullstack.
- [devices](https://doutorai.pt/en/components/skills__devices): This reference covers all FDA medical device-related API endpoints accessible through openFDA.
- [devops-iac-engineer](https://doutorai.pt/en/components/skills__devops-iac-engineer): Implements infrastructure as code using Terraform, Kubernetes, and cloud platforms. Designs scalable architectures, CI/CD pipelines, and observability solutions
- [devtools](https://doutorai.pt/en/components/skills__devtools): Neon provides developer tools to enhance your local development workflow, including a VSCode extension and MCP server for AI-assisted development.
- [diagnostic_reports_standards](https://doutorai.pt/en/components/skills__diagnostic-reports-standards): The ACR provides comprehensive practice parameters for diagnostic imaging reporting to ensure quality, consistency, and communication effectiveness.
- [diagrammer](https://doutorai.pt/en/components/skills__diagrammer): Render clean blueprint-style SVG diagrams from JSON specs. Use when users ask to draw, sketch, or diagram a request flow, neural net, transformer block, system 
- [diffdock](https://doutorai.pt/en/components/skills__diffdock): Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. 
- [differential-expression](https://doutorai.pt/en/components/skills__differential-expression): This document provides detailed information about differential expression (DE) analysis using scvi-tools' probabilistic framework.
- [difficult-conversation-scripts](https://doutorai.pt/en/components/skills__difficult-conversation-scripts): Ready-to-use opening lines, responses to common reactions, and frameworks for navigating challenging workplace conversations.
- [difficult-workplace-conversations](https://doutorai.pt/en/components/skills__difficult-workplace-conversations): Structured approach to workplace conflicts, performance discussions, and challenging feedback using preparation-delivery-followup framework. Use when preparing 
- [dimensions](https://doutorai.pt/en/components/skills__dimensions): Resolution options (720p/1080p) and aspect ratios for HeyGen videos
- [direct-creation](https://doutorai.pt/en/components/skills__direct-creation): Use this reference for MCP direct-creation examples and follow-on configuration.
- [discord-bot-architect](https://doutorai.pt/en/components/skills__discord-bot-architect): Specialized skill for building production-ready Discord bots. Covers Discord.js (JavaScript) and Pycord (Python), gateway intents, slash commands, interactive c
- [discrete_choice](https://doutorai.pt/en/components/skills__discrete-choice): This document provides comprehensive guidance on discrete choice models in statsmodels, including binary, multinomial, count, and ordinal models.
- [discussions](https://doutorai.pt/en/components/skills__discussions): The Discussions API enables collaborative commenting on protocols. Comments can be added at both the protocol level and the individual step level, with support 
- [dispatching-parallel-agents](https://doutorai.pt/en/components/skills__dispatching-parallel-agents): Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
- [dispatching-parallel-agents](https://doutorai.pt/en/components/skills__dispatching-parallel-agents-2): Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
- [display-captions](https://doutorai.pt/en/components/skills__display-captions): Displaying captions in Remotion with TikTok-style pages and word highlighting
- [display-captions](https://doutorai.pt/en/components/skills__display-captions-2): Displaying captions in Remotion with TikTok-style pages and word highlighting
- [distances](https://doutorai.pt/en/components/skills__distances): Aeon provides specialized distance functions for measuring similarity between time series, compatible with both aeon and scikit-learn estimators.
- [distributed](https://doutorai.pt/en/components/skills__distributed): Lightning supports multiple distributed strategies with a single parameter change.
- [distributed-eval](https://doutorai.pt/en/components/skills__distributed-eval): Guide to running evaluation across multiple GPUs using data parallelism and tensor/pipeline parallelism.
- [distributed-llm-pretraining-torchtitan](https://doutorai.pt/en/components/skills__distributed-llm-pretraining-torchtitan): Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or cu
- [distributed-training](https://doutorai.pt/en/components/skills__distributed-training): Guide to FSDP (Fully Sharded Data Parallel) distributed training in LitGPT for scaling to multiple GPUs and nodes.
- [distributed_computing](https://doutorai.pt/en/components/skills__distributed-computing): Arboreto leverages Dask for parallelized computation, enabling efficient GRN inference from single-machine multi-core processing to multi-node cluster environme
- [distributed_training](https://doutorai.pt/en/components/skills__distributed-training-2): PyTorch Lightning provides several strategies for training large models efficiently across multiple GPUs, nodes, and machines. Choose the right strategy based o
- [distributions](https://doutorai.pt/en/components/skills__distributions): This reference provides a comprehensive catalog of probability distributions available in PyMC, organized by category. Use this to select appropriate distributi
- [django-pro](https://doutorai.pt/en/components/skills__django-pro): Master Django 5.x with async views, DRF, Celery, and Django Channels. Build scalable web applications with proper architecture, testing, and deployment.
- [django__django-10914](https://doutorai.pt/en/components/skills__django-django-10914): Based on my search, this repository does not contain the Django codebase. However, I can generate the patch based on my knowledge of the Django codebase structu
- [django__django-10914](https://doutorai.pt/en/components/skills__django-django-10914-2): I can see this patch file contains explanatory text before the actual patch content. The actual unified diff starts at line 15. Based on the issue requirements 
- [django__django-10924](https://doutorai.pt/en/components/skills__django-django-10924): Based on my knowledge of Django's codebase and the issue description, I can create the patch. The issue requests that FilePathField's path parameter should acce
- [django__django-10924](https://doutorai.pt/en/components/skills__django-django-10924-2): Based on my knowledge of Django's codebase and the issue description, I can create the patch. The issue is clear: FilePathField's path parameter should accept a
- [django__django-11001](https://doutorai.pt/en/components/skills__django-django-11001): Based on my knowledge of the Django codebase and the issue description, I can create the patch. The issue is in django/db/models/sql/compiler.py:
- [django__django-11001](https://doutorai.pt/en/components/skills__django-django-11001-2): Based on my knowledge of this Django issue and the existing patch file I found in the repository, let me analyze the problem and provide the correct fix.
- [django__django-11001](https://doutorai.pt/en/components/skills__django-django-11001-3): Based on the issue description and architect's analysis, I can generate the patch for the Django repository. The fix is to normalize multiline SQL by replacing 
- [django__django-11019](https://doutorai.pt/en/components/skills__django-django-11019): Based on the detailed issue description and the algorithm description provided by the reporter (Matt), I can construct the patch. The key insight from the issue
- [django__django-11019](https://doutorai.pt/en/components/skills__django-django-11019-2): Based on my analysis of the issue and the existing patches, I understand the problem and solution clearly. The Django issue is about the Media.merge method in d
- [django__django-11019](https://doutorai.pt/en/components/skills__django-django-11019-3): Based on the issue description and architect's analysis, I can generate the patch. The issue is in Django's django/forms/widgets.py file where the merge() metho
- [django__django-11039](https://doutorai.pt/en/components/skills__django-django-11039): Based on my knowledge of Django's codebase and the issue description, I can create the patch. The issue is in the sqlmigrate management command where self.outpu
- [django__django-11039](https://doutorai.pt/en/components/skills__django-django-11039-2): Based on my knowledge of the Django codebase and the issue description, I can provide the fix. The issue is in django/core/management/commands/sqlmigrate.py whe
- [django__django-11049](https://doutorai.pt/en/components/skills__django-django-11049): Based on my knowledge of the Django codebase, the DurationField error message is defined in django/forms/fields.py. The error message format string needs to be 
- [django__django-11049](https://doutorai.pt/en/components/skills__django-django-11049-2): Now I have the information I need. The correct patch is:
- [django__django-11099](https://doutorai.pt/en/components/skills__django-django-11099): This is a skill repository, not the Django repository. The issue is from Django's repository. Based on the issue description, I can generate the patch directly 
- [django__django-11099](https://doutorai.pt/en/components/skills__django-django-11099-2): Based on the issue description, I need to generate a patch for Django's contrib/auth/validators.py file. The issue is that the username validators use ^ and $ a
- [django__django-11133](https://doutorai.pt/en/components/skills__django-django-11133): Based on the issue description and hints provided, I can generate the patch. The issue is in the make_bytes method of HttpResponseBase class in django/http/resp
- [django__django-11133](https://doutorai.pt/en/components/skills__django-django-11133-2): Based on my knowledge of Django's codebase and the issue description, I can provide the fix. The issue is in django/http/response.py in the make_bytes method of
- [django__django-11179](https://doutorai.pt/en/components/skills__django-django-11179): Based on my knowledge of Django's deletion code and the issue description, I can provide the fix. The issue is that when delete() is called on a single model in
- [django__django-11179](https://doutorai.pt/en/components/skills__django-django-11179-2): Based on my knowledge of this specific Django issue, I can provide the fix. The issue is that in Django's deletion.py, when a model instance with no dependencie
- [django__django-11283](https://doutorai.pt/en/components/skills__django-django-11283): Based on the issue description and the hints provided, I can analyze the problem and generate the fix. The issue is in Django's django/contrib/auth/migrations/0
- [django__django-11283](https://doutorai.pt/en/components/skills__django-django-11283-2): I found the patch for django__django-11283. Now let me verify the correct format by understanding the original Django migration file structure. Based on the iss
- [django__django-11283](https://doutorai.pt/en/components/skills__django-django-11283-3): Based on the issue description and architect's analysis, I need to generate a patch for Django's auth.0011_update_proxy_permissions migration. Let me create the
- [django__django-11422](https://doutorai.pt/en/components/skills__django-django-11422): Based on my knowledge of Django and the issue description, I can construct the patch. The issue is in the iter_modules_and_files function in django/utils/autore
- [django__django-11422](https://doutorai.pt/en/components/skills__django-django-11422-2): Now I understand the issue completely. Let me create a clean patch based on my analysis. The issue is that manage.py runs as the __main__ module, and Python set
- [django__django-11564](https://doutorai.pt/en/components/skills__django-django-11564): Based on my knowledge of Django's codebase and the issue description, I'll create a patch that adds SCRIPT_NAME support to STATIC_URL and MEDIA_URL. The key cha
- [django__django-11564](https://doutorai.pt/en/components/skills__django-django-11564-2): Based on my understanding of Django's codebase and the issue description, I'll create a proper patch. The key requirements are:
- [django__django-11564](https://doutorai.pt/en/components/skills__django-django-11564-3): Based on my knowledge of the Django codebase and the issue description, I'll generate the patch. The fix involves modifying the url() methods in the storage cla
- [django__django-11583](https://doutorai.pt/en/components/skills__django-django-11583): Based on my knowledge of the Django issue and the stacktrace provided, I can create the fix. The issue is in django/utils/autoreload.py in the iter_modules_and_
- [django__django-11583](https://doutorai.pt/en/components/skills__django-django-11583-2): Based on the issue description and the stack trace, I understand the problem. The issue is in Django's django/utils/autoreload.py file in the iter_modules_and_f
- [django__django-11583](https://doutorai.pt/en/components/skills__django-django-11583-3): Based on the architect's analysis and the stacktrace from the issue, I can generate the patch. The issue is at line ~132 in django/utils/autoreload.py where pat
- [django__django-11620](https://doutorai.pt/en/components/skills__django-django-11620): Based on the issue description and the hints provided, I have sufficient information to generate the patch. The issue clearly states:
- [django__django-11620](https://doutorai.pt/en/components/skills__django-django-11620-2): Based on the issue description and hints, I can construct the fix. The issue states:
- [django__django-11620](https://doutorai.pt/en/components/skills__django-django-11620-3): Based on the architect's analysis, I can generate the patch for the Django issue. The fix is straightforward - change the exception handling in technical_404_re
- [django__django-11630](https://doutorai.pt/en/components/skills__django-django-11630): Now I have a good understanding of the fix. Let me provide a cleaner, more properly formatted patch for this Django issue:
- [django__django-11630](https://doutorai.pt/en/components/skills__django-django-11630-2): Based on the issue, I need to generate a patch for Django's model checks. The issue is about the E028 error for duplicate table names, which should be downgrade
- [django__django-11742](https://doutorai.pt/en/components/skills__django-django-11742): Based on my knowledge of Django's codebase structure, I'll create the patch. The issue is to add a check that ensures max_length is large enough to fit the long
- [django__django-11797](https://doutorai.pt/en/components/skills__django-django-11797): Based on the issue description and hints provided, I can generate the patch. The issue is in Django's django/db/models/lookups.py file. The problem is that the 
- [django__django-11797](https://doutorai.pt/en/components/skills__django-django-11797-2): I have found the existing patch. This is the correct fix for the Django issue. The patch modifies the Exact.process_rhs method in django/db/models/lookups.py to
- [django__django-11815](https://doutorai.pt/en/components/skills__django-django-11815): Based on the issue description and the hints provided, I can generate the patch directly. The issue is in django/db/migrations/serializer.py in the EnumSerializ
- [django__django-11815](https://doutorai.pt/en/components/skills__django-django-11815-2): Based on the issue description and hints, I have all the information needed to generate the patch. The issue is in django/db/migrations/serializer.py in the Enu
- [django__django-11815](https://doutorai.pt/en/components/skills__django-django-11815-3): Based on the architect's analysis, I need to generate a patch for Django's django/db/migrations/serializer.py file. The issue is in the EnumSerializer.serialize
- [django__django-11848](https://doutorai.pt/en/components/skills__django-django-11848): The patch file contains the fix. Let me extract just the patch content in the proper unified diff format:
- [django__django-11848](https://doutorai.pt/en/components/skills__django-django-11848-3): Based on the issue description and the architect's analysis, I can generate the patch. The issue is well-documented - the current Django code has hardcoded year
- [django__django-11905](https://doutorai.pt/en/components/skills__django-django-11905): Based on the issue and the hints provided, I can generate the correct patch. The issue requests adding validation to prevent using __isnull lookup with non-bool
- [django__django-11905](https://doutorai.pt/en/components/skills__django-django-11905-2): Based on my analysis of the issue and the hints provided, I can see that the fix requires adding validation to the IsNull class's as_sql method in django/db/mod
- [django__django-11910](https://doutorai.pt/en/components/skills__django-django-11910): Based on my knowledge of this Django issue and the hint provided, I can provide the fix. The issue is in Django's migration autodetector.py file. The regression
- [django__django-11910](https://doutorai.pt/en/components/skills__django-django-11910-2): Based on my research and understanding of the Django migration issue, I can now provide the fix. The issue is in Django's migration autodetector where when a pr
- [django__django-11910](https://doutorai.pt/en/components/skills__django-django-11910-3): Based on the issue description and the architect's analysis, I need to generate a patch for the Django migration autodetector. The bug is that when renaming a P
- [django__django-11964](https://doutorai.pt/en/components/skills__django-django-11964): I see this isn't the Django repository. Based on the issue description, I understand the problem well enough to create a fix. The issue is in Django's model fie
- [django__django-11964](https://doutorai.pt/en/components/skills__django-django-11964-2): The fix needs to ensure that when a value is assigned to a field, if it's an enum member, the actual value is extracted. The proper place for this fix is in the
- [django__django-11999](https://doutorai.pt/en/components/skills__django-django-11999): Based on the issue description, hints, and the patch file I found in the benchmarks directory, I can provide the correct patch. The fix is straightforward - we 
- [django__django-11999](https://doutorai.pt/en/components/skills__django-django-11999-2): This is not the Django repository. Based on the issue description and the hints provided, I have enough information to generate the fix. The issue is in Django'
- [django__django-12113](https://doutorai.pt/en/components/skills__django-django-12113): Based on my analysis of this Django issue, I understand the problem. The issue is in the SQLite backend's test_db_signature() method. When using TEST["NAME"] co
- [django__django-12113](https://doutorai.pt/en/components/skills__django-django-12113-2): Based on my knowledge of this Django issue, I can provide the patch. The problem is that when using TEST["NAME"] for SQLite databases, the test_db_signature() m
- [django__django-12125](https://doutorai.pt/en/components/skills__django-django-12125): Based on the issue description, the problem is in django/db/migrations/serializer.py where the TypeSerializer class uses __name__ instead of __qualname__. The f
- [django__django-12125](https://doutorai.pt/en/components/skills__django-django-12125-2): Based on my research, I understand the issue. The TypeSerializer class in Django's migration serializer uses __name__ instead of __qualname__, which causes inco
- [django__django-12184](https://doutorai.pt/en/components/skills__django-django-12184): This file contains analysis/notes, not an actual patch. Based on the analysis in this file and the original issue, I understand the problem. Let me now analyze 
- [django__django-12184](https://doutorai.pt/en/components/skills__django-django-12184-2): Based on my knowledge of the Django codebase and the issue described, I can provide the fix. The issue is in django/urls/resolvers.py.
- [django__django-12184](https://doutorai.pt/en/components/skills__django-django-12184-3): Based on my knowledge of Django's URL resolver implementation and the issue description, I can generate the patch. The problem is in Django's RegexPattern.match
- [django__django-12284](https://doutorai.pt/en/components/skills__django-django-12284): Based on my knowledge of Django's codebase and the issue description, I can provide the patch. The bug is in django/db/models/fields/__init__.py in the contribu
- [django__django-12284](https://doutorai.pt/en/components/skills__django-django-12284-2): The Django source is not in this repository. I need to generate the patch based on my understanding of the issue. Based on the detailed analysis:
- [django__django-12286](https://doutorai.pt/en/components/skills__django-django-12286): This is a Django repository issue but the current directory doesn't contain Django code. I need to understand the issue based on the problem description and cre
- [django__django-12286](https://doutorai.pt/en/components/skills__django-django-12286-2): I can see there's already a patch in the benchmarks results. This gives me a good understanding of the issue. The fix is in django/core/checks/translation.py. L
- [django__django-12286](https://doutorai.pt/en/components/skills__django-django-12286-3): Based on the architect's analysis and my knowledge of Django's codebase structure, I can generate the patch for the translation.E004 check. The issue is in the 
- [django__django-12308](https://doutorai.pt/en/components/skills__django-django-12308): The fix needs to be in django/contrib/admin/utils.py in the display_for_field function, which should check if the field is a JSONField and use its prepare_value
- [django__django-12308](https://doutorai.pt/en/components/skills__django-django-12308-2): Now I have two versions of the patch. The issue description says to use prepare_value method to handle the InvalidJSONInput case properly. Let me verify my unde
- [django__django-12453](https://doutorai.pt/en/components/skills__django-django-12453): Based on the issue description, I can generate the patch directly. The issue clearly describes:
- [django__django-12453](https://doutorai.pt/en/components/skills__django-django-12453-2): This is not the Django repository. This appears to be a different project. Based on the issue description, I need to generate a patch for Django's django/db/bac
- [django__django-12453](https://doutorai.pt/en/components/skills__django-django-12453-3): Based on the issue description and the architect's analysis, I need to generate a patch for the Django repository. The issue describes the exact location and fi
- [django__django-12470](https://doutorai.pt/en/components/skills__django-django-12470): Based on my knowledge of Django's codebase and this specific bug, I can identify the issue. The problem occurs in Django's SQL compiler when resolving ordering 
- [django__django-12470](https://doutorai.pt/en/components/skills__django-django-12470-2): I don't have direct access to the Django repository to explore the codebase. However, based on my knowledge of Django's ORM and this specific issue, I can provi
- [django__django-12470](https://doutorai.pt/en/components/skills__django-django-12470-3): This appears to be a benchmark repository, not the actual Django repository. Since this is a patch generation task for the django/django repository and I need t
- [django__django-12497](https://doutorai.pt/en/components/skills__django-django-12497): Based on the issue description, I need to create a patch that fixes the hint message in Django's code. The issue states: 1. There's a hint that incorrectly says
- [django__django-12497](https://doutorai.pt/en/components/skills__django-django-12497-2): Based on the issue description and the patch file I found, I can provide the correct fix. The issue is:
- [django__django-12589](https://doutorai.pt/en/components/skills__django-django-12589): Based on the problem description and the hints provided, I understand the issue. The problem is in Django's sql/query.py file in the set_group_by method. When t
- [django__django-12589](https://doutorai.pt/en/components/skills__django-django-12589-2): Based on the issue description and hints, I have enough context to generate the patch. The hint specifically says:
- [django__django-12589](https://doutorai.pt/en/components/skills__django-django-12589-3): Based on the architect's analysis and the issue description, I can generate the patch. The issue is in the set_group_by method in django/db/models/sql/query.py 
- [django__django-12700](https://doutorai.pt/en/components/skills__django-django-12700): This repository doesn't contain the Django codebase. Let me understand the issue better and provide a patch based on my knowledge of Django's code structure. Th
- [django__django-12700](https://doutorai.pt/en/components/skills__django-django-12700-2): Now I understand the issue. The cleanse_setting method in Django's django/views/debug.py only handles dictionary types but doesn't handle other iterables like l
- [django__django-12708](https://doutorai.pt/en/components/skills__django-django-12708): Based on my knowledge of the Django codebase and this specific issue, I can provide the fix. The issue is in django/db/backends/base/schema.py in the _delete_co
- [django__django-12708](https://doutorai.pt/en/components/skills__django-django-12708-2): Based on my research, I understand the issue. The problem is in django/db/backends/base/schema.py in the _delete_composed_index method. When both unique_togethe
- [django__django-12708](https://doutorai.pt/en/components/skills__django-django-12708-3): Based on the issue description and the architect's analysis, I need to generate a patch for the Django _delete_composed_index() method. The bug is that when del
- [django__django-12747](https://doutorai.pt/en/components/skills__django-django-12747): Based on my knowledge of Django's codebase and the issue described, I can provide the patch. The issue is in django/db/models/deletion.py in the Collector.delet
- [django__django-12747](https://doutorai.pt/en/components/skills__django-django-12747-2): Based on my knowledge of Django's codebase and the issue description, let me provide the patch. The issue is in django/db/models/deletion.py in the Collector.de
- [django__django-12856](https://doutorai.pt/en/components/skills__django-django-12856): Based on my knowledge of Django's codebase structure, I can generate the fix for this issue. The issue is about adding validation for UniqueConstraint fields, s
- [django__django-12856](https://doutorai.pt/en/components/skills__django-django-12856-2): Based on my knowledge of Django's codebase, I can create the patch. The issue is that when a model has a UniqueConstraint, the system doesn't validate that the 
- [django__django-12856](https://doutorai.pt/en/components/skills__django-django-12856-3): Based on the architect's analysis and my knowledge of Django's codebase, I'll generate the patch. The issue is that _check_constraints() doesn't validate that f
- [django__django-12908](https://doutorai.pt/en/components/skills__django-django-12908-2): I understand this is a Django issue that needs to be fixed. Since I don't have direct access to the Django repository, let me provide the patch based on my know
- [django__django-12908](https://doutorai.pt/en/components/skills__django-django-12908-3): Based on my knowledge of Django's codebase and the issue description, I can generate the appropriate patch. The distinct() method in Django's QuerySet class nee
- [django__django-12915](https://doutorai.pt/en/components/skills__django-django-12915): Based on the issue description and traceback, I can understand the problem well enough to create the patch. The issue is:
- [django__django-12915](https://doutorai.pt/en/components/skills__django-django-12915-2): Based on my analysis of the existing patches and the issue, I need to create a clean patch. The key insight is:
- [django__django-12983](https://doutorai.pt/en/components/skills__django-django-12983): Based on the issue description, I need to create a patch for Django's slugify function. The issue states that: - Current behavior: text.slugify("___This is a te
- [django__django-12983](https://doutorai.pt/en/components/skills__django-django-12983-2): Based on my research, I can now generate the correct patch for the Django slugify issue. The fix requires changing .strip() to .strip('-_') and updating the doc
- [django__django-13028](https://doutorai.pt/en/components/skills__django-django-13028): This appears to be a Django issue that needs to be applied to the Django repository. Based on the issue description and the provided hint, I can generate the pa
- [django__django-13028](https://doutorai.pt/en/components/skills__django-django-13028-2): Based on the issue description and the hint provided, I can generate the patch. The issue is that Django's check_filterable method incorrectly checks for a filt
- [django__django-13033](https://doutorai.pt/en/components/skills__django-django-13033): This doesn't seem to be a Django repository. The task asks to solve a GitHub issue from django/django repository. Let me check if this is a separate task that j
- [django__django-13033](https://doutorai.pt/en/components/skills__django-django-13033-2): This appears to be a task to generate a patch for the Django repository, but the Django repository is not present in the current working directory. Based on the
- [django__django-13033](https://doutorai.pt/en/components/skills__django-django-13033-3): Based on the architect's analysis, I can generate the patch. The issue is in django/db/models/sql/compiler.py around line 727 where the comparison getattr(field
- [django__django-13158](https://doutorai.pt/en/components/skills__django-django-13158): Based on the exploration and the previous patch attempt I found, I understand the issue. The problem is in Django's QuerySet.none() method - when called on a co
- [django__django-13158](https://doutorai.pt/en/components/skills__django-django-13158-2): Both patches confirm the same fix. The correct patch clears the combinator and combined_queries attributes before calling set_empty(). Based on my analysis, her
- [django__django-13220](https://doutorai.pt/en/components/skills__django-django-13220): Based on my knowledge of Django's ValidationError class and the issue requirements, I need to create a patch that adds __eq__ and __hash__ methods. Let me look 
- [django__django-13220](https://doutorai.pt/en/components/skills__django-django-13220-2): Based on my research and the existing patch, I have enough information to create the proper patch. The issue requests adding an __eq__ method to ValidationError
- [django__django-13220](https://doutorai.pt/en/components/skills__django-django-13220-3): This is a Django repository issue, but the Django codebase isn't in this directory. Based on my knowledge of Django's codebase, I'll generate the patch for the 
- [django__django-13230](https://doutorai.pt/en/components/skills__django-django-13230): Based on my knowledge of the Django syndication framework, I can create the patch. The issue is about adding a comments argument to the add_item() call in syndi
- [django__django-13265](https://doutorai.pt/en/components/skills__django-django-13265): I don't have direct access to Django's repository in the local filesystem. Based on my knowledge of Django's codebase and the issue description, I can generate 
- [django__django-13265](https://doutorai.pt/en/components/skills__django-django-13265-2): Based on my knowledge of Django's migration autodetector, I can generate the patch. The issue is that AlterOrderWithRespectTo creates the _order field, but AddI
- [django__django-13315](https://doutorai.pt/en/components/skills__django-django-13315): Based on my knowledge of Django and the issue description, I can construct the fix. The issue is that when limit_choices_to on a ForeignKey uses a Q object with
- [django__django-13321](https://doutorai.pt/en/components/skills__django-django-13321): This repository doesn't seem to contain the Django source code. This is a different project. The issue is about Django's session handling. Let me understand the
- [django__django-13321](https://doutorai.pt/en/components/skills__django-django-13321-2): Based on the architect's analysis and the traceback, I can generate the patch. The issue is in django/contrib/sessions/backends/base.py where the _legacy_decode
- [django__django-13401](https://doutorai.pt/en/components/skills__django-django-13401): Based on my knowledge of Django's source code and the issue description, I can create the patch. The Field class is in django/db/models/fields/__init__.py and t
- [django__django-13447](https://doutorai.pt/en/components/skills__django-django-13447): Based on my knowledge of Django's admin codebase (specifically Django 3.x/4.x era when this issue was likely filed), I'll generate the patch. The _build_app_dic
- [django__django-13448](https://doutorai.pt/en/components/skills__django-django-13448): The issue is in create_test_db() method. When MIGRATE is False, Django still tries to serialize the database, but the tables don't exist because migrations were
- [django__django-13448](https://doutorai.pt/en/components/skills__django-django-13448-2): Based on the issue description and the architect's analysis, I can generate the patch. The bug is that when TEST: {"MIGRATE": False} is set, serialize_db_to_str
- [django__django-13551](https://doutorai.pt/en/components/skills__django-django-13551): Based on the issue description and my knowledge of Django's codebase, I need to generate a patch for Django's PasswordResetTokenGenerator._make_hash_value() met
- [django__django-13590](https://doutorai.pt/en/components/skills__django-django-13590): Based on the issue description, I need to create a patch for the Django codebase. The issue is in django/db/models/sql/query.py in the resolve_lookup_value meth
- [django__django-13658](https://doutorai.pt/en/components/skills__django-django-13658): Based on the issue description, I can see this is about the Django framework's ManagementUtility class. The issue describes exactly what needs to be fixed - the
- [django__django-13658](https://doutorai.pt/en/components/skills__django-django-13658-2): I can see the format of an existing patch. Now I need to generate the correct patch. Based on the issue description, there's a CommandParser instantiation in Ma
- [django__django-13660](https://doutorai.pt/en/components/skills__django-django-13660): This issue is from the django/django repository, but we're in a different directory. Let me search for the Django source or understand this is about creating a 
- [django__django-13710](https://doutorai.pt/en/components/skills__django-django-13710): Based on my knowledge of Django's codebase, I can provide the patch for this issue. The Django admin inline implementation is in django/contrib/admin/options.py
- [django__django-13757](https://doutorai.pt/en/components/skills__django-django-13757): Based on my knowledge of Django's codebase and the issue description, I can provide the patch. The issue is in Django's django/db/models/fields/json.py file whe
- [django__django-13768](https://doutorai.pt/en/components/skills__django-django-13768): This is a Django issue, not related to the current repository. Based on the issue description, I need to create a patch for the Django Signal.send_robust() meth
- [django__django-13925](https://doutorai.pt/en/components/skills__django-django-13925): The bug was in the _check_default_pk method in Django's model options. When checking if a model needs a primary key warning (W042), the code wasn't properly che
- [django__django-13925](https://doutorai.pt/en/components/skills__django-django-13925-2): Based on the architect's analysis and my knowledge of the Django codebase, I can generate the patch for this issue. The _check_default_pk method in Django 3.2 c
- [django__django-13933](https://doutorai.pt/en/components/skills__django-django-13933): Based on the issue description, I need to create a patch for Django's ModelChoiceField class in django/forms/models.py. The issue is that ModelChoiceField doesn
- [django__django-13964](https://doutorai.pt/en/components/skills__django-django-13964): The Django repository is not in the current directory. This appears to be a different repository. Since the user is asking me to solve a Django issue, I need to
- [django__django-14016](https://doutorai.pt/en/components/skills__django-django-14016): Based on the issue description, I need to create a patch for Django's Q object implementation. The issue is that applying the | operator to a Q object fails whe
- [django__django-14017](https://doutorai.pt/en/components/skills__django-django-14017): This is not a Django repository. This issue is from the django/django repository, but I'm in a different directory. Let me understand the issue from the problem
- [django__django-14155](https://doutorai.pt/en/components/skills__django-django-14155): Based on my knowledge of Django's codebase and the issue description, I can generate the patch. The ResolverMatch class is in django/urls/resolvers.py. The issu
- [django__django-14155](https://doutorai.pt/en/components/skills__django-django-14155-2): Based on my knowledge of Django's codebase and the issue description (django__django-14155), I can generate the appropriate patch. The ResolverMatch class is in
- [django__django-14238](https://doutorai.pt/en/components/skills__django-django-14238): Based on the issue description and my knowledge of the Django codebase, I can generate the fix. The issue is that the AutoFieldMeta metaclass has a _subclasses 
- [django__django-14238](https://doutorai.pt/en/components/skills__django-django-14238-2): Based on the patch file I found, I can see this is indeed the Django issue about DEFAULT_AUTO_FIELD subclass checking. The fix is straightforward - change subcl
- [django__django-14382](https://doutorai.pt/en/components/skills__django-django-14382): This appears to be a separate repository. The task is asking to generate a patch for the django/django repository, not a file in this current directory. Based o
- [django__django-14411](https://doutorai.pt/en/components/skills__django-django-14411): Based on my knowledge of Django's codebase and this specific issue, I can provide the fix. The issue is that ReadOnlyPasswordHashWidget renders text (not an inp
- [django__django-14534](https://doutorai.pt/en/components/skills__django-django-14534): This codebase doesn't contain Django. Based on the GitHub issue, I need to generate a patch for the Django repository. The issue is clearly described - the Boun
- [django__django-14608](https://doutorai.pt/en/components/skills__django-django-14608): Based on my knowledge of Django's codebase and the issue description, I can create the patch. The issue is asking to:
- [django__django-14667](https://doutorai.pt/en/components/skills__django-django-14667): Based on my knowledge of Django and the issue description, I can construct the patch. The bug is in how defer() handles the case when it's chained after only().
- [django__django-14667](https://doutorai.pt/en/components/skills__django-django-14667-2): I found an existing patch for this issue. Let me verify this is correct by understanding the logic better and outputting the proper patch:
- [django__django-14672](https://doutorai.pt/en/components/skills__django-django-14672): Based on the issue description, I need to create a patch for Django's ManyToManyRel class. The issue states that through_fields can be a list, and when hashing 
- [django__django-14730](https://doutorai.pt/en/components/skills__django-django-14730): Based on my knowledge of Django's codebase and the issue description, I can create the patch. The issue is about adding a system check that raises an error when
- [django__django-14730](https://doutorai.pt/en/components/skills__django-django-14730-2): These patches are for a different Django issue (12497). The issue I need to solve is about preventing developers from defining related_name on symmetrical ManyT
- [django__django-14752](https://doutorai.pt/en/components/skills__django-django-14752): Based on the GitHub issue from django/django repository, I need to create a patch for the AutocompleteJsonView class. Let me create the patch based on the issue
- [django__django-14752](https://doutorai.pt/en/components/skills__django-django-14752-2): Based on the issue description and the architect's analysis, I need to generate a patch for Django's AutocompleteJsonView class. The issue provides the current 
- [django__django-14787](https://doutorai.pt/en/components/skills__django-django-14787): Now I have the exact patch. The fix is simple - wrap the partial object with wraps(method) to copy the wrapper assignments from the original method to the parti
- [django__django-14855](https://doutorai.pt/en/components/skills__django-django-14855): Based on the issue description and the hints provided, I can create the patch. The issue clearly states:
- [django__django-14855](https://doutorai.pt/en/components/skills__django-django-14855-2): Based on the issue description and the architect's analysis, I can generate the patch. The issue is clear: in get_admin_url() method, the reverse() call is miss
- [django__django-14915](https://doutorai.pt/en/components/skills__django-django-14915): Based on the issue description and hints provided, I can generate the patch. The ModelChoiceIteratorValue class is in django/forms/widgets.py and needs a __hash
- [django__django-14915](https://doutorai.pt/en/components/skills__django-django-14915-2): Based on the architect's analysis, I have enough information to generate the patch. The ModelChoiceIteratorValue class needs a __hash__ method that returns hash
- [django__django-14997](https://doutorai.pt/en/components/skills__django-django-14997): Based on the problem description and the hints provided, I can understand the issue and generate the fix. The problem is:
- [django__django-14999](https://doutorai.pt/en/components/skills__django-django-14999): Based on my knowledge of Django's codebase and this specific issue, I'll provide the patch. The issue is in django/db/migrations/operations/models.py in the Ren
- [django__django-15061](https://doutorai.pt/en/components/skills__django-django-15061): Based on my knowledge of Django's codebase, I can provide the patch for this issue. The MultiWidget class is in django/forms/widgets.py and has an id_for_label 
- [django__django-15202](https://doutorai.pt/en/components/skills__django-django-15202): Based on my knowledge of Django's codebase and the error traceback, I can generate the fix. The issue is in django/core/validators.py where the URLValidator.__c
- [django__django-15202](https://doutorai.pt/en/components/skills__django-django-15202-2): Based on the architect's analysis, I'll generate the patch for the Django URLValidator issue. The fix needs to catch ValueError from urlsplit() and convert it t
- [django__django-15213](https://doutorai.pt/en/components/skills__django-django-15213): The Django source code is not in this repository. Based on the GitHub issue description and my knowledge of Django's codebase, I can analyze this issue and prov
- [django__django-15252](https://doutorai.pt/en/components/skills__django-django-15252): This repository is not the Django codebase. I need to produce the patch based on my knowledge of Django's migration system. Based on the issue description and m
- [django__django-15320](https://doutorai.pt/en/components/skills__django-django-15320): Based on my knowledge of Django's codebase and the issue description, I can generate the fix. The issue is in Django's django/db/models/expressions.py file wher
- [django__django-15347](https://doutorai.pt/en/components/skills__django-django-15347): Based on the problem statement, I understand the issue clearly. The bug is in the Django Messages framework where MessageEncoder checks for truthiness of extra_
- [django__django-15388](https://doutorai.pt/en/components/skills__django-django-15388): The issue is in Django's template autoreload module. When BASE_DIR is added to TEMPLATES[0]['DIRS'], the entire project becomes a template directory. The templa
- [django__django-15388](https://doutorai.pt/en/components/skills__django-django-15388-2): Based on the architect's analysis and my knowledge of Django's autoreload functionality, I can generate the patch. The template_changed function in django/templ
- [django__django-15400](https://doutorai.pt/en/components/skills__django-django-15400): Based on the issue description, I have enough context to generate the patch. The issue is that SimpleLazyObject in Django's django/utils/functional.py doesn't i
- [django__django-15400](https://doutorai.pt/en/components/skills__django-django-15400-2): Based on the analysis provided and standard Django source code structure, I can generate the patch for the SimpleLazyObject.__radd__ issue. The fix adds the __r
- [django__django-15498](https://doutorai.pt/en/components/skills__django-django-15498): Based on my knowledge of Django and this issue, the problem is in django/views/static.py. The was_modified_since function should handle an empty string for the 
- [django__django-15695](https://doutorai.pt/en/components/skills__django-django-15695): Based on my knowledge of Django and the issue description, I can provide the fix. The issue is in the RenameIndex operation in Django's migrations. When databas
- [django__django-15695](https://doutorai.pt/en/components/skills__django-django-15695-2): Based on the issue description and architect's analysis, I can generate the patch. The issue is in the database_backwards() method of RenameIndex - when old_nam
- [django__django-15738](https://doutorai.pt/en/components/skills__django-django-15738): Based on my knowledge of the Django codebase and the issue description, I can provide the patch. The issue is in the generate_removed_fields method of django/db
- [django__django-15738](https://doutorai.pt/en/components/skills__django-django-15738-2): Based on my knowledge of Django's migration autodetector and the issue described, I can generate the patch. The problem is that when a ForeignKey field is chang
- [django__django-15781](https://doutorai.pt/en/components/skills__django-django-15781): The Django repository doesn't appear to be in the current directory. Based on the issue and hints provided, I can generate the patch directly since the change i
- [django__django-15789](https://doutorai.pt/en/components/skills__django-django-15789): Based on my knowledge of Django's codebase and the issue description, I can create the patch. The json_script function in Django's django/utils/html.py uses a h
- [django__django-15790](https://doutorai.pt/en/components/skills__django-django-15790): Based on my knowledge of Django and the issue described, I can provide the fix for this bug. The issue is in Django's template system checks where the check_for
- [django__django-15790](https://doutorai.pt/en/components/skills__django-django-15790-2): Based on the architect's analysis and my knowledge of Django's template checks system, I can generate the patch. The check_for_template_tags_with_the_same_name 
- [django__django-15814](https://doutorai.pt/en/components/skills__django-django-15814): This is a different project. Based on the issue description, I need to create a patch for the Django source code. The issue is in django/db/models/sql/query.py 
- [django__django-15819](https://doutorai.pt/en/components/skills__django-django-15819): Based on my knowledge of Django's inspectdb command, I can create the patch. The inspectdb command is located in django/core/management/commands/inspectdb.py. T
- [django__django-15819](https://doutorai.pt/en/components/skills__django-django-15819-2): Based on my knowledge of Django's inspectdb command and the issue description, I can generate the patch. The inspectdb.py file generates Django models from exis
- [django__django-15851](https://doutorai.pt/en/components/skills__django-django-15851): Based on my knowledge of the Django codebase and the issue description, I can provide the fix. The issue is in django/db/backends/postgresql/client.py where the
- [django__django-15902](https://doutorai.pt/en/components/skills__django-django-15902): This appears to be a task about creating a patch for the Django repository. Based on the issue description and hint provided, I need to create a patch that adds
- [django__django-15996](https://doutorai.pt/en/components/skills__django-django-15996): Now I understand. For a single flag, list(value) returns a list with one element. For combined flags, it returns multiple elements. For a zero value, it returns
- [django__django-16041](https://doutorai.pt/en/components/skills__django-django-16041): This is a Django issue that needs to be resolved in the Django codebase. Based on the problem statement and hints provided, I can generate the patch. The issue 
- [django__django-16046](https://doutorai.pt/en/components/skills__django-django-16046): Based on the issue description, I understand the problem. The Django numberformat.py file has code that checks if str_number[0] == "-" to detect negative number
- [django__django-16139](https://doutorai.pt/en/components/skills__django-django-16139): This appears to be a task asking me to generate a patch for Django's codebase based on the issue description. The issue is in the UserChangeForm class where the
- [django__django-16229](https://doutorai.pt/en/components/skills__django-django-16229): Based on my knowledge of Django's codebase, I can analyze and fix this issue. The problem is in django/forms/models.py in the apply_limit_choices_to_to_formfiel
- [django__django-16229](https://doutorai.pt/en/components/skills__django-django-16229-2): Based on my analysis of the issue and the architect's guidance, I need to create a proper patch for Django's django/forms/models.py. The issue is that when a Mo
- [django__django-16255](https://doutorai.pt/en/components/skills__django-django-16255): This is not the Django repository. The task is to generate a patch for the Django sitemaps issue. Based on the problem statement and the hint, I need to generat
- [django__django-16379](https://doutorai.pt/en/components/skills__django-django-16379): This is a different repository. The task is asking me to generate a patch for the Django repository's FileBasedCache.has_key method. I need to understand the is
- [django__django-16400](https://doutorai.pt/en/components/skills__django-django-16400): This is a Django issue but the current directory is not the Django repository. The task is asking me to generate a patch for the Django project. Let me create t
- [django__django-16400](https://doutorai.pt/en/components/skills__django-django-16400-2): Based on the architect's analysis and my knowledge of Django's codebase, I can generate the patch. The issue is in django/contrib/auth/management/__init__.py wh
- [django__django-16408](https://doutorai.pt/en/components/skills__django-django-16408): Based on the issue description and the hints provided, I can understand the problem and generate the appropriate patch. The issue is that when using multi-level
- [django__django-16527](https://doutorai.pt/en/components/skills__django-django-16527): This is not a Django repository. Based on the issue description, I need to generate a patch for the Django codebase. The issue is about the show_save_as_new con
- [django__django-16595](https://doutorai.pt/en/components/skills__django-django-16595): Based on my knowledge of Django's codebase and the issue description, I can provide the patch. The issue is in django/db/migrations/operations/fields.py where t
- [django__django-16595](https://doutorai.pt/en/components/skills__django-django-16595-2): Based on my knowledge of Django's migration optimizer and the issue description, I can generate the correct patch. The AlterField.reduce() method in Django's co
- [django__django-16816](https://doutorai.pt/en/components/skills__django-django-16816): Based on the issue description and the hints provided, I need to create a patch for Django's admin checks. The issue describes that E108 error doesn't catch rev
- [django__django-16816](https://doutorai.pt/en/components/skills__django-django-16816-2): Based on my analysis of the issue and the architect's guidance, I can generate the patch for this Django admin check E108 bug. The fix needs to be applied to dj
- [django__django-16820](https://doutorai.pt/en/components/skills__django-django-16820): Based on Django's migration optimizer pattern, the fix should add optimization rules to handle the transition from index_together to indexes. When squashing mig
- [django__django-16820](https://doutorai.pt/en/components/skills__django-django-16820-2): Based on my knowledge of Django's migration system architecture, I'll generate the patch. The key insight is that the migration optimizer needs to handle the ca
- [django__django-16873](https://doutorai.pt/en/components/skills__django-django-16873): This repository doesn't contain the Django source code. Based on my knowledge of Django's codebase and the issue description, I can provide the fix. The issue i
- [django__django-16873](https://doutorai.pt/en/components/skills__django-django-16873-2): Based on my knowledge of Django's codebase and the architect's analysis, I can generate the patch for the join filter in django/template/defaultfilters.py. The 
- [django__django-16910](https://doutorai.pt/en/components/skills__django-django-16910): Based on the architect's analysis and my knowledge of Django internals, I can generate the patch for this issue. The problem is in how _get_defer_select_mask() 
- [django__django-17051](https://doutorai.pt/en/components/skills__django-django-17051): Based on the issue description and hint, I have enough context to create the patch. The issue is clear:
- [django__django-17087](https://doutorai.pt/en/components/skills__django-django-17087): The Django codebase is not present in the current working directory. Based on the issue description and hints, I have all the information needed to generate the
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- [draw-io](https://doutorai.pt/en/components/skills__draw-io): draw.io diagram creation, editing, and review. Use for .drawio XML editing, PNG conversion, layout adjustment, and AWS icon usage.
- [drizzle-orm-expert](https://doutorai.pt/en/components/skills__drizzle-orm-expert): Expert in Drizzle ORM for TypeScript - schema design, relational queries, migrations, and serverless database integration. Use when building type-safe database 
- [drugbank-database](https://doutorai.pt/en/components/skills__drugbank-database): Access and analyze comprehensive drug information from the DrugBank database including drug properties, interactions, targets, pathways, chemical structures, an
- [drugbank_helper](https://doutorai.pt/en/components/skills__drugbank-helper): Args: root: Pre-loaded XML root element. If None, will load from drugbank-downloader """ self.root = root self._drug_cache = {}
- [drugs](https://doutorai.pt/en/components/skills__drugs): This reference covers all FDA drug-related API endpoints accessible through openFDA.
- [dspy](https://doutorai.pt/en/components/skills__dspy): Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framewor
- [dynamic-routing](https://doutorai.pt/en/components/skills__dynamic-routing): Configure complex routing in dashboard without code changes. Use route names instead of model names.
- [e2e-testing-patterns](https://doutorai.pt/en/components/skills__e2e-testing-patterns): Build reliable, fast, and maintainable end-to-end test suites that provide confidence to ship code quickly and catch regressions before users do.
- [easing](https://doutorai.pt/en/components/skills__easing): Provides various easing functions for natural motion and timing. All functions take a value t (0.0 to 1.0) and return eased value (0.0 to 1.0).
- [easing](https://doutorai.pt/en/components/skills__easing-2): Provides various easing functions for natural motion and timing. All functions take a value t (0.0 to 1.0) and return eased value (0.0 to 1.0).
- [ecg_cardiac](https://doutorai.pt/en/components/skills__ecg-cardiac): Process electrocardiogram (ECG) and photoplethysmography (PPG) signals for cardiovascular analysis. This module provides comprehensive tools for R-peak detectio
- [eda](https://doutorai.pt/en/components/skills__eda): Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR) or Skin Conductance (SC), measures the electrical conductance of the skin, reflecting s
- [editing](https://doutorai.pt/en/components/skills__editing): ⚠️ USE VARIED LAYOUTS - monotonous presentations are a common failure mode. Don't default to basic title + bullet slides. Actively seek out: - Multi-column layo
- [editing](https://doutorai.pt/en/components/skills__editing-2): ⚠️ USE VARIED LAYOUTS - monotonous presentations are a common failure mode. Don't default to basic title + bullet slides. Actively seek out: - Multi-column layo
- [eeg](https://doutorai.pt/en/components/skills__eeg): Analyze electroencephalography (EEG) signals for frequency band power, channel quality assessment, source localization, and microstate identification. NeuroKit2
- [effect_sizes_and_power](https://doutorai.pt/en/components/skills__effect-sizes-and-power): This document provides guidance on calculating, interpreting, and reporting effect sizes, as well as conducting power analyses for study planning.
- [effective_genome_sizes](https://doutorai.pt/en/components/skills__effective-genome-sizes): Effective genome size refers to the length of the "mappable" genome - regions that can be uniquely mapped by sequencing reads. This metric is crucial for proper
- [effort](https://doutorai.pt/en/components/skills__effort): Effort controls how eagerly Claude spends tokens. It affects all tokens: thinking, text responses, and function calls.
- [electron-desktop](https://doutorai.pt/en/components/skills__electron-desktop): Electron desktop app template principles. Cross-platform, React, TypeScript.
- [electron-development](https://doutorai.pt/en/components/skills__electron-development): Master Electron desktop app development with secure IPC, contextIsolation, preload scripts, multi-process architecture, electron-builder packaging, code signing
- [elixir-pro](https://doutorai.pt/en/components/skills__elixir-pro): Write idiomatic Elixir code with OTP patterns, supervision trees, and Phoenix LiveView. Masters concurrency, fault tolerance, and distributed systems.
- [email-sequence](https://doutorai.pt/en/components/skills__email-sequence): When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "e
- [email-systems](https://doutorai.pt/en/components/skills__email-systems): Email has the highest ROI of any marketing channel. $36 for every $1 spent. Yet most startups treat it as an afterthought - bulk blasts, no personalization, lan
- [email-templates](https://doutorai.pt/en/components/skills__email-templates): Ready-to-use email templates for common software development scenarios. Adapt the tone and detail level to your audience.
- [embeddings](https://doutorai.pt/en/components/skills__embeddings): Generate embeddings for text or multimodal content (images and videos) to perform semantic search, clustering, and other NLP tasks. Text and multimodal embeddin
- [emg](https://doutorai.pt/en/components/skills__emg): Electromyography (EMG) measures electrical activity produced by skeletal muscles during contraction. EMG analysis in NeuroKit2 focuses on amplitude estimation, 
- [emotional-regulation](https://doutorai.pt/en/components/skills__emotional-regulation): Techniques for managing your own emotions before, during, and after challenging workplace conversations.
- [ena-database](https://doutorai.pt/en/components/skills__ena-database): Access European Nucleotide Archive via API/FTP. Retrieve DNA/RNA sequences, raw reads (FASTQ), genome assemblies by accession, for genomics and bioinformatics p
- [endpoints](https://doutorai.pt/en/components/skills__endpoints): This section provides rules for managing compute endpoints associated with branches in a project. Compute endpoints are Neon compute instances that allow you to
- [ensembl-database](https://doutorai.pt/en/components/skills__ensembl-database): Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, f
- [ensembl_query](https://doutorai.pt/en/components/skills__ensembl-query): if response.status_code == 200: return response.json() elif response.status_code == 429: # Rate limited - wait and retry retry_after = int(response.headers.get(
- [ensemble-models](https://doutorai.pt/en/components/skills__ensemble-models): Random Survival Forests extend the random forest algorithm to survival analysis with censored data. They build multiple decision trees on bootstrap samples and 
- [entry_operations](https://doutorai.pt/en/components/skills__entry-operations): Utilities for creating entries, uploading attachments, and managing notebook content.
- [env_template](https://doutorai.pt/en/components/skills__env-template): This template provides a starting point for creating custom PufferEnv environments. Customize the observation space, action space, and environment logic for you
- [environment-config](https://doutorai.pt/en/components/skills__environment-config): The EnvironmentConfig object is used to configure services, volumes, and shared variables in Railway.
- [environment-setup-guide](https://doutorai.pt/en/components/skills__environment-setup-guide): Guide developers through setting up development environments with proper tools, dependencies, and configurations
- [environments](https://doutorai.pt/en/components/skills__environments): PufferLib provides the PufferEnv API for creating high-performance custom environments, and the Ocean suite containing 20+ pre-built environments. Environments 
- [enzyme_pathway_builder](https://doutorai.pt/en/components/skills__enzyme-pathway-builder): This module provides tools for constructing enzymatic pathways and retrosynthetic trees using BRENDA database information.
- [eog](https://doutorai.pt/en/components/skills__eog): Electrooculography (EOG) measures eye movements and blinks by detecting electrical potential differences generated by eye position changes. EOG is used in sleep
- [epochs_events](https://doutorai.pt/en/components/skills__epochs-events): Event-related analysis examines physiological responses time-locked to specific stimuli or events. NeuroKit2 provides tools for event detection, epoch creation,
- [erd-diagrams](https://doutorai.pt/en/components/skills__erd-diagrams): ERDs model database schemas, showing tables (entities), their columns (attributes), and relationships between tables. Essential for database design and document
- [error-codes](https://doutorai.pt/en/components/skills__error-codes): This file documents HTTP error codes returned by the Claude API, their common causes, and how to handle them. For language-specific error handling examples, see
- [error-patterns](https://doutorai.pt/en/components/skills__error-patterns-3): Use this to quickly map log signatures to likely causes and fixes.
- [esm](https://doutorai.pt/en/components/skills__esm): Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (effi
- [esm-c-api](https://doutorai.pt/en/components/skills__esm-c-api): ESM C (Cambrian) is a family of protein language models optimized for representation learning and efficient embedding generation. Designed as a drop-in replacem
- [esm3-api](https://doutorai.pt/en/components/skills__esm3-api): ESM3 is a frontier multimodal generative language model that reasons over the sequence, structure, and function of proteins. It uses iterative masked language m
- [etetoolkit](https://doutorai.pt/en/components/skills__etetoolkit): Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for p
- [evaluating-code-models](https://doutorai.pt/en/components/skills__evaluating-code-models): Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding 
- [evaluating-llms-harness](https://doutorai.pt/en/components/skills__evaluating-llms-harness): Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting 
- [evaluation](https://doutorai.pt/en/components/skills__evaluation): Complete guide to benchmarking and testing merged models based on research best practices.
- [evaluation](https://doutorai.pt/en/components/skills__evaluation-2): This document provides guidance on creating comprehensive evaluations for MCP servers. Evaluations test whether LLMs can effectively use your MCP server to answ
- [evaluation](https://doutorai.pt/en/components/skills__evaluation-3): This script evaluates MCP servers by running test questions against them using Claude.
- [evaluation-metrics](https://doutorai.pt/en/components/skills__evaluation-metrics): Evaluating survival models requires specialized metrics that account for censored data. scikit-survival provides three main categories of metrics: 1. Concordanc
- [evaluation_framework](https://doutorai.pt/en/components/skills__evaluation-framework): This document provides detailed evaluation criteria, rubrics, and quality indicators for each dimension of the ScholarEval framework. Use these standards when c
- [event-handlers](https://doutorai.pt/en/components/skills__event-handlers): Proper event typing ensures type-safe access to event properties and target elements.
- [event-sourcing-architect](https://doutorai.pt/en/components/skills__event-sourcing-architect): Expert in event sourcing, CQRS, and event-driven architecture patterns. Masters event store design, projection building, saga orchestration, and eventual consis
- [events](https://doutorai.pt/en/components/skills__events): This guide covers the event system in SimPy, which forms the foundation of discrete-event simulation.
- [evidence_synthesis](https://doutorai.pt/en/components/skills__evidence-synthesis): Evidence synthesis involves systematically reviewing, analyzing, and integrating research findings to inform clinical recommendations. This guide covers guideli
- [evidence_types](https://doutorai.pt/en/components/skills__evidence-types): Evidence represents any event or set of events that identifies a target as a potential causal gene or protein for a disease. Evidence is standardized and mapped
- [exa-search](https://doutorai.pt/en/components/skills__exa-search): Semantic search, similar content discovery, and structured research using Exa API
- [example](https://doutorai.pt/en/components/skills__example): This is a placeholder script that can be executed directly. Replace with actual implementation or delete if not needed.
- [example](https://doutorai.pt/en/components/skills__example-2): This is a placeholder script that can be executed directly. Replace with actual implementation or delete if not needed.
- [example](https://doutorai.pt/en/components/skills__example-3): This is a placeholder script that can be executed directly. Replace with actual implementation or delete if not needed.
- [example](https://doutorai.pt/en/components/skills__example-4): This is a placeholder script that can be executed directly. Replace with actual implementation or delete if not needed.
- [example](https://doutorai.pt/en/components/skills__example-5): This is a placeholder script that can be executed directly. Replace with actual implementation or delete if not needed.
- [example](https://doutorai.pt/en/components/skills__example-6): This is a placeholder script that can be executed directly. Replace with actual implementation or delete if not needed.
- [example](https://doutorai.pt/en/components/skills__example-7): This is a placeholder script that can be executed directly. Replace with actual implementation or delete if not needed.
- [example](https://doutorai.pt/en/components/skills__example-8): This is a placeholder script that can be executed directly. Replace with actual implementation or delete if not needed.
- [example](https://doutorai.pt/en/components/skills__example-9): This is a placeholder script that can be executed directly. Replace with actual implementation or delete if not needed.
- [example](https://doutorai.pt/en/components/skills__example-10): This is a placeholder script that can be executed directly. Replace with actual implementation or delete if not needed.
- [example](https://doutorai.pt/en/components/skills__example-11): This is a placeholder script that can be executed directly. Replace with actual implementation or delete if not needed.
- [example-settings](https://doutorai.pt/en/components/skills__example-settings): Changes require Claude Code restart - hooks can't be hot-swapped.
- [example_asset](https://doutorai.pt/en/components/skills__example-asset): This placeholder represents where asset files would be stored. Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
- [example_asset](https://doutorai.pt/en/components/skills__example-asset-2): This placeholder represents where asset files would be stored. Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
- [example_asset](https://doutorai.pt/en/components/skills__example-asset-3): This placeholder represents where asset files would be stored. Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
- [example_asset](https://doutorai.pt/en/components/skills__example-asset-4): This placeholder represents where asset files would be stored. Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
- [example_asset](https://doutorai.pt/en/components/skills__example-asset-5): This placeholder represents where asset files would be stored. Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
- [example_asset](https://doutorai.pt/en/components/skills__example-asset-6): This placeholder represents where asset files would be stored. Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
- [example_asset](https://doutorai.pt/en/components/skills__example-asset-7): This placeholder represents where asset files would be stored. Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
- [example_asset](https://doutorai.pt/en/components/skills__example-asset-8): This placeholder represents where asset files would be stored. Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
- [example_asset](https://doutorai.pt/en/components/skills__example-asset-9): This placeholder represents where asset files would be stored. Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
- [example_asset](https://doutorai.pt/en/components/skills__example-asset-10): This placeholder represents where asset files would be stored. Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
- [example_asset](https://doutorai.pt/en/components/skills__example-asset-11): This placeholder represents where asset files would be stored. Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
- [example_asset](https://doutorai.pt/en/components/skills__example-asset-12): This placeholder represents where asset files would be stored. Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
- [example_evaluation](https://doutorai.pt/en/components/skills__example-evaluation): Calculate the compound interest on $10,000 invested at 5% annual interest rate, compounded monthly for 3 years. What is the final amount in dollars (rounded to 
- [example_gbm_cohort](https://doutorai.pt/en/components/skills__example-gbm-cohort): This example demonstrates a patient cohort analysis stratified by molecular biomarkers, similar to the GBM Mesenchymal-Immune-Active cluster analysis provided a
- [example_notebookchat](https://doutorai.pt/en/components/skills__example-notebookchat): PNG IHDR y 0 s PLTE#&'777&+=fgh-01145367),-GJJ/23=?@+./gijBEEdff?AB;==FHIEGGDFGX[[]_@BBnpq;>?69:8:;9; Jw|zdi {qwB6_e% s> p ^ ̀hg8B1W;Ol4C^/ɄJF #I&'$KZ ]YY $xVB 
- [example_paper](https://doutorai.pt/en/components/skills__example-paper): % hyperref makes hyperlinks in the resulting PDF. % If your build breaks (sometimes temporarily if a hyperlink spans a page) % please comment out the following 
- [example_paper](https://doutorai.pt/en/components/skills__example-paper-2): @inproceedings{langley00, author = {P. Langley}, title = {Crafting Papers on Machine Learning}, year = {2000}, pages = {1207--1216}, editor = {Pat Langley}, boo
- [example_paper](https://doutorai.pt/en/components/skills__example-paper-3): %PDF-1.5 % 9 0 obj > stream xڥZ[ ~[M_hIڎux'M g'>9iSIԨivn߹4"] =ًRU.rn $jD zv 8o~^ y2vYwU[M; oiãpw ΃2 wIdJ RI;LL' S)T0,K RITJ RTaY,eJ RauX OT0:L R4a4aMJ ҄eiT >IJ 
- [example_queries](https://doutorai.pt/en/components/skills__example-queries): This script demonstrates common query patterns for the ChEMBL database using the chembl_webresource_client Python library.
- [example_usage](https://doutorai.pt/en/components/skills__example-usage-2): This document provides practical examples of using MarkItDown in various scenarios.
- [examples](https://doutorai.pt/en/components/skills__examples-4): This is why Manus can handle ~50 tool calls without losing track. The plan file acts as a "goal refresh" mechanism.
- [examples](https://doutorai.pt/en/components/skills__examples-5): This is why Manus can handle ~50 tool calls without losing track. The plan file acts as a "goal refresh" mechanism.
- [examples](https://doutorai.pt/en/components/skills__examples-6): Real-world examples of using Outlines for structured generation in production systems.
- [examples](https://doutorai.pt/en/components/skills__examples-7): Practical examples of using Instructor for structured data extraction.
- [examples](https://doutorai.pt/en/components/skills__examples-8): Practical examples of building production systems with DSPy.
- [examples](https://doutorai.pt/en/components/skills__examples-9): Real-world examples of using Guidance for structured generation, agents, and workflows.
- [examples](https://doutorai.pt/en/components/skills__examples-10): Real-world merge configurations from successful models on HuggingFace and research papers.
- [examples](https://doutorai.pt/en/components/skills__examples-12): This example demonstrates a full research pipeline from data to publication.
- [examples](https://doutorai.pt/en/components/skills__examples-13): This document provides practical examples for common data visualization scenarios using seaborn.
- [examples](https://doutorai.pt/en/components/skills__examples-14): This script demonstrates: 1. Automatic model selection based on query complexity 2. Manual model override options 3. Batch query processing 4. Integration with 
- [examples](https://doutorai.pt/en/components/skills__examples-16): This document provides practical examples for common molfeat use cases.
- [examples](https://doutorai.pt/en/components/skills__examples-17): This document provides practical examples of using the meme generation skill in various contexts.
- [examples](https://doutorai.pt/en/components/skills__examples-18): Use these examples when the user needs raw HTTP requests or is working in a language without an official SDK.
- [examples](https://doutorai.pt/en/components/skills__examples-20): This document provides complete, real-world examples of slash commands from the erk project. Use these as references when creating new commands.
- [excalidraw](https://doutorai.pt/en/components/skills__excalidraw): Use when working with *.excalidraw or *.excalidraw.json files, user mentions diagrams/flowcharts, or requests architecture visualization - delegates all Excalid
- [executing-marketing-campaigns](https://doutorai.pt/en/components/skills__executing-marketing-campaigns): Plans, creates, and optimizes marketing campaigns including content strategy, social media, email, and analytics. Helps develop go-to-market strategies, campaig
- [executing-plans](https://doutorai.pt/en/components/skills__executing-plans): Use when you have a written implementation plan to execute in a separate session with review checkpoints
- [executing-plans](https://doutorai.pt/en/components/skills__executing-plans-2): Use when you have a written implementation plan to execute in a separate session with review checkpoints
- [execution-backends](https://doutorai.pt/en/components/skills__execution-backends): NeMo Evaluator supports three execution backends: Local (Docker), Slurm (HPC), and Lepton (Cloud). Each backend implements the same interface but has different 
- [executive-resume-writer](https://doutorai.pt/en/components/skills__executive-resume-writer): Create C-suite and VP-level resumes emphasizing strategic leadership, P&L ownership, and board-level impact. Use when the user mentions executive, C-suite, CEO,
- [expectation-alignment](https://doutorai.pt/en/components/skills__expectation-alignment): Frameworks for setting, communicating, and managing expectations with teammates, stakeholders, and leadership.
- [expensive-to-add-later](https://doutorai.pt/en/components/skills__expensive-to-add-later): Some things you Probably Are Gonna Need - know when YAGNI doesn't apply because retrofitting is dramatically more expensive than building it in from the start.
- [experiment_design_frameworks](https://doutorai.pt/en/components/skills__experiment-design-frameworks): World-class experiment design frameworks for senior data scientist.
- [experimental_design_patterns](https://doutorai.pt/en/components/skills__experimental-design-patterns): This reference provides patterns and frameworks for designing experiments across scientific domains. Use these patterns to develop rigorous tests for generated 
- [experiments](https://doutorai.pt/en/components/skills__experiments): Adaptyv provides multiple experimental assay types for comprehensive protein characterization. Each experiment type has specific applications, workflows, and da
- [experiments](https://doutorai.pt/en/components/skills__experiments-2): This guide covers designing and executing quantum experiments, including parameter sweeps, data collection, and using the ReCirq framework.
- [explainers](https://doutorai.pt/en/components/skills__explainers): This document provides comprehensive information about all SHAP explainer classes, their parameters, methods, and when to use each type.
- [exploratory-data-analysis](https://doutorai.pt/en/components/skills__exploratory-data-analysis): Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data 
- [expo-deployment](https://doutorai.pt/en/components/skills__expo-deployment): Deploy Expo apps to production
- [export-interactivity](https://doutorai.pt/en/components/skills__export-interactivity): Kaleido v1+ requires Chrome/Chromium on your system.
- [express-api](https://doutorai.pt/en/components/skills__express-api): Express.js REST API template principles. TypeScript, Prisma, JWT.
- [extension_methods](https://doutorai.pt/en/components/skills__extension-methods): Comprehensive comparison of YaRN, ALiBi, and Position Interpolation based on published research.
- [extensions](https://doutorai.pt/en/components/skills__extensions): Guide for building UI extensions and Shopify Functions.
- [external-review](https://doutorai.pt/en/components/skills__external-review): This step sends claude-plan.md to external LLMs (Gemini and Codex) for independent review using CLI subagents.
- [extra_pkgs](https://doutorai.pt/en/components/skills__extra-pkgs): \lstset{ backgroundcolor=\color{white}, % choose the background color; you must add \usepackage{color} or \usepackage{xcolor}; should come as last argument basi
- [extract-frames](https://doutorai.pt/en/components/skills__extract-frames): Extract frames from videos at specific timestamps using Mediabunny
- [extract-frames](https://doutorai.pt/en/components/skills__extract-frames-2): Extract frames from videos at specific timestamps using Mediabunny
- [extract_form_field_info](https://doutorai.pt/en/components/skills__extract-form-field-info): for field_id, field in fields.items(): # Skip if this is a container field with children, except that it might be # a parent group for radio button options. if 
- [faiss](https://doutorai.pt/en/components/skills__faiss): Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Fla
- [fancyhdr](https://doutorai.pt/en/components/skills__fancyhdr-2): % Oct 7, 2004 % version 3.1 % Added '\endlinechar=13' to \fancy@reset to prevent problems with % includegraphics in header when verbatiminput is active.
- [fancyhdr](https://doutorai.pt/en/components/skills__fancyhdr-3): % Oct 7, 2004 % version 3.1 % Added '\endlinechar=13' to \fancy@reset to prevent problems with % includegraphics in header when verbatiminput is active.
- [faq-answers](https://doutorai.pt/en/components/skills__faq-answers): Some examples of areas that may be interesting to folks: recent corporate events (fundraising, new executives, etc.), upcoming launches, hiring progress, change
- [faq-answers](https://doutorai.pt/en/components/skills__faq-answers-2): Some examples of areas that may be interesting to folks: recent corporate events (fundraising, new executives, etc.), upcoming launches, hiring progress, change
- [fastapi-endpoint](https://doutorai.pt/en/components/skills__fastapi-endpoint): Plan and build production-ready FastAPI endpoints with async SQLAlchemy, Pydantic v2 models, dependency injection for auth, and pytest tests. Uses interview-dri
- [fastapi-pro](https://doutorai.pt/en/components/skills__fastapi-pro): Build high-performance async APIs with FastAPI, SQLAlchemy 2.0, and Pydantic V2. Master microservices, WebSockets, and modern Python async patterns.
- [fastmcp-server](https://doutorai.pt/en/components/skills__fastmcp-server): Complete guide for building MCP servers with FastMCP 3.0 - tools, resources, authentication, providers, middleware, and deployment. Use when creating Python MCP
- [fda-consultant-specialist](https://doutorai.pt/en/components/skills__fda-consultant-specialist): Senior FDA consultant and specialist for medical device companies including HIPAA compliance and requirement management. Provides FDA pathway expertise, QSR com
- [fda-database](https://doutorai.pt/en/components/skills__fda-database): Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data anal
- [fda_examples](https://doutorai.pt/en/components/skills__fda-examples): Demonstrates common use cases for querying FDA databases.
- [fda_query](https://doutorai.pt/en/components/skills__fda-query): Comprehensive utility for querying FDA databases through openFDA API. Includes error handling, rate limiting, caching, and common query patterns.
- [feature-design-assistant](https://doutorai.pt/en/components/skills__feature-design-assistant): Turn ideas into fully formed designs and specs through natural collaborative dialogue. Use when planning new features, designing architecture, or making signifi
- [feature_detection](https://doutorai.pt/en/components/skills__feature-detection): Feature detection identifies persistent signals (chromatographic peaks) in LC-MS data. Feature linking combines features across multiple samples for quantitativ
- [feature_engineering_patterns](https://doutorai.pt/en/components/skills__feature-engineering-patterns): World-class feature engineering patterns for senior data scientist.
- [features](https://doutorai.pt/en/components/skills__features): Overview of Neon's key platform features. For detailed information, fetch the official docs.
- [features](https://doutorai.pt/en/components/skills__features-2): Filter prompts/responses for inappropriate content. Actions: Flag (log) or Block (reject).
- [feedback-mastery](https://doutorai.pt/en/components/skills__feedback-mastery): Navigate difficult conversations and deliver constructive feedback using structured frameworks. Covers the Preparation-Delivery-Follow-up model and Situation-Be
- [feedback-sbi-model](https://doutorai.pt/en/components/skills__feedback-sbi-model): The Situation-Behavior-Impact (SBI) model, developed by the Center for Creative Leadership, provides a structured approach to giving clear, objective feedback t
- [fetch_gene_data](https://doutorai.pt/en/components/skills__fetch-gene-data): This script provides access to the NCBI Datasets API for retrieving comprehensive gene information including metadata and sequences.
- [few-shot-learning](https://doutorai.pt/en/components/skills__few-shot-learning): Few-shot learning enables LLMs to perform tasks by providing a small number of examples (typically 1-10) within the prompt. This technique is highly effective f
- [figma](https://doutorai.pt/en/components/skills__figma): Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when
- [figma](https://doutorai.pt/en/components/skills__figma-2): PNG IHDR d d pT pHYs sRGB gAMA a IDATx MlTU oZA;ҖJ(5 (эt!Qi a@ %1. ta5 P0& Ri t:WGt{ /yymfzӱm A F2 e !0Ba( Q #D F2 e !0Ba( QF.Qk2BpN!" E ?Ǡ BHֲ Z :B@ ߡ Kzp T ܨH
- [figma](https://doutorai.pt/en/components/skills__figma-3): PNG IHDR d d pT pHYs sRGB gAMA a IDATx MlTU oZA;ҖJ(5 (эt!Qi a@ %1. ta5 P0& Ri t:WGt{ /yymfzӱm A F2 e !0Ba( Q #D F2 e !0Ba( QF.Qk2BpN!" E ?Ǡ BHֲ Z :B@ ߡ Kzp T ܨH
- [figma-mcp-config](https://doutorai.pt/en/components/skills__figma-mcp-config): Use this snippet to register the Figma MCP server in ~/.codex/config.toml as a streamable HTTP server with bearer auth pulled from your env.
- [figma-tools-and-prompts](https://doutorai.pt/en/components/skills__figma-tools-and-prompts): Quick reference for the Figma MCP toolset, when to use each tool, and prompt examples to steer output toward your stack.
- [figma_validation](https://doutorai.pt/en/components/skills__figma-validation): Guide for validating UI implementation against Figma designs using Figma MCP.
- [figure_export](https://doutorai.pt/en/components/skills__figure-export): This module provides utilities to export matplotlib figures in publication-ready formats with appropriate settings for various journals.
- [figures_tables](https://doutorai.pt/en/components/skills__figures-tables): Figures and tables are essential components of scientific papers, serving to display data patterns, summarize results, and provide evidence for conclusions. Eff
- [file-organization](https://doutorai.pt/en/components/skills__file-organization): Proper file and directory structure for maintainable, scalable frontend code in the the application.
- [file-organizer](https://doutorai.pt/en/components/skills__file-organizer): Intelligently organizes files and folders by understanding context, finding duplicates, and suggesting better organizational structures. Use when user wants to 
- [file-uploads](https://doutorai.pt/en/components/skills__file-uploads): Expert at handling file uploads and cloud storage. Covers S3, Cloudflare R2, presigned URLs, multipart uploads, and image optimization. Knows how to handle larg
- [file_formats](https://doutorai.pt/en/components/skills__file-formats): This document provides detailed information about each file format supported by MarkItDown.
- [file_formats](https://doutorai.pt/en/components/skills__file-formats-2): The global roadmap. Updated on every MERGE and periodically on significant COMMITs.
- [file_io](https://doutorai.pt/en/components/skills__file-io): PyOpenMS supports multiple mass spectrometry file formats for reading and writing. This guide covers file handling strategies and format-specific operations.
- [file_manager](https://doutorai.pt/en/components/skills__file-manager): The File Manager API enables file operations within protocols.io workspaces, including uploading files, organizing folders, searching content, and managing file
- [files-api](https://doutorai.pt/en/components/skills__files-api): The Files API uploads files for use in Messages API requests. Reference files via file_id in content blocks, avoiding re-uploads across multiple API calls.
- [files-api](https://doutorai.pt/en/components/skills__files-api-2): The Files API uploads files for use in Messages API requests. Reference files via file_id in content blocks, avoiding re-uploads across multiple API calls.
- [filesystem](https://doutorai.pt/en/components/skills__filesystem): FileSystemProvider scans a directory for Python files and automatically registers functions decorated with @tool, @resource, or @prompt. This enables a file-bas
- [filter_molecules](https://doutorai.pt/en/components/skills__filter-molecules): This script provides a production-ready workflow for filtering compound libraries using medchem rules, structural alerts, and custom constraints.
- [filtering](https://doutorai.pt/en/components/skills__filtering): Complete guide to NeMo Curator's 30+ quality filters.
- [filtering](https://doutorai.pt/en/components/skills__filtering-2): This document provides a comprehensive reference of all filtering functions available in matchms for processing mass spectrometry data.
- [filters](https://doutorai.pt/en/components/skills__filters): Use filters to format variables: {{variable|filter}}.
- [filters_preprocessing](https://doutorai.pt/en/components/skills__filters-preprocessing): Histolab provides a comprehensive set of filters for preprocessing whole slide images and tiles. Filters can be applied to images for visualization, quality con
- [find-bugs](https://doutorai.pt/en/components/skills__find-bugs): Find bugs, security vulnerabilities, and code quality issues in local branch changes. Use when asked to review changes, find bugs, security review, or audit cod
- [fine-tuning-with-trl](https://doutorai.pt/en/components/skills__fine-tuning-with-trl): Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward mo
- [fine_tuning](https://doutorai.pt/en/components/skills__fine-tuning): Complete guide to fine-tuning transformer models for longer context windows.
- [finishing-a-development-branch](https://doutorai.pt/en/components/skills__finishing-a-development-branch): Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting stru
- [firebase](https://doutorai.pt/en/components/skills__firebase): Firebase gives you a complete backend in minutes - auth, database, storage, functions, hosting. But the ease of setup hides real complexity. Security rules are 
- [firebase-basics](https://doutorai.pt/en/components/skills__firebase-basics): Sets up and develops projects using Firebase products and services, especially for mobile or web apps. Covers Firebase CLI setup, project configuration, and int
- [firecrawl-scraper](https://doutorai.pt/en/components/skills__firecrawl-scraper): Deep web scraping, screenshots, PDF parsing, and website crawling using Firecrawl API
- [firewall-analysis](https://doutorai.pt/en/components/skills__firewall-analysis): Use firewall logs (compute.googleapis.com/firewall) to verify if traffic is allowed or denied.
- [fits](https://doutorai.pt/en/components/skills__fits): The astropy.io.fits module provides comprehensive tools for reading, writing, and manipulating FITS (Flexible Image Transport System) files.
- [float8](https://doutorai.pt/en/components/skills__float8): Float8 training provides substantial speedups for models where GEMMs are large enough that the FP8 tensorcore speedup outweighs dynamic quantization overhead.
- [flow_patterns](https://doutorai.pt/en/components/skills__flow-patterns): This reference provides common patterns and examples for building CocoIndex flows.
- [flowcharts](https://doutorai.pt/en/components/skills__flowcharts): Flowcharts visualize processes, algorithms, decision trees, and user journeys. They show step-by-step progression through a system or workflow.
- [flowio](https://doutorai.pt/en/components/skills__flowio): Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data pr
- [flows](https://doutorai.pt/en/components/skills__flows): Flows provide event-driven orchestration with precise control over execution paths, state management, and conditional branching. Use Flows when you need more co
- [fluidsim](https://doutorai.pt/en/components/skills__fluidsim): Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shal
- [flutter-app](https://doutorai.pt/en/components/skills__flutter-app): Flutter mobile app template principles. Riverpod, Go Router, clean architecture.
- [flutter-expert](https://doutorai.pt/en/components/skills__flutter-expert): Master Flutter development with Dart 3, advanced widgets, and multi-platform deployment.
- [fonts](https://doutorai.pt/en/components/skills__fonts): Loading Google Fonts and local fonts in Remotion
- [fonts](https://doutorai.pt/en/components/skills__fonts-2): Loading Google Fonts and local fonts in Remotion
- [foods](https://doutorai.pt/en/components/skills__foods): This reference covers FDA food-related API endpoints accessible through openFDA.
- [footballbin-predictions](https://doutorai.pt/en/components/skills__footballbin-predictions): Get AI-powered match predictions for Premier League and Champions League including scores, next goal, and corners.
- [forecasting](https://doutorai.pt/en/components/skills__forecasting): Aeon provides forecasting algorithms for predicting future time series values.
- [forest-canopy](https://doutorai.pt/en/components/skills__forest-canopy): A natural and grounded theme featuring earth tones inspired by dense forest environments.
- [forge-api](https://doutorai.pt/en/components/skills__forge-api): Forge is EvolutionaryScale's cloud platform for scalable protein design and inference. It provides API access to the full ESM3 model family, including large mod
- [form-cro](https://doutorai.pt/en/components/skills__form-cro): When the user wants to optimize any form that is NOT signup/registration - including lead capture forms, contact forms, demo request forms, application forms, s
- [format_adverse_events](https://doutorai.pt/en/components/skills__format-adverse-events): Converts CSV or structured data into formatted AE summary tables.
- [formatting](https://doutorai.pt/en/components/skills__formatting): The following instructions are for authors of papers submitted for review to ACL conferences (hereafter, "review version") or paper accepted for publication in 
- [forms](https://doutorai.pt/en/components/skills__forms-3): Use Field orientation="horizontal" for settings pages. Use FieldLabel className="sr-only" for visually hidden labels.
- [forms](https://doutorai.pt/en/components/skills__forms-4): If you need to fill out a PDF form, first check to see if the PDF has fillable form fields. Run this script from this file's directory: python scripts/check_fil
- [forms](https://doutorai.pt/en/components/skills__forms-5): If you need to fill out a PDF form, first check to see if the PDF has fillable form fields. Run this script from this file's directory: python scripts/check_fil
- [forms](https://doutorai.pt/en/components/skills__forms-6): If you need to fill out a PDF form, first check to see if the PDF has fillable form fields. Run this script from this file's directory: python scripts/check_fil
- [forms](https://doutorai.pt/en/components/skills__forms-7): If you need to fill out a PDF form, first check to see if the PDF has fillable form fields. Run this script from this file's directory: python scripts/check_fil
- [fragments_scaffolds](https://doutorai.pt/en/components/skills__fragments-scaffolds): Scaffolds represent the core structure of molecules, useful for identifying structural families and analyzing structure-activity relationships (SAR).
- [frame_composer](https://doutorai.pt/en/components/skills__frame-composer): Provides functions for drawing shapes, text, emojis, and compositing elements together to create animation frames.
- [frame_composer](https://doutorai.pt/en/components/skills__frame-composer-2): Provides functions for drawing shapes, text, emojis, and compositing elements together to create animation frames.
- [frameworks](https://doutorai.pt/en/components/skills__frameworks): Workers-native web framework with excellent TypeScript support and middleware ecosystem.
- [free-tool-strategy](https://doutorai.pt/en/components/skills__free-tool-strategy): When the user wants to plan, evaluate, or build a free tool for marketing purposes - lead generation, SEO value, or brand awareness. Also use when the user ment
- [frontend-design](https://doutorai.pt/en/components/skills__frontend-design): Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, 
- [frontend-dev-guidelines](https://doutorai.pt/en/components/skills__frontend-dev-guidelines): Frontend development guidelines for React/TypeScript applications. Modern patterns including Suspense, lazy loading, useSuspenseQuery, file organization with fe
- [frontend-patterns](https://doutorai.pt/en/components/skills__frontend-patterns): Frontend development patterns for React, Next.js, state management, performance optimization, and UI best practices.
- [frontend-to-backend-requirements](https://doutorai.pt/en/components/skills__frontend-to-backend-requirements): Document frontend data needs for backend developers. Use when frontend needs to communicate API requirements to backend, or user says 'backend requirements', 'w
- [frontend_best_practices](https://doutorai.pt/en/components/skills__frontend-best-practices): This reference guide provides comprehensive information for senior frontend.
- [frontmatter-reference](https://doutorai.pt/en/components/skills__frontmatter-reference): Complete reference for YAML frontmatter fields in slash commands.
- [fsdp](https://doutorai.pt/en/components/skills__fsdp): FSDP2 is a rewrite of PyTorch's Fully Sharded Data Parallel (FSDP) API, removing the FlatParameter abstraction for better composability and simpler implementati
- [full-oauth-server](https://doutorai.pt/en/components/skills__full-oauth-server): The Full OAuth Server pattern exists to support the MCP protocol specification's requirements. Your FastMCP server becomes both an Authorization Server and Reso
- [function_reference](https://doutorai.pt/en/components/skills__function-reference): This document provides a comprehensive reference for all major seaborn functions, organized by category.
- [futures](https://doutorai.pt/en/components/skills__futures): Dask futures extend Python's concurrent.futures interface, enabling immediate (non-lazy) task execution. Unlike delayed computations (used in DataFrames, Arrays
- [ga_client](https://doutorai.pt/en/components/skills__ga-client): Fetches analytics data from Google Analytics 4 using the Data API. Authentication uses service account credentials from environment variables.
- [game-art](https://doutorai.pt/en/components/skills__game-art): Game art principles. Visual style selection, asset pipeline, animation workflow.
- [game-audio](https://doutorai.pt/en/components/skills__game-audio): Game audio principles. Sound design, music integration, adaptive audio systems.
- [game-changing-features](https://doutorai.pt/en/components/skills__game-changing-features): Find 10x product opportunities and high-leverage improvements. Use when user wants strategic product thinking, mentions '10x', wants to find high-impact feature
- [game-design](https://doutorai.pt/en/components/skills__game-design): Game design principles. GDD structure, balancing, player psychology, progression.
- [game-development](https://doutorai.pt/en/components/skills__game-development): Game development orchestrator. Routes to platform-specific skills based on project needs.
- [gcc](https://doutorai.pt/en/components/skills__gcc): Git Context Controller (GCC) - Manages agent memory as a versioned file system under .GCC/. This skill should be used when working on multi-step projects that b
- [gcp-cloud-run](https://doutorai.pt/en/components/skills__gcp-cloud-run): Specialized skill for building production-ready serverless applications on GCP. Covers Cloud Run services (containerized), Cloud Run Functions (event-driven), c
- [gdpr-dsgvo-expert](https://doutorai.pt/en/components/skills__gdpr-dsgvo-expert): Senior GDPR/DSGVO expert and internal/external auditor for data protection compliance. Provides EU GDPR and German DSGVO expertise, privacy impact assessments, 
- [gemini](https://doutorai.pt/en/components/skills__gemini): Use when the user asks to run Gemini CLI for code review, plan review, or big context (>200k) processing. Ideal for comprehensive analysis requiring large conte
- [gemini-api-agent-platform](https://doutorai.pt/en/components/skills__gemini-api-agent-platform): Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK for enterprise AI applications. Covers SDK usage (Python, JS/TS, Go, Java, C#), 
- [gene-database](https://doutorai.pt/en/components/skills__gene-database): Query NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation a
- [general-comms](https://doutorai.pt/en/components/skills__general-comms): Before proceeding: 1. Ask the user about their target audience 2. Understand the communication's purpose 3. Clarify the desired tone (formal, casual, urgent, in
- [general-comms](https://doutorai.pt/en/components/skills__general-comms-2): Before proceeding: 1. Ask the user about their target audience 2. Understand the communication's purpose 3. Clarify the desired tone (formal, casual, urgent, in
- [general_medical_treatment_plan](https://doutorai.pt/en/components/skills__general-medical-treatment-plan): extbf{Anticipated Treatment Response}: With adherence, expect HbA1c reduction of 1-1.5\%, BP reduction of 10-15 mmHg, and 5-10\% weight loss over 6 months. Impr
- [general_scientific_formats](https://doutorai.pt/en/components/skills__general-scientific-formats): This reference covers general-purpose scientific data formats used across multiple disciplines.
- [generate-image](https://doutorai.pt/en/components/skills__generate-image): Generate or edit images using AI models (FLUX, Gemini). Use for general-purpose image generation including photos, illustrations, artwork, visual assets, concep
- [generate_image](https://doutorai.pt/en/components/skills__generate-image-2): For image editing, provide an input image along with an editing prompt.
- [generate_market_visuals](https://doutorai.pt/en/components/skills__generate-market-visuals): Batch generates visuals for a market research report using scientific-schematics and generate-image skills.
- [generate_report](https://doutorai.pt/en/components/skills__generate-report): Takes the JSON output from run_loop.py and generates a visual HTML report showing each description attempt with check/x for each test case. Distinguishes betwee
- [generate_report](https://doutorai.pt/en/components/skills__generate-report-3): Takes the JSON output from run_loop.py and generates a visual HTML report showing each description attempt with check/x for each test case. Distinguishes betwee
- [generate_report](https://doutorai.pt/en/components/skills__generate-report-4): Takes the JSON output from run_loop.py and generates a visual HTML report showing each description attempt with check/x for each test case. Distinguishes betwee
- [generate_report_template](https://doutorai.pt/en/components/skills__generate-report-template): Helps users select and generate appropriate clinical report templates.
- [generate_review](https://doutorai.pt/en/components/skills__generate-review): Reads the workspace directory, discovers runs (directories with outputs/), embeds all output data into a self-contained HTML page, and serves it via a tiny HTTP
- [generate_review](https://doutorai.pt/en/components/skills__generate-review-2): Reads the workspace directory, discovers runs (directories with outputs/), embeds all output data into a self-contained HTML page, and serves it via a tiny HTTP
- [generate_review](https://doutorai.pt/en/components/skills__generate-review-3): Reads the workspace directory, discovers runs (directories with outputs/), embeds all output data into a self-contained HTML page, and serves it via a tiny HTTP
- [generate_schematic](https://doutorai.pt/en/components/skills__generate-schematic): Generate any scientific diagram by describing it in natural language. Nano Banana Pro handles everything automatically with smart iterative refinement.
- [generate_schematic_ai](https://doutorai.pt/en/components/skills__generate-schematic-ai): This script uses a smart iterative refinement approach: 1. Generate initial image with Nano Banana Pro 2. AI quality review using Gemini 3 Pro for scientific cr
- [generate_slide_image](https://doutorai.pt/en/components/skills__generate-slide-image): Generate presentation slides or visuals by describing them in natural language. Nano Banana Pro handles everything automatically with smart iterative refinement
- [generate_slide_image_ai](https://doutorai.pt/en/components/skills__generate-slide-image-ai): Supports attaching reference images for context (e.g., "create a slide about this chart").
- [generate_test_cases](https://doutorai.pt/en/components/skills__generate-test-cases): prompt_input "What are you testing? (objective):" OBJECTIVE true prompt_input "Why is this test important?" WHY_IMPORTANT false
- [generation](https://doutorai.pt/en/components/skills__generation): Generate text with language models using the generate() method. Control output quality and style through generation strategies and parameters.
- [generator_template](https://doutorai.pt/en/components/skills__generator-template): / ═══════════════════════════════════════════════════════════════════════════ P5.JS GENERATIVE ART - BEST PRACTICES ════════════════════════════════════════════
- [generators](https://doutorai.pt/en/components/skills__generators): Generator functions are the core building block for describing animations in Motion Canvas. They use JavaScript's function syntax and yield to control animation
- [generic-components](https://doutorai.pt/en/components/skills__generic-components): Generic components provide type safety while maintaining reusability. TypeScript infers generic type parameters from prop values - no manual type annotations ne
- [geniml](https://doutorai.pt/en/components/skills__geniml): This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspa
- [geo-database](https://doutorai.pt/en/components/skills__geo-database): Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptom
- [geo-fundamentals](https://doutorai.pt/en/components/skills__geo-fundamentals): Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).
- [geo_reference](https://doutorai.pt/en/components/skills__geo-reference): The NCBI Entrez Programming Utilities (E-utilities) provide programmatic access to GEO metadata through a set of nine server-side programs. All E-utilities retu
- [geometric-operations](https://doutorai.pt/en/components/skills__geometric-operations): GeoPandas provides extensive geometric manipulation through Shapely integration.
- [geopandas](https://doutorai.pt/en/components/skills__geopandas): Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial an
- [gepetto](https://doutorai.pt/en/components/skills__gepetto): Creates detailed, sectionized implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough
- [get-audio-duration](https://doutorai.pt/en/components/skills__get-audio-duration): Getting the duration of an audio file in seconds with Mediabunny
- [get-audio-duration](https://doutorai.pt/en/components/skills__get-audio-duration-2): Getting the duration of an audio file in seconds with Mediabunny
- [get-available-resources](https://doutorai.pt/en/components/skills__get-available-resources): This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory
- [get-video-dimensions](https://doutorai.pt/en/components/skills__get-video-dimensions): Getting the width and height of a video file with Mediabunny
- [get-video-dimensions](https://doutorai.pt/en/components/skills__get-video-dimensions-2): Getting the width and height of a video file with Mediabunny
- [get-video-duration](https://doutorai.pt/en/components/skills__get-video-duration): Getting the duration of a video file in seconds with Mediabunny
- [get-video-duration](https://doutorai.pt/en/components/skills__get-video-duration-2): Getting the duration of a video file in seconds with Mediabunny
- [getting-started](https://doutorai.pt/en/components/skills__getting-started): Interactive guide to help users get started with Neon in their project. Sets up their Neon project (with a connection string) and connects their database to the
- [getting-started](https://doutorai.pt/en/components/skills__getting-started-2): Sign up for free at https://modal.com and get $30/month of credits.
- [getting_started](https://doutorai.pt/en/components/skills__getting-started-3): 保证当前 Linux 版本支持CUDA. 在命令行中输入 uname -m && cat /etc/release，应当看到类似的输出
- [getting_started](https://doutorai.pt/en/components/skills__getting-started-4): This guide provides end-to-end examples for common Data Commons workflows.
- [getting_started](https://doutorai.pt/en/components/skills__getting-started-5): PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. It enables training quantum computers like 
- [gget](https://doutorai.pt/en/components/skills__gget): CLI/Python toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl/UniProt), AlphaFold, ARCHS4
- [gguf-quantization](https://doutorai.pt/en/components/skills__gguf-quantization): GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible 
- [gh-address-comments](https://doutorai.pt/en/components/skills__gh-address-comments): Help address review/issue comments on the open GitHub PR for the current branch using gh CLI; verify gh auth first and prompt the user to authenticate if not lo
- [gh-fix-ci](https://doutorai.pt/en/components/skills__gh-fix-ci): Inspect GitHub PR checks with gh, pull failing GitHub Actions logs, summarize failure context, then create a fix plan and implement after user approval. Use whe
- [gif](https://doutorai.pt/en/components/skills__gif): Displaying GIFs, APNG, AVIF and WebP in Remotion
- [gif](https://doutorai.pt/en/components/skills__gif-2): Displaying GIFs, APNG, AVIF and WebP in Remotion
- [gif_builder](https://doutorai.pt/en/components/skills__gif-builder): This module provides the main interface for creating GIFs from programmatically generated frames, with automatic optimization for Slack's requirements.
- [gif_builder](https://doutorai.pt/en/components/skills__gif-builder-2): This module provides the main interface for creating GIFs from programmatically generated frames, with automatic optimization for Slack's requirements.
- [git](https://doutorai.pt/en/components/skills__git): Common Git errors with diagnosis and solutions.
- [git-pushing](https://doutorai.pt/en/components/skills__git-pushing): Stage, commit, and push git changes with conventional commit messages. Use when user wants to commit and push changes, mentions pushing to remote, or asks to sa
- [github-actions](https://doutorai.pt/en/components/skills__github-actions): steps: - name: Send notification run: | echo "Deployment failed! Sending notification..." # Add your notification logic here (Slack, email, etc.)
- [github-actions-creator](https://doutorai.pt/en/components/skills__github-actions-creator): Use when the user wants to create, generate, or set up a GitHub Actions workflow. Handles CI/CD pipelines, testing, deployment, linting, security scanning, rele
- [github-actions-templates](https://doutorai.pt/en/components/skills__github-actions-templates): Production-ready GitHub Actions workflow patterns for testing, building, and deploying applications.
- [github-automation](https://doutorai.pt/en/components/skills__github-automation): Automate GitHub repositories, issues, pull requests, branches, CI/CD, and permissions via Rube MCP (Composio). Manage code workflows, review PRs, search code, a
- [github-workflow-automation](https://doutorai.pt/en/components/skills__github-workflow-automation): Automate GitHub workflows with AI assistance. Includes PR reviews, issue triage, CI/CD integration, and Git operations. Use when automating GitHub workflows, se
- [github-workflow-automation](https://doutorai.pt/en/components/skills__github-workflow-automation-2): Automate GitHub workflows with AI assistance. Includes PR reviews, issue triage, CI/CD integration, and Git operations. Use when automating GitHub workflows, se
- [gitops-workflow](https://doutorai.pt/en/components/skills__gitops-workflow): Complete guide to implementing GitOps workflows with ArgoCD and Flux for automated Kubernetes deployments.
- [gke-app-onboarding](https://doutorai.pt/en/components/skills__gke-app-onboarding): This reference provides workflows for containerizing and deploying applications to GKE for the first time.
- [gke-backup-dr](https://doutorai.pt/en/components/skills__gke-backup-dr): This reference provides workflows for protecting stateful workloads on GKE using Backup for GKE.
- [gke-basics](https://doutorai.pt/en/components/skills__gke-basics): Plans, creates, and configures production-ready Google Kubernetes Engine (GKE) clusters using the golden path Autopilot configuration. Covers networking, securi
- [gke-batch-hpc](https://doutorai.pt/en/components/skills__gke-batch-hpc): This reference covers running batch processing and high-performance computing (HPC) workloads on GKE.
- [gke-cluster-creation](https://doutorai.pt/en/components/skills__gke-cluster-creation): This reference guides creating GKE clusters. The golden path Autopilot configuration is the default for all new clusters.
- [gke-compute-classes](https://doutorai.pt/en/components/skills__gke-compute-classes): ComputeClasses allow declarative node configuration and autoscaling priorities in GKE Autopilot (and Standard with NAP). Use them to specify machine families, S
- [gke-cost](https://doutorai.pt/en/components/skills__gke-cost): This reference covers strategies for reducing GKE costs while maintaining the golden path security and reliability posture.
- [gke-golden-path](https://doutorai.pt/en/components/skills__gke-golden-path): The golden path is the recommended Autopilot configuration for production clusters. It defines sensible defaults - when the user requests different settings, ap
- [gke-inference](https://doutorai.pt/en/components/skills__gke-inference): This reference covers deploying AI/ML inference workloads on GKE using Google's Inference Quickstart (GIQ) and best practices for LLM serving.
- [gke-multitenancy](https://doutorai.pt/en/components/skills__gke-multitenancy): This reference covers enterprise multi-tenancy patterns on GKE, including namespace isolation, RBAC planning, resource quotas, and network segmentation.
- [gke-networking](https://doutorai.pt/en/components/skills__gke-networking): This reference covers networking configuration for GKE clusters. The golden path enforces private, VPC-native clusters with Dataplane V2.
- [gke-observability](https://doutorai.pt/en/components/skills__gke-observability): This reference covers monitoring, logging, and metrics configuration for GKE. The golden path enables comprehensive observability including control-plane metric
- [gke-reliability](https://doutorai.pt/en/components/skills__gke-reliability): This reference covers high availability and reliability configuration for GKE clusters and workloads.
- [gke-scaling](https://doutorai.pt/en/components/skills__gke-scaling): This reference covers scaling workloads on GKE. The golden path enables VPA, OPTIMIZE_UTILIZATION autoscaling profile, and Node Auto Provisioning by default.
- [gke-security](https://doutorai.pt/en/components/skills__gke-security): This reference covers security configuration for GKE clusters. The golden path enforces a hardened security posture by default.
- [gke-storage](https://doutorai.pt/en/components/skills__gke-storage): This reference covers storage configuration for GKE clusters including persistent disks, file storage, and cloud storage integration.
- [gke-upgrades](https://doutorai.pt/en/components/skills__gke-upgrades): This reference covers upgrade strategy, maintenance windows, and release channel management for GKE clusters.
- [glm](https://doutorai.pt/en/components/skills__glm): This document provides comprehensive guidance on generalized linear models in statsmodels, including families, link functions, and applications.
- [go-api](https://doutorai.pt/en/components/skills__go-api): services: - type: web name: go-api runtime: go plan: free region: oregon branch: main autoDeploy: true buildCommand: go build -o bin/app -ldflags="-s -w" . star
- [goal_setting_frameworks](https://doutorai.pt/en/components/skills__goal-setting-frameworks): Effective treatment goals are the cornerstone of successful patient care. This reference provides comprehensive guidance on creating SMART goals, patient-center
- [golang-general-backend-security](https://doutorai.pt/en/components/skills__golang-general-backend-security): This document is designed as a security spec that supports: 1) Secure-by-default code generation for new Go code. 2) Security review / vulnerability hunting in 
- [golang-pro](https://doutorai.pt/en/components/skills__golang-pro): Master Go 1.21+ with modern patterns, advanced concurrency, performance optimization, and production-ready microservices.
- [golden-hour](https://doutorai.pt/en/components/skills__golden-hour): A rich and warm autumnal palette that creates an inviting and sophisticated atmosphere.
- [google-analytics](https://doutorai.pt/en/components/skills__google-analytics): Analyze Google Analytics data, review website performance metrics, identify traffic patterns, and suggest data-driven improvements. Use when the user asks about
- [google-cloud-auth](https://doutorai.pt/en/components/skills__google-cloud-auth): Provides expert guidance on authenticating and authorizing to Google Cloud services and APIs, covering human users, service identities, Application Default Cred
- [google-cloud-networking-observability](https://doutorai.pt/en/components/skills__google-cloud-networking-observability): Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs, NAT, firewall, or threat logs, qu
- [google-cloud-onboarding](https://doutorai.pt/en/components/skills__google-cloud-onboarding): Guides developers through their first steps on Google Cloud, covering account creation, billing setup, project management, CLI installation, and deploying a fir
- [google-cloud-setup](https://doutorai.pt/en/components/skills__google-cloud-setup): This guide provides an overview of the enterprise-level setup process for Google Cloud, focusing on creating a secure, scalable foundation.
- [google-cloud-waf-cost-optimization](https://doutorai.pt/en/components/skills__google-cloud-waf-cost-optimization): Generates cost optimization guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework (WAF). Use to evaluate a workload, identify
- [google-cloud-waf-reliability](https://doutorai.pt/en/components/skills__google-cloud-waf-reliability): Generates reliability-focused guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework. Use to evaluate a workload, identify rel
- [google-cloud-waf-security](https://doutorai.pt/en/components/skills__google-cloud-waf-security): Generates security-focused guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework (WAF). Use to evaluate a workload, identify 
- [google_scholar_search](https://doutorai.pt/en/components/skills__google-scholar-search): Comprehensive guide to searching Google Scholar for academic papers, including advanced search operators, filtering strategies, and metadata extraction.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-4): Writes can fail silently. Check tail logs.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-7): Issue resolves automatically after 48h. Contact Cloudflare Support if persists.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-8): Common issues, security considerations, and best practices.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-11): Consume message.raw immediately before any async operations.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-13): Common mistakes, security best practices, and troubleshooting for Cloudflare TURN.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-14): Common pitfalls, limitations, and solutions for TCP Sockets in Cloudflare Workers.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-15): Limitations, troubleshooting, and common pitfalls for R2 SQL.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-16): See README.md, configuration.md, api.md, patterns.md.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-17): Replies fail silently without DMARC. Verify: dig TXT _dmarc.example.com
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-23): See Cloudflare API Rate Limits for details.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-25): This is one of the most common and impactful Smart Placement misconfigurations.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-27): See configuration.md for config details, patterns.md for working examples, api.md for runtime APIs.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-29): Wrong platform? Recreate with correct --platform flag.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-31): Plan for API changes, test thoroughly before production.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-33): Cannot modify SQL after creation. Must delete and recreate.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-34): For local DOs in same Worker, script_name is optional.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-36): Navigation requests skip Worker invocation, reducing costs.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-42): Phases execute sequentially (can't be changed): 1. http_request_firewall_custom - Custom rules 2. http_request_firewall_managed - Managed rulesets 3. http_ratel
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-44): Users install WARP client instead of VPN.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-51): Use gatsby-browser.js to load client-side only.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-56): TURN automatically configured in SDK when enabled in account.
- [gotchas](https://doutorai.pt/en/components/skills__gotchas-59): See patterns.md for progressive rollout examples.
- [gptq](https://doutorai.pt/en/components/skills__gptq): Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduc
- [gpu](https://doutorai.pt/en/components/skills__gpu): See https://modal.com/pricing for pricing.
- [grader](https://doutorai.pt/en/components/skills__grader): Evaluate expectations against an execution transcript and outputs.
- [grader](https://doutorai.pt/en/components/skills__grader-2): Evaluate expectations against an execution transcript and outputs.
- [grader](https://doutorai.pt/en/components/skills__grader-3): Evaluate expectations against an execution transcript and outputs.
- [grafana-dashboards](https://doutorai.pt/en/components/skills__grafana-dashboards): Create and manage production-ready Grafana dashboards for comprehensive system observability.
- [grants_requirements](https://doutorai.pt/en/components/skills__grants-requirements): Comprehensive requirements and formatting guidelines for major federal and private foundation grant programs.
- [graph-objects](https://doutorai.pt/en/components/skills__graph-objects): The plotly.graph_objects module provides fine-grained control over figure construction through Python classes representing Plotly components.
- [graph-query](https://doutorai.pt/en/components/skills__graph-query): Query the code graph database to understand component relationships, dependencies, and change impact. Use when the user asks to "find callers", "check dependenc
- [graph_neural_network](https://doutorai.pt/en/components/skills__graph-neural-network): This script demonstrates training Graph Convolutional Networks (GCNs) and other graph-based models for molecular property prediction.
- [graphql](https://doutorai.pt/en/components/skills__graphql-2): GraphQL gives clients exactly the data they need - no more, no less. One endpoint, typed schema, introspection. But the flexibility that makes it powerful also 
- [graphql-architect](https://doutorai.pt/en/components/skills__graphql-architect): Master modern GraphQL with federation, performance optimization, and enterprise security. Build scalable schemas, implement advanced caching, and design real-ti
- [graphs](https://doutorai.pt/en/components/skills__graphs): PathML provides tools for constructing spatial graphs from tissue images to represent cellular and tissue-level relationships. Graph-based representations enabl
- [grpo-rl-training](https://doutorai.pt/en/components/skills__grpo-rl-training): Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training
- [gtars](https://doutorai.pt/en/components/skills__gtars): High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap 
- [guidance](https://doutorai.pt/en/components/skills__guidance): Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance -
- [guidelines](https://doutorai.pt/en/components/skills__guidelines): This document provides a comprehensive set of rules and guidelines for an AI agent to interact with the Neon API. The Neon API is a RESTful service that allows 
- [gwas-database](https://doutorai.pt/en/components/skills__gwas-database): Query NHGRI-EBI GWAS Catalog for SNP-trait associations. Search variants by rs ID, disease/trait, gene, retrieve p-values and summary statistics, for genetic ep
- [handoff-formats](https://doutorai.pt/en/components/skills__handoff-formats): This file is read in Phase 7, after the user locks in the blueprint. Its job: convert the wiki into something an executor (GSD, Superpowers, or raw Claude Code)
- [handoff-template](https://doutorai.pt/en/components/skills__handoff-template): Use this template structure when creating handoff documents. The smart scaffold script will pre-fill metadata sections; complete the remaining sections based on
- [hardware](https://doutorai.pt/en/components/skills__hardware): This guide covers running quantum circuits on real quantum hardware through Cirq's device interfaces and service providers.
- [hardware-backends](https://doutorai.pt/en/components/skills__hardware-backends): PyLabRobot uses a backend abstraction system that allows the same protocol code to run on different liquid handling robots and platforms. Backends handle device
- [haskell-pro](https://doutorai.pt/en/components/skills__haskell-pro): Expert Haskell engineer specializing in advanced type systems, pure
- [helm-chart-scaffolding](https://doutorai.pt/en/components/skills__helm-chart-scaffolding): Comprehensive guidance for creating, organizing, and managing Helm charts for packaging and deploying Kubernetes applications.
- [heygen-best-practices](https://doutorai.pt/en/components/skills__heygen-best-practices): Best practices for HeyGen - AI avatar video creation API
- [hierarchical_model_template](https://doutorai.pt/en/components/skills__hierarchical-model-template): This template provides a complete workflow for Bayesian hierarchical models, useful for grouped/nested data (e.g., students within schools, patients within hosp
- [histolab](https://doutorai.pt/en/components/skills__histolab): Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained t
- [hmdb-database](https://doutorai.pt/en/components/skills__hmdb-database): Access Human Metabolome Database (220K+ metabolites). Search by name/ID/structure, retrieve chemical properties, biomarker data, NMR/MS spectra, pathways, for m
- [hmdb_data_fields](https://doutorai.pt/en/components/skills__hmdb-data-fields): This document provides detailed information about the data fields available in HMDB metabolite entries.
- [hono](https://doutorai.pt/en/components/skills__hono): Build ultra-fast web APIs and full-stack apps with Hono - runs on Cloudflare Workers, Deno, Bun, Node.js, and any WinterCG-compatible runtime.
- [hooks](https://doutorai.pt/en/components/skills__hooks): Type-safe hook patterns for useState, useRef, useReducer, useContext, and custom hooks.
- [hqq-quantization](https://doutorai.pt/en/components/skills__hqq-quantization): Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast 
- [hrv](https://doutorai.pt/en/components/skills__hrv): Heart Rate Variability (HRV) reflects the variation in time intervals between consecutive heartbeats, providing insights into autonomic nervous system regulatio
- [html2pptx](https://doutorai.pt/en/components/skills__html2pptx): Convert HTML slides to PowerPoint presentations with accurate positioning using the html2pptx.js library.
- [html2pptx](https://doutorai.pt/en/components/skills__html2pptx-3): Convert HTML slides to PowerPoint presentations with accurate positioning using the html2pptx.js library.
- [http_api_integration](https://doutorai.pt/en/components/skills__http-api-integration): Use search_templates({query: "http api"}) to find more!
- [hubspot-integration](https://doutorai.pt/en/components/skills__hubspot-integration): Expert patterns for HubSpot CRM integration including OAuth authentication, CRM objects, associations, batch operations, webhooks, and custom objects. Covers No
- [huggingface-accelerate](https://doutorai.pt/en/components/skills__huggingface-accelerate): Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device place
- [huggingface-tokenizers](https://doutorai.pt/en/components/skills__huggingface-tokenizers): Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. 
- [hybrid-engine](https://doutorai.pt/en/components/skills__hybrid-engine): Complete guide to OpenRLHF's GPU resource sharing system for maximizing utilization during RLHF training.
- [hyperparameter-tuning](https://doutorai.pt/en/components/skills__hyperparameter-tuning): Lightning integrates seamlessly with popular hyperparameter tuning libraries.
- [hyperparameters](https://doutorai.pt/en/components/skills__hyperparameters): Complete guide to SimPO hyperparameter selection and tuning.
- [hypogenic](https://doutorai.pt/en/components/skills__hypogenic): Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literatur
- [hypothesis-generation](https://doutorai.pt/en/components/skills__hypothesis-generation): Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scie
- [hypothesis_quality_criteria](https://doutorai.pt/en/components/skills__hypothesis-quality-criteria): Use these criteria to assess the quality and rigor of generated hypotheses. A robust hypothesis should score well across multiple dimensions.
- [hypothesis_report_template](https://doutorai.pt/en/components/skills__hypothesis-report-template): \begin{summarybox}[Executive Summary] extbf{Phenomenon:} [One paragraph: What was observed? Why is it important?]
- [i18n-localization](https://doutorai.pt/en/components/skills__i18n-localization): Internationalization and localization patterns. Detecting hardcoded strings, managing translations, locale files, RTL support.
- [iac-usage](https://doutorai.pt/en/components/skills__iac-usage): AlloyDB resources can be managed using Terraform via the Google Cloud Provider, or via Kubernetes Config Connector (KCC).
- [iac-usage](https://doutorai.pt/en/components/skills__iac-usage-2): Cloud SQL resources can be provisioned and managed using Terraform and other IaC tools.
- [iac-usage](https://doutorai.pt/en/components/skills__iac-usage-3): Managing BigQuery resources using Infrastructure as Code (IaC) ensures consistency and repeatability across environments.
- [iac-usage](https://doutorai.pt/en/components/skills__iac-usage-4): Cloud Run resources can be provisioned and managed using Terraform and other IaC tools.
- [iac-usage](https://doutorai.pt/en/components/skills__iac-usage-5): GKE resources, including clusters and Kubernetes objects, can be provisioned and managed using Terraform.
- [iac-usage](https://doutorai.pt/en/components/skills__iac-usage-6): Firebase resources can be provisioned using Infrastructure as Code (IaC) tools, like Terraform.
- [iam-security](https://doutorai.pt/en/components/skills__iam-security): AlloyDB utilizes Google Cloud Identity and Access Management (IAM) to provide granular access control and robust security features.
- [iam-security](https://doutorai.pt/en/components/skills__iam-security-2): Cloud SQL uses Identity and Access Management (IAM) to control access to instances and databases.
- [iam-security](https://doutorai.pt/en/components/skills__iam-security-3): BigQuery uses Identity and Access Management (IAM) to provide granular access control to its resources. As a security best practice, follow the principle of lea
- [iam-security](https://doutorai.pt/en/components/skills__iam-security-4): Cloud Run uses Identity and Access Management (IAM) to secure your resources and control who can deploy or invoke them.
- [iam-security](https://doutorai.pt/en/components/skills__iam-security-5): Firebase Security Rules are CRITICAL to protecting Firestore data and Cloud Storage for Firebase data from unauthorized mobile and web client-side access. They 
- [iclr2026_conference](https://doutorai.pt/en/components/skills__iclr2026-conference): @article{Hinton06, author = {Hinton, Geoffrey E. and Osindero, Simon and Teh, Yee Whye}, journal = {Neural Computation}, pages = {1527--1554}, title = {A Fast L
- [iclr2026_conference](https://doutorai.pt/en/components/skills__iclr2026-conference-2): %PDF-1.5 % 78 0 obj > stream xڵYYF ~_ ;x Y83#/v HL(R!)O&~yhv fYW5, ԁW7W/Q,xy ɸA )F2삟(V?o Dsi|A YgK?YO ?;󒹟Kd?\zo\r?%O KR ~._Byo"\ ?ϥR&ϥs)\J.K oR~._ϥs\?ʯKKR~._ϥ\^
- [iclr2026_conference](https://doutorai.pt/en/components/skills__iclr2026-conference-3): % Optional math commands from https://github.com/goodfeli/dlbook_notation. \input{math_commands.tex}
- [iclr2026_conference](https://doutorai.pt/en/components/skills__iclr2026-conference-4): % This file can be used with Latex2e whether running in main mode, or % 2.09 compatibility mode. % % If using main mode, you need to include the commands % \doc
- [iclr2026_conference](https://doutorai.pt/en/components/skills__iclr2026-conference-5): %% File: iclr2024.bst' %% A copy of iclm2010.bst, which is a modification of plainnl.bst' for use with natbib package %% %% Copyright 2010 Hal Daum\'e III %% Mo
- [icml2026](https://doutorai.pt/en/components/skills__icml2026): % This file contains the LaTeX formatting parameters for a two-column % conference proceedings that is 8.5 inches wide by 11 inches high. % % Modified by Hanze 
- [icml2026](https://doutorai.pt/en/components/skills__icml2026-2): %% File: icml2025.bst' %% A modification of plainnl.bst' for use with natbib package %% %% Copyright 2010 Hal Daum\'e III %% Modified by J. Fürnkranz %% - Chang
- [icons](https://doutorai.pt/en/components/skills__icons): Add data-icon="inline-start" (prefix) or data-icon="inline-end" (suffix) to the icon. No sizing classes on the icon.
- [icons](https://doutorai.pt/en/components/skills__icons-3): Zafiro.Avalonia simplifies icon management using a specialized markup extension and styling options.
- [icons](https://doutorai.pt/en/components/skills__icons-4): Icons provide visual representations for your MCP servers and components, helping client applications present better user interfaces. When displayed in MCP clie
- [id_mapping_databases](https://doutorai.pt/en/components/skills__id-mapping-databases): Complete list of databases supported by the UniProt ID Mapping service. Use these database names when calling the ID mapping API.
- [identification](https://doutorai.pt/en/components/skills__identification): PyOpenMS supports peptide/protein identification through integration with search engines and provides tools for post-processing identification results including
- [identifier_mapping](https://doutorai.pt/en/components/skills__identifier-mapping): This document provides comprehensive information about converting identifiers between different biological databases using BioServices.
- [image-enhancer](https://doutorai.pt/en/components/skills__image-enhancer): Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documen
- [image-patterns](https://doutorai.pt/en/components/skills__image-patterns): Image placement and styling methods based on official Marpit image syntax.
- [image_gen](https://doutorai.pt/en/components/skills__image-gen): Defaults to gpt-image-1.5 and a structured prompt augmentation workflow.
- [image_loading](https://doutorai.pt/en/components/skills__image-loading): PathML provides comprehensive support for loading whole-slide images (WSI) from 160+ proprietary medical imaging formats. The framework abstracts vendor-specifi
- [image_processing](https://doutorai.pt/en/components/skills__image-processing): This reference covers accessing raw pixel data, image rendering, and creating new images in OMERO.
- [imagegen](https://doutorai.pt/en/components/skills__imagegen-2): PNG IHDR d d pT pHYs sRGB gAMA a DIDATx Ol G ؎ U4(i$ $BhoE! 7TD =B K.Z ©R z, ڐ 08q! c;k{fw=;fm|] 8=ɻXχpq \lKՅb, ɻH P )|D3o#&!$3 X 3Z GB>\β5{ :7"e ;}#næ 28Kǫ}T찐 
- [images](https://doutorai.pt/en/components/skills__images-2): Embedding images in Remotion using the component
- [images](https://doutorai.pt/en/components/skills__images-3): Modal Images define the environment code runs in - containers with dependencies installed. Images are built from method chains starting from a base image.
- [images](https://doutorai.pt/en/components/skills__images-4): Embedding images in Remotion using the component
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-2): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-3): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-4): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-5): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-6): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-8): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-9): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-10): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-11): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-12): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-13): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-14): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementation-playbook](https://doutorai.pt/en/components/skills__implementation-playbook-15): This file contains detailed patterns, checklists, and code samples referenced by the skill.
- [implementer-prompt](https://doutorai.pt/en/components/skills__implementer-prompt): Use this template when dispatching an implementer subagent.
- [implementer-prompt](https://doutorai.pt/en/components/skills__implementer-prompt-2): Use this template when dispatching an implementer subagent.
- [implementing-llms-litgpt](https://doutorai.pt/en/components/skills__implementing-llms-litgpt): Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implement
- [import-srt-captions](https://doutorai.pt/en/components/skills__import-srt-captions): Importing .srt subtitle files into Remotion using @remotion/captions
- [import-srt-captions](https://doutorai.pt/en/components/skills__import-srt-captions-2): Importing .srt subtitle files into Remotion using @remotion/captions
- [importing_exporting](https://doutorai.pt/en/components/skills__importing-exporting): This document details all file format support in matchms for loading and saving mass spectrometry data.
- [improve_description](https://doutorai.pt/en/components/skills__improve-description): Takes eval results (from run_eval.py) and generates an improved description by calling claude -p as a subprocess (same auth pattern as run_eval.py - uses the se
- [improve_description](https://doutorai.pt/en/components/skills__improve-description-2): Takes eval results (from run_eval.py) and generates an improved description by calling claude -p as a subprocess (same auth pattern as run_eval.py - uses the se
- [improve_description](https://doutorai.pt/en/components/skills__improve-description-3): Takes eval results (from run_eval.py) and generates an improved description by calling claude -p as a subprocess (same auth pattern as run_eval.py - uses the se
- [imrad_structure](https://doutorai.pt/en/components/skills__imrad-structure): IMRAD (Introduction, Methods, Results, And Discussion) is the predominant organizational structure for scientific journal articles of original research. Adopted
- [incident-management](https://doutorai.pt/en/components/skills__incident-management): Comprehensive guide to incident response, root cause analysis, post-mortems, and building resilient incident management processes.
- [incident-responder](https://doutorai.pt/en/components/skills__incident-responder): Expert SRE incident responder specializing in rapid problem resolution, modern observability, and comprehensive incident management.
- [index_types](https://doutorai.pt/en/components/skills__index-types): Complete guide to choosing and using FAISS index types.
- [inference](https://doutorai.pt/en/components/skills__inference): Complete guide to optimizing MoE inference based on MoE-Inference-Bench research (arXiv 2508.17467, 2024).
- [information-security-manager-iso27001](https://doutorai.pt/en/components/skills__information-security-manager-iso27001): Senior Information Security Manager specializing in ISO 27001 and ISO 27002 implementation for HealthTech and MedTech companies. Provides ISMS implementation, c
- [infrastructure](https://doutorai.pt/en/components/skills__infrastructure): Comprehensive guide to server management, network operations, capacity planning, and infrastructure operations for IT teams.
- [infrastructure_as_code](https://doutorai.pt/en/components/skills__infrastructure-as-code): This reference guide provides comprehensive information for senior devops.
- [init-artifact](https://doutorai.pt/en/components/skills__init-artifact): echo "🧹 Cleaning up Vite template..." $SED_INPLACE '/ . / '"$PROJECT_NAME"' /' index.html
- [init-artifact](https://doutorai.pt/en/components/skills__init-artifact-2): echo "🧹 Cleaning up Vite template..." $SED_INPLACE '/ . / '"$PROJECT_NAME"' /' index.html
- [init-artifact](https://doutorai.pt/en/components/skills__init-artifact-3): echo "🧹 Cleaning up Vite template..." $SED_INPLACE '/ . / '"$PROJECT_NAME"' /' index.html
- [init_skill](https://doutorai.pt/en/components/skills__init-skill): SKILL_TEMPLATE = """--- name: {skill_name} description: [TODO: Complete and informative explanation of what the skill does and when to use it. Include WHEN to u
- [init_skill](https://doutorai.pt/en/components/skills__init-skill-2): SKILL_TEMPLATE = """--- name: {skill_name} description: [TODO: Complete and informative explanation of what the skill does and when to use it. Include WHEN to u
- [inngest](https://doutorai.pt/en/components/skills__inngest): Inngest expert for serverless-first background jobs, event-driven workflows, and durable execution without managing queues or workers. Use when: inngest, server
- [installation](https://doutorai.pt/en/components/skills__installation-2): Access the GUI at http://localhost:8501 after the container starts.
- [installation](https://doutorai.pt/en/components/skills__installation-3): This installs fluidfft and pyfftw dependencies.
- [installation](https://doutorai.pt/en/components/skills__installation-4): If successful, you should see the help menu with all available options.
- [installation](https://doutorai.pt/en/components/skills__installation-5): We recommend using uv to install and manage FastMCP.
- [instructor](https://doutorai.pt/en/components/skills__instructor): Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream par
- [integration](https://doutorai.pt/en/components/skills__integration): Integration with vector stores, LangSmith observability, and deployment.
- [integration](https://doutorai.pt/en/components/skills__integration-2): Integration with Ray Train and ML frameworks.
- [integration](https://doutorai.pt/en/components/skills__integration-3): Integration with LangChain, LlamaIndex, and frameworks.
- [integration](https://doutorai.pt/en/components/skills__integration-4): Complete guide to using HuggingFace Tokenizers with the Transformers library.
- [integration](https://doutorai.pt/en/components/skills__integration-5): Integration with transformers, PEFT, vLLM, and other frameworks.
- [integration](https://doutorai.pt/en/components/skills__integration-6): PufferLib provides an emulation layer that enables seamless integration with popular RL frameworks including Gymnasium, OpenAI Gym, PettingZoo, and many special
- [integrations](https://doutorai.pt/en/components/skills__integrations): Complete guide to integrating W&B with popular ML frameworks.
- [integrations](https://doutorai.pt/en/components/skills__integrations-2): Complete guide to integrating TensorBoard with popular ML frameworks.
- [integrations](https://doutorai.pt/en/components/skills__integrations-3): This document covers LaminDB integrations with workflow managers, MLOps platforms, visualization tools, and other third-party systems.
- [integrations](https://doutorai.pt/en/components/skills__integrations-4): LabArchives integrates with numerous scientific software platforms to streamline research workflows. This document covers programmatic integration approaches, a
- [interactive-commands](https://doutorai.pt/en/components/skills__interactive-commands): Comprehensive guide to creating commands that gather user feedback and make decisions through the AskUserQuestion tool.
- [interactive-portfolio](https://doutorai.pt/en/components/skills__interactive-portfolio): Expert in building portfolios that actually land jobs and clients - not just showing work, but creating memorable experiences. Covers developer portfolios, desi
- [internal](https://doutorai.pt/en/components/skills__internal): Use this template for team codebases, services, and internal tools. Focus on onboarding new team members and operational knowledge.
- [internal-comms](https://doutorai.pt/en/components/skills__internal-comms): A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever 
- [internal-comms](https://doutorai.pt/en/components/skills__internal-comms-2): A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever 
- [internal-comms](https://doutorai.pt/en/components/skills__internal-comms-3): A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever 
- [intervention_guidelines](https://doutorai.pt/en/components/skills__intervention-guidelines): This reference provides comprehensive guidance on selecting, implementing, and documenting evidence-based interventions across pharmacological, non-pharmacologi
- [interview-prep-generator](https://doutorai.pt/en/components/skills__interview-prep-generator): Generate STAR stories, practice questions, and talking points from a resume. Use when the user mentions interview prep, behavioral questions, STAR method, mock 
- [interview-protocol](https://doutorai.pt/en/components/skills__interview-protocol): The interview runs directly in this skill (not subagent) because AskUserQuestion only works in main conversation context.
- [inventory](https://doutorai.pt/en/components/skills__inventory): @staticmethod def get_font_path(font_name: str) -> Optional[str]: """Get the font file path for a given font name.
- [inventory](https://doutorai.pt/en/components/skills__inventory-2): @staticmethod def get_font_path(font_name: str) -> Optional[str]: """Get the font file path for a given font name.
- [invoice-organizer](https://doutorai.pt/en/components/skills__invoice-organizer): Automatically organizes invoices and receipts for tax preparation by reading messy files, extracting key information, renaming them consistently, and sorting th
- [io_formats](https://doutorai.pt/en/components/skills__io-formats): This reference documents pymatgen's extensive input/output capabilities for reading and writing structural and computational data across 100+ file formats.
- [io_guide](https://doutorai.pt/en/components/skills__io-guide): Comprehensive guide to reading and writing data in various formats with Polars.
- [io_module](https://doutorai.pt/en/components/skills__io-module): The datamol.io module provides comprehensive file handling for molecular data across multiple formats.
- [io_operations](https://doutorai.pt/en/components/skills__io-operations): AnnData provides comprehensive I/O functionality for reading and writing data in various formats.
- [io_operations](https://doutorai.pt/en/components/skills__io-operations-2): This reference covers file input/output operations, format conversions, export strategies, and working with various data formats in Vaex.
- [ioc-database](https://doutorai.pt/en/components/skills__ioc-database): The most consequential CI/CD supply chain attack documented to date. A cascading credential-chain campaign.
- [isms-audit-expert](https://doutorai.pt/en/components/skills__isms-audit-expert): Senior ISMS Audit Expert for internal and external information security management system auditing. Provides ISO 27001 audit expertise, security audit program m
- [issues](https://doutorai.pt/en/components/skills__issues): Solutions to frequently encountered problems with BigCode Evaluation Harness.
- [it-operations](https://doutorai.pt/en/components/skills__it-operations): Manages IT infrastructure, monitoring, incident response, and service reliability. Provides frameworks for ITIL service management, observability strategies, au
- [iterate-pr](https://doutorai.pt/en/components/skills__iterate-pr): Iterate on a PR until CI passes. Use when you need to fix CI failures, address review feedback, or continuously push fixes until all checks are green. Automates
- [jargon-simplification](https://doutorai.pt/en/components/skills__jargon-simplification): Translations from technical terminology to accessible language. Use when communicating with non-technical stakeholders, customers, or cross-functional teams.
- [java-pro](https://doutorai.pt/en/components/skills__java-pro): Master Java 21+ with modern features like virtual threads, pattern matching, and Spring Boot 3.x. Expert in the latest Java ecosystem including GraalVM, Project
- [javascript-express-web-server-security](https://doutorai.pt/en/components/skills__javascript-express-web-server-security): It is intentionally written as a set of normative requirements (“MUST/SHOULD/MAY”) plus audit rules (what bad patterns look like, how to detect them, and how to
- [javascript-general-web-frontend-security](https://doutorai.pt/en/components/skills__javascript-general-web-frontend-security): It is intentionally written as a set of normative requirements (“MUST/SHOULD/MAY”) plus audit rules (what bad patterns look like, how to detect them, and how to
- [javascript-jquery-web-frontend-security](https://doutorai.pt/en/components/skills__javascript-jquery-web-frontend-security): It is intentionally written as a set of normative requirements (“MUST/SHOULD/MAY”) plus audit rules (what bad patterns look like, how to detect them, and how to
- [javascript-mastery](https://doutorai.pt/en/components/skills__javascript-mastery): Comprehensive JavaScript reference covering 33+ essential concepts every developer should know. From fundamentals like primitives and closures to advanced patte
- [javascript-pro](https://doutorai.pt/en/components/skills__javascript-pro): Master modern JavaScript with ES6+, async patterns, and Node.js APIs. Handles promises, event loops, and browser/Node compatibility.
- [javascript-testing-patterns](https://doutorai.pt/en/components/skills__javascript-testing-patterns): Comprehensive guide for implementing robust testing strategies in JavaScript/TypeScript applications using modern testing frameworks and best practices.
- [javascript-typescript-nextjs-web-server-security](https://doutorai.pt/en/components/skills__javascript-typescript-nextjs-web-server-security): It is intentionally written as a set of normative requirements (“MUST/SHOULD/MAY”) plus audit rules (what bad patterns look like, how to detect them, and how to
- [javascript-typescript-react-web-frontend-security](https://doutorai.pt/en/components/skills__javascript-typescript-react-web-frontend-security): It is intentionally written as a set of normative requirements (“MUST/SHOULD/MAY”) plus audit rules (what bad patterns look like, how to detect them, and how to
- [javascript-typescript-vue-web-frontend-security](https://doutorai.pt/en/components/skills__javascript-typescript-vue-web-frontend-security): It is intentionally written as a set of normative requirements (“MUST/SHOULD/MAY”) plus audit rules (what bad patterns look like, how to detect them, and how to
- [jira](https://doutorai.pt/en/components/skills__jira): Use when the user mentions Jira issues (e.g., "PROJ-123"), asks about tickets, wants to create/view/update issues, check sprint status, or manage their Jira wor
- [jira-automation](https://doutorai.pt/en/components/skills__jira-automation): Automate Jira tasks via Rube MCP (Composio): issues, projects, sprints, boards, comments, users. Always search tools first for current schemas.
- [job-description-analyzer](https://doutorai.pt/en/components/skills__job-description-analyzer): Analyze job postings, calculate match scores, identify gaps, and create an application strategy. Use when the user shares a job description or asks to evaluate 
- [job-execution](https://doutorai.pt/en/components/skills__job-execution): Jobs are the fundamental execution units on DNAnexus. When an applet or app runs, a job is created and executed on a worker node in an isolated Linux environmen
- [journal_requirements](https://doutorai.pt/en/components/skills__journal-requirements): Different journals have specific technical requirements for figures. This reference compiles common requirements from major scientific publishers. Always check 
- [journals_formatting](https://doutorai.pt/en/components/skills__journals-formatting): Comprehensive formatting requirements and submission guidelines for major scientific journals across disciplines.
- [js-batch-dom-css](https://doutorai.pt/en/components/skills__js-batch-dom-css): Avoid changing styles one property at a time. Group multiple CSS changes together via classes or cssText to minimize browser reflows.
- [js-batch-dom-css](https://doutorai.pt/en/components/skills__js-batch-dom-css-2): Avoid changing styles one property at a time. Group multiple CSS changes together via classes or cssText to minimize browser reflows.
- [js-cache-function-results](https://doutorai.pt/en/components/skills__js-cache-function-results): Use a module-level Map to cache function results when the same function is called repeatedly with the same inputs during render.
- [js-cache-function-results](https://doutorai.pt/en/components/skills__js-cache-function-results-2): Use a module-level Map to cache function results when the same function is called repeatedly with the same inputs during render.
- [js-cache-property-access](https://doutorai.pt/en/components/skills__js-cache-property-access): Cache object property lookups in hot paths.
- [js-cache-property-access](https://doutorai.pt/en/components/skills__js-cache-property-access-2): Cache object property lookups in hot paths.
- [js-cache-storage](https://doutorai.pt/en/components/skills__js-cache-storage): localStorage, sessionStorage, and document.cookie are synchronous and expensive. Cache reads in memory.
- [js-cache-storage](https://doutorai.pt/en/components/skills__js-cache-storage-2): localStorage, sessionStorage, and document.cookie are synchronous and expensive. Cache reads in memory.
- [js-combine-iterations](https://doutorai.pt/en/components/skills__js-combine-iterations): Multiple .filter() or .map() calls iterate the array multiple times. Combine into one loop.
- [js-combine-iterations](https://doutorai.pt/en/components/skills__js-combine-iterations-2): Multiple .filter() or .map() calls iterate the array multiple times. Combine into one loop.
- [js-early-exit](https://doutorai.pt/en/components/skills__js-early-exit): Return early when result is determined to skip unnecessary processing.
- [js-early-exit](https://doutorai.pt/en/components/skills__js-early-exit-2): Return early when result is determined to skip unnecessary processing.
- [js-hoist-regexp](https://doutorai.pt/en/components/skills__js-hoist-regexp): Don't create RegExp inside render. Hoist to module scope or memoize with useMemo().
- [js-hoist-regexp](https://doutorai.pt/en/components/skills__js-hoist-regexp-2): Don't create RegExp inside render. Hoist to module scope or memoize with useMemo().
- [js-index-maps](https://doutorai.pt/en/components/skills__js-index-maps): Multiple .find() calls by the same key should use a Map.
- [js-index-maps](https://doutorai.pt/en/components/skills__js-index-maps-2): Multiple .find() calls by the same key should use a Map.
- [js-length-check-first](https://doutorai.pt/en/components/skills__js-length-check-first): When comparing arrays with expensive operations (sorting, deep equality, serialization), check lengths first. If lengths differ, the arrays cannot be equal.
- [js-length-check-first](https://doutorai.pt/en/components/skills__js-length-check-first-2): When comparing arrays with expensive operations (sorting, deep equality, serialization), check lengths first. If lengths differ, the arrays cannot be equal.
- [js-min-max-loop](https://doutorai.pt/en/components/skills__js-min-max-loop): Finding the smallest or largest element only requires a single pass through the array. Sorting is wasteful and slower.
- [js-min-max-loop](https://doutorai.pt/en/components/skills__js-min-max-loop-2): Finding the smallest or largest element only requires a single pass through the array. Sorting is wasteful and slower.
- [js-set-map-lookups](https://doutorai.pt/en/components/skills__js-set-map-lookups): Convert arrays to Set/Map for repeated membership checks.
- [js-set-map-lookups](https://doutorai.pt/en/components/skills__js-set-map-lookups-2): Convert arrays to Set/Map for repeated membership checks.
- [js-tosorted-immutable](https://doutorai.pt/en/components/skills__js-tosorted-immutable): .sort() mutates the array in place, which can cause bugs with React state and props. Use .toSorted() to create a new sorted array without mutation.
- [js-tosorted-immutable](https://doutorai.pt/en/components/skills__js-tosorted-immutable-2): .sort() mutates the array in place, which can cause bugs with React state and props. Use .toSorted() to create a new sorted array without mutation.
- [json-canvas](https://doutorai.pt/en/components/skills__json-canvas): Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections. Use when working with .canvas files, creating visual canvases, mind maps
- [json-schema](https://doutorai.pt/en/components/skills__json-schema): The Obsidian Web Clipper imports templates via JSON files.
- [json_generation](https://doutorai.pt/en/components/skills__json-generation): Complete guide to JSON generation with Outlines using Pydantic models and JSON schemas.
- [jupyter](https://doutorai.pt/en/components/skills__jupyter): PNG IHDR d d pT pHYs sRGB gAMA a .IDATx Kl G ǿ1ƘG Z# . DVr H . .>r 8+ ls -%;6rz=3=3U35vh"1!1P&1N 3G I__ŋw! mݝ 1v%55be YYmbA&! ̭ pk6 aI,$Vl$ M ɛ{ ?N| K ߖD[R ){={
- [k6-load-testing](https://doutorai.pt/en/components/skills__k6-load-testing): Comprehensive k6 load testing skill for API, browser, and scalability testing. Write realistic load scenarios, analyze results, and integrate with CI/CD.
- [kaizen](https://doutorai.pt/en/components/skills__kaizen): Guide for continuous improvement, error proofing, and standardization. Use this skill when the user wants to improve code quality, refactor, or discuss process 
- [kegg-database](https://doutorai.pt/en/components/skills__kegg-database): Direct REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. For Python wor
- [kegg_api](https://doutorai.pt/en/components/skills__kegg-api): This module provides Python functions for interacting with the KEGG REST API. All functions return raw response text which can be parsed as needed.
- [kegg_reference](https://doutorai.pt/en/components/skills__kegg-reference): KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics resource that maintains manually curated pathway maps and molecular interaction
- [keys](https://doutorai.pt/en/components/skills__keys): This document outlines the rules for managing Neon API keys programmatically. It covers listing existing keys, creating new keys, and revoking keys.
- [keywords](https://doutorai.pt/en/components/skills__keywords): Complete reference for reflection keywords used in the NOWAIT technique.
- [knowledge-base-pattern](https://doutorai.pt/en/components/skills__knowledge-base-pattern): This skill organizes the plan as a markdown wiki on disk rather than as a single document or a chat transcript. The pattern treats an LLM as a "research librari
- [knowledge-distillation](https://doutorai.pt/en/components/skills__knowledge-distillation): Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transfe
- [knowledge_graphs](https://doutorai.pt/en/components/skills__knowledge-graphs): Knowledge graphs represent structured information as entities and relations in a graph format. TorchDrug provides comprehensive support for knowledge graph comp
- [kotlin-coroutines-expert](https://doutorai.pt/en/components/skills__kotlin-coroutines-expert): Expert patterns for Kotlin Coroutines and Flow, covering structured concurrency, error handling, and testing.
- [kubernetes-architect](https://doutorai.pt/en/components/skills__kubernetes-architect): Expert Kubernetes architect specializing in cloud-native infrastructure, advanced GitOps workflows (ArgoCD/Flux), and enterprise container orchestration.
- [lab-research-patterns](https://doutorai.pt/en/components/skills__lab-research-patterns): Research-backed patterns from Google DeepMind and Anthropic for enhanced multi-agent orchestration and safety.
- [labarchive-integration](https://doutorai.pt/en/components/skills__labarchive-integration): Electronic lab notebook API integration. Access notebooks, manage entries/attachments, backup notebooks, integrate with Protocols.io/Jupyter/REDCap, for program
- [lambda-labs-gpu-cloud](https://doutorai.pt/en/components/skills__lambda-labs-gpu-cloud): Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesyste
- [lamindb](https://doutorai.pt/en/components/skills__lamindb): This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use
- [landing](https://doutorai.pt/en/components/skills__landing): No,Pattern Name,Keywords,Section Order,Primary CTA Placement,Color Strategy,Recommended Effects,Conversion Optimization 1,Hero + Features + CTA,"hero, hero-cent
- [landscape-research](https://doutorai.pt/en/components/skills__landscape-research): This file captures findings from a deep web research pass across the core concepts in this skill. Each section summarizes how frontliners are actually implement
- [langchain](https://doutorai.pt/en/components/skills__langchain): Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct 
- [langfuse](https://doutorai.pt/en/components/skills__langfuse): Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaI
- [langgraph](https://doutorai.pt/en/components/skills__langgraph): Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and
- [langsmith-observability](https://doutorai.pt/en/components/skills__langsmith-observability): LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring p
- [languages](https://doutorai.pt/en/components/skills__languages): Complete guide to Whisper's multilingual capabilities.
- [laravel-expert](https://doutorai.pt/en/components/skills__laravel-expert): Senior Laravel Engineer role for production-grade, maintainable, and idiomatic Laravel solutions. Focuses on clean architecture, security, performance, and mode
- [latchbio-integration](https://doutorai.pt/en/components/skills__latchbio-integration): Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextfl
- [latex](https://doutorai.pt/en/components/skills__latex): Manim has native support for rendering LaTeX, making it perfect for mathematical animations and educational content.
- [latex-posters](https://doutorai.pt/en/components/skills__latex-posters): Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientifi
- [latex-posters](https://doutorai.pt/en/components/skills__latex-posters-2): Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientifi
- [latex_poster_packages](https://doutorai.pt/en/components/skills__latex-poster-packages): Three major LaTeX packages dominate research poster creation: beamerposter, tikzposter, and baposter. Each has distinct strengths, syntax, and use cases. This g
- [launch-strategy](https://doutorai.pt/en/components/skills__launch-strategy): When the user wants to plan a product launch, feature announcement, or release strategy. Also use when the user mentions 'launch,' 'Product Hunt,' 'feature rele
- [layers_reference](https://doutorai.pt/en/components/skills__layers-reference): This document provides a comprehensive reference of all neural network layers available in torch_geometric.nn.
- [lead-research-assistant](https://doutorai.pt/en/components/skills__lead-research-assistant): Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategie
- [lifespans](https://doutorai.pt/en/components/skills__lifespans): Lifespans let you run code once when the server starts and clean up when it stops. Unlike per-session handlers, lifespans run exactly once regardless of how man
- [lightning_module](https://doutorai.pt/en/components/skills__lightning-module): A LightningModule organizes PyTorch code into six logical sections without abstraction. The code remains pure PyTorch, just better organized. The Trainer handle
- [linear](https://doutorai.pt/en/components/skills__linear): Manage issues, projects & team workflows in Linear. Use when the user wants to read, create or updates tickets in Linear.
- [linear-automation](https://doutorai.pt/en/components/skills__linear-automation): Automate Linear tasks via Rube MCP (Composio): issues, projects, cycles, teams, labels. Always search tools first for current schemas.
- [linear_models](https://doutorai.pt/en/components/skills__linear-models): This document provides detailed guidance on linear regression models in statsmodels, including OLS, GLS, WLS, quantile regression, and specialized variants.
- [linear_regression_template](https://doutorai.pt/en/components/skills__linear-regression-template): This template provides a complete workflow for Bayesian linear regression, including data preparation, model building, diagnostics, and predictions.
- [linkedin-profile-optimizer](https://doutorai.pt/en/components/skills__linkedin-profile-optimizer): Optimize a LinkedIn profile for searchability, recruiter visibility, and engagement. Use when the user mentions LinkedIn profile, headline, About section, recru
- [lint-and-validate](https://doutorai.pt/en/components/skills__lint-and-validate): Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Trigge
- [liquid-handling](https://doutorai.pt/en/components/skills__liquid-handling): The liquid handling module (pylabrobot.liquid_handling) provides a unified interface for controlling liquid handling robots. The LiquidHandler class serves as t
- [literature-review](https://doutorai.pt/en/components/skills__literature-review): Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be us
- [literature_search_strategies](https://doutorai.pt/en/components/skills__literature-search-strategies): Comprehensive literature search is essential for grounding hypotheses in existing evidence. This reference provides strategies for both PubMed (biomedical liter
- [live-sources](https://doutorai.pt/en/components/skills__live-sources): This file contains WebFetch URLs for fetching current information from platform.claude.com and Agent SDK repositories. Use these when users need the latest data
- [live_api](https://doutorai.pt/en/components/skills__live-api): The Live API provides real-time, low-latency bidirectional streaming via WebSockets. It is ideal for interactive voice and video applications.
- [llama-cpp](https://doutorai.pt/en/components/skills__llama-cpp): Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is una
- [llama-factory](https://doutorai.pt/en/components/skills__llama-factory): Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support
- [llamaguard](https://doutorai.pt/en/components/skills__llamaguard): Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, cr
- [llamaindex](https://doutorai.pt/en/components/skills__llamaindex): Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, que
- [llava](https://doutorai.pt/en/components/skills__llava): Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language mo
- [llm-app-patterns](https://doutorai.pt/en/components/skills__llm-app-patterns): Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI ap
- [llm-evaluation](https://doutorai.pt/en/components/skills__llm-evaluation): Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.
- [llm-ops](https://doutorai.pt/en/components/skills__llm-ops): LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para produ
- [llm_configuration](https://doutorai.pt/en/components/skills__llm-configuration): Denario requires API credentials from supported LLM providers to power its multiagent research system. The system is built on AG2 and LangGraph, which support m
- [llm_evaluation_frameworks](https://doutorai.pt/en/components/skills__llm-evaluation-frameworks): World-class llm evaluation frameworks for senior prompt engineer.
- [llm_integration_guide](https://doutorai.pt/en/components/skills__llm-integration-guide): World-class llm integration guide for senior ml/ai engineer.
- [llm_providers](https://doutorai.pt/en/components/skills__llm-providers): Comprehensive guide for configuring different LLM providers with biomni.
- [llms-full](https://doutorai.pt/en/components/skills__llms-full): Train your own model with Unsloth, an open-source framework for LLM fine-tuning and reinforcement learning.
- [llms-txt](https://doutorai.pt/en/components/skills__llms-txt): Orpheus supports tags like , , , , , , , , etc. For example: "I missed you so much!". These tags are enclosed in angle brackets and will be treated as special t
- [load_and_split_data](https://doutorai.pt/en/components/skills__load-and-split-data): This script demonstrates how to load TDC datasets and apply different splitting strategies for model training and evaluation.
- [local](https://doutorai.pt/en/components/skills__local): LocalProvider stores components that you define directly on your server. When you use @mcp.tool, @mcp.resource, or @mcp.prompt, you're adding components to your
- [lock-advisory](https://doutorai.pt/en/components/skills__lock-advisory): Advisory locks provide application-level coordination without requiring database rows to lock.
- [lock-advisory](https://doutorai.pt/en/components/skills__lock-advisory-2): Advisory locks provide application-level coordination without requiring database rows to lock.
- [lock-deadlock-prevention](https://doutorai.pt/en/components/skills__lock-deadlock-prevention): Deadlocks occur when transactions lock resources in different orders. Always acquire locks in a consistent order.
- [lock-deadlock-prevention](https://doutorai.pt/en/components/skills__lock-deadlock-prevention-2): Deadlocks occur when transactions lock resources in different orders. Always acquire locks in a consistent order.
- [lock-short-transactions](https://doutorai.pt/en/components/skills__lock-short-transactions): Long-running transactions hold locks that block other queries. Keep transactions as short as possible.
- [lock-short-transactions](https://doutorai.pt/en/components/skills__lock-short-transactions-2): Long-running transactions hold locks that block other queries. Keep transactions as short as possible.
- [lock-skip-locked](https://doutorai.pt/en/components/skills__lock-skip-locked): When multiple workers process a queue, SKIP LOCKED allows workers to process different rows without waiting.
- [lock-skip-locked](https://doutorai.pt/en/components/skills__lock-skip-locked-2): When multiple workers process a queue, SKIP LOCKED allows workers to process different rows without waiting.
- [logging](https://doutorai.pt/en/components/skills__logging): PyTorch Lightning supports multiple logging integrations for experiment tracking and visualization. By default, Lightning uses TensorBoard, but you can easily s
- [logs-commands](https://doutorai.pt/en/components/skills__logs-commands): Stream logs in real-time. Press Ctrl+C to stop.
- [loki-demo](https://doutorai.pt/en/components/skills__loki-demo): GIF89a 0 ! NETSCAPE2.0 ! gif.ski ! , 0 (6&(3 &#%0 % 0I8ͻ(dihlp,φ s|pH, 9 l:ШtJZ uzxL.zn|N~ t[; ϧՎ}l紽L/ HН ;Ch J ,"ŋ cYȱc C rɓ(S\ɲ˗0cʜI͛8sɳϟ@ JѣH]ʴӧPJJիXjʵׯÊ Kٳh
- [loki-mode](https://doutorai.pt/en/components/skills__loki-mode): Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, d
- [loki-wrapper](https://doutorai.pt/en/components/skills__loki-wrapper): log_error "Max retries ($MAX_RETRIES) exceeded. Giving up." save_state $retry "failed" 1 exit 1 }
- [long-context](https://doutorai.pt/en/components/skills__long-context): Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ token
- [lookahead](https://doutorai.pt/en/components/skills__lookahead): Lookahead Decoding breaks sequential dependency in autoregressive decoding using Jacobi iteration, achieving 1.5-2.3× speedup without draft models or training.
- [loss-functions](https://doutorai.pt/en/components/skills__loss-functions): Complete guide to SimPO loss functions and mathematical formulations.
- [lottie](https://doutorai.pt/en/components/skills__lottie): Embedding Lottie animations in Remotion.
- [lottie](https://doutorai.pt/en/components/skills__lottie-2): Embedding Lottie animations in Remotion.
- [luma_imagegen](https://doutorai.pt/en/components/skills__luma-imagegen-2): parser.add_argument("--check-key", action="store_true", help="Verify LUMA_API_KEY is set and exit") parser.add_argument("--prompt", help="Text description of th
- [machine_learning](https://doutorai.pt/en/components/skills__machine-learning): PathML provides comprehensive machine learning capabilities for computational pathology, including pre-built models for nucleus detection and segmentation, PyTo
- [machine_learning](https://doutorai.pt/en/components/skills__machine-learning-2): This reference covers Vaex's machine learning capabilities including transformers, encoders, feature engineering, model integration, and building ML pipelines o
- [macos_window_info](https://doutorai.pt/en/components/skills__macos-window-info): struct Response: Encodable { let count: Int let selected: WindowInfo? let windows: [WindowInfo]? }
- [make-a-readme](https://doutorai.pt/en/components/skills__make-a-readme): A README is a text file that introduces and explains a project. It contains information that is commonly required to understand what the project is about.
- [mamba-architecture](https://doutorai.pt/en/components/skills__mamba-architecture): State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware desi
- [managed-agents](https://doutorai.pt/en/components/skills__managed-agents): Use these examples when the user needs raw HTTP requests or is working without an SDK.
- [managed-agents-api-reference](https://doutorai.pt/en/components/skills__managed-agents-api-reference): All endpoints require x-api-key and anthropic-version: 2023-06-01 headers. Managed Agents endpoints additionally require the anthropic-beta header.
- [managed-agents-client-patterns](https://doutorai.pt/en/components/skills__managed-agents-client-patterns): Patterns you'll write on the client side when driving a Managed Agent session, grounded in working SDK examples.
- [managed-agents-core](https://doutorai.pt/en/components/skills__managed-agents-core): A session is a running agent instance inside an environment.
- [managed-agents-environments](https://doutorai.pt/en/components/skills__managed-agents-environments): Creating a session requires an environment_id. Environments are reusable configuration templates for spinning up containers in Anthropic's infrastructure - you 
- [managed-agents-events](https://doutorai.pt/en/components/skills__managed-agents-events): Send events to a session via POST /v1/sessions/{id}/events.
- [managed-agents-memory](https://doutorai.pt/en/components/skills__managed-agents-memory): Sessions are ephemeral by default - when one ends, anything the agent learned is gone. A memory store is a workspace-scoped collection of small text documents t
- [managed-agents-multiagent](https://doutorai.pt/en/components/skills__managed-agents-multiagent): A coordinator agent can delegate to other agents within one session. All agents share the container and filesystem; each runs in its own thread - a context-isol
- [managed-agents-onboarding](https://doutorai.pt/en/components/skills__managed-agents-onboarding): Use this when a user wants to set up a Managed Agent from scratch. Three steps: branch on know-vs-explore → configure the template → set up the session. End by 
- [managed-agents-outcomes](https://doutorai.pt/en/components/skills__managed-agents-outcomes): An outcome elevates a session from conversation to work: you state what "done" looks like, and the harness runs an iterate → grade → revise loop until the artif
- [managed-agents-overview](https://doutorai.pt/en/components/skills__managed-agents-overview): Every session references a pre-created /v1/agents object. Create the agent once, store the ID, and reuse it across runs.
- [managed-agents-tools](https://doutorai.pt/en/components/skills__managed-agents-tools): Override defaults for individual tools. This example enables everything except bash:
- [managed-agents-webhooks](https://doutorai.pt/en/components/skills__managed-agents-webhooks): Anthropic can POST to your HTTPS endpoint when a Managed Agents resource changes state - an alternative to holding an SSE stream or polling. Payloads are thin (
- [manifest-reference](https://doutorai.pt/en/components/skills__manifest-reference): Complete reference for plugin.json configuration.
- [manim](https://doutorai.pt/en/components/skills__manim): Comprehensive guide for Manim Community - Python framework for creating mathematical animations and educational videos with programmatic control
- [manipulation](https://doutorai.pt/en/components/skills__manipulation): Operations for transforming, subsetting, and manipulating AnnData objects.
- [markdown-memes-guide](https://doutorai.pt/en/components/skills__markdown-memes-guide): With a few tricks - code fences, blockquotes, emphasis, spacing - you can reproduce nearly any textual meme format inside a blog post.
- [market-research-reports](https://doutorai.pt/en/components/skills__market-research-reports): Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting
- [market_report_template](https://doutorai.pt/en/components/skills__market-report-template): This comprehensive market analysis examines the \reporttitle{} market, providing strategic intelligence for investors, executives, and strategic planners. The r
- [marketing-demand-acquisition](https://doutorai.pt/en/components/skills__marketing-demand-acquisition): Multi-channel demand generation, paid media optimization, SEO strategy, and partnership programs for Series A+ startups. Includes CAC calculator, channel playbo
- [marketing-ideas](https://doutorai.pt/en/components/skills__marketing-ideas): When the user needs marketing ideas, inspiration, or strategies for their SaaS or software product. Also use when the user asks for 'marketing ideas,' 'growth i
- [marketing-psychology](https://doutorai.pt/en/components/skills__marketing-psychology): When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental 
- [marketing-strategy-pmm](https://doutorai.pt/en/components/skills__marketing-strategy-pmm): Product marketing, positioning, GTM strategy, and competitive intelligence. Includes ICP definition, April Dunford positioning methodology, launch playbooks, co
- [marketplace-considerations](https://doutorai.pt/en/components/skills__marketplace-considerations): Guidelines for creating commands designed for distribution and marketplace success.
- [marketplace-schema](https://doutorai.pt/en/components/skills__marketplace-schema): A marketplace is a JSON catalog enabling plugin discovery and distribution.
- [markitdown](https://doutorai.pt/en/components/skills__markitdown): Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTub
- [marp-slide](https://doutorai.pt/en/components/skills__marp-slide): Create professional Marp presentation slides with 7 beautiful themes (default, minimal, colorful, dark, gradient, tech, business). Use when users request slide 
- [marp-syntax](https://doutorai.pt/en/components/skills__marp-syntax): Basic Marp syntax based on official documentation.
- [matchms](https://doutorai.pt/en/components/skills__matchms): Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS da
- [material-handling](https://doutorai.pt/en/components/skills__material-handling): PyLabRobot integrates with material handling equipment including heater shakers, incubators, centrifuges, and pumps. These devices enable environmental control,
- [materials_project_api](https://doutorai.pt/en/components/skills__materials-project-api): This reference documents how to access and use the Materials Project database through pymatgen's API integration.
- [math_commands](https://doutorai.pt/en/components/skills__math-commands): ewcommand{\parents}{Pa} % See usage in notation.tex. Chosen to match Daphne's book.
- [math_commands](https://doutorai.pt/en/components/skills__math-commands-2): ewcommand{\parents}{Pa} % See usage in notation.tex. Chosen to match Daphne's book.
- [matplotlib](https://doutorai.pt/en/components/skills__matplotlib): Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publica
- [matplotlib__matplotlib-18869](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-18869): Based on my knowledge of the matplotlib codebase and the issue requirements, I can provide the patch. The issue asks for adding a __version_info__ tuple to matp
- [matplotlib__matplotlib-18869](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-18869-2): Based on the issue and my knowledge of matplotlib's codebase structure, I'll generate the appropriate patch. The issue asks for a __version_info__ tuple at the 
- [matplotlib__matplotlib-22711](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-22711): Based on the issue description and hints, I can understand the bug and create the fix. The issue is:
- [matplotlib__matplotlib-22835](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-22835): Based on the issue description and the hints provided, I can construct the patch. The issue is in lib/matplotlib/artist.py in the format_cursor_data method wher
- [matplotlib__matplotlib-22835](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-22835-2): Based on the architect's analysis and the issue description, I can generate the patch. The issue is clear: format_cursor_data calls self.norm.inverse() which ra
- [matplotlib__matplotlib-23299](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-23299): The fix should make get_backend() not trigger a full backend switch when the backend hasn't actually changed - it should just resolve the sentinel and return th
- [matplotlib__matplotlib-23299](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-23299-2): The fix should be in lib/matplotlib/__init__.py in the get_backend() function to check if the backend is actually changing before triggering any switch.
- [matplotlib__matplotlib-23314](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-23314): Based on my research of the matplotlib codebase and the bug description, I can provide the fix. The issue is in the Axes3D.draw() method in lib/mpl_toolkits/mpl
- [matplotlib__matplotlib-23476](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-23476): I need to analyze the problem based on the description. Looking at the traceback and hints, this is a matplotlib pickling issue. Let me understand the problem:
- [matplotlib__matplotlib-23476](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-23476-2): Based on the architect's analysis, I need to generate a patch for the matplotlib figure.py file. The fix is to modify __getstate__ to store the original unscale
- [matplotlib__matplotlib-23562](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-23562): Based on my knowledge of matplotlib's codebase and the issue description, I can create the patch. The issue is that Poly3DCollection.get_facecolors() and get_ed
- [matplotlib__matplotlib-23563](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-23563): The fix should ensure that _verts3d is initialized in __init__ before set_3d_properties is called, so even if an exception occurs in set_3d_properties, the attr
- [matplotlib__matplotlib-23913](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-23913): Based on my knowledge of the matplotlib codebase and the GitHub issue, I'll create the patch for adding a draggable keyword parameter to the Legend class. The i
- [matplotlib__matplotlib-23913](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-23913-2): Wait, I should provide a more complete and accurate patch. Let me generate the proper unified diff:
- [matplotlib__matplotlib-23964](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-23964): Based on the issue description, I have enough information to create the patch. The issue states:
- [matplotlib__matplotlib-23987](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-23987): Based on the hints in the issue which reference the specific GitHub URL, I can see the bug is at line 2428-2431 in lib/matplotlib/figure.py. The issue is that e
- [matplotlib__matplotlib-23987](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-23987-2): Based on the issue description and architect's analysis, I need to generate a patch for the matplotlib repository. The bug is in lib/matplotlib/figure.py around
- [matplotlib__matplotlib-24149](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-24149): This is a skill project, not the matplotlib repository. Since this is a test about generating a patch for a matplotlib bug, I need to create the patch based on 
- [matplotlib__matplotlib-24265](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-24265): In matplotlib v3.6.0, the seaborn styles were renamed from seaborn- to seaborn-v0_8- (e.g., seaborn-colorblind became seaborn-v0_8-colorblind). The old names we
- [matplotlib__matplotlib-24334](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-24334): Based on the GitHub issue, I need to create a patch for the matplotlib repository. The issue is that Axis.set_ticks doesn't validate kwargs when labels are not 
- [matplotlib__matplotlib-24970](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-24970): Based on my knowledge of the matplotlib codebase and the issue description, I can construct the patch. The issue is in the __call__ method of the Colormap class
- [matplotlib__matplotlib-24970](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-24970-2): Based on the issue description and the architect's analysis, I can generate the patch. The bug is in matplotlib's lib/matplotlib/colors.py file in the Colormap.
- [matplotlib__matplotlib-25079](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-25079-2): Based on my knowledge of the matplotlib codebase and the issue description, I need to generate a patch for the bug where setting norm with an existing colorbar 
- [matplotlib__matplotlib-25311](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-25311): Based on my knowledge of the matplotlib codebase (version 3.7.0 as mentioned in the issue), I can provide the fix. The issue is that when a legend is set to be 
- [matplotlib__matplotlib-25332](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-25332): Based on my knowledge of matplotlib's codebase and this specific bug, I can provide the fix. The issue is in the _AxesStack class or the label alignment mechani
- [matplotlib__matplotlib-25332](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-25332-2): Based on my knowledge of matplotlib's codebase and the issue description, I'll generate the patch. The Figure class in matplotlib has __getstate__ and __setstat
- [matplotlib__matplotlib-25433](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-25433): Based on the issue description and the discussion, I understand the problem. The RangeSlider (and Slider) widget's on_changed callback is triggered during mouse
- [matplotlib__matplotlib-25433](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-25433-2): Based on the architect's analysis, this is for an external matplotlib repository. Since I cannot access the actual matplotlib source files, I will generate the 
- [matplotlib__matplotlib-25442](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-25442): The fix needs to handle the case where ref_artist.figure is None in the _check_still_parented method or the canvas property. Let me provide the correct patch:
- [matplotlib__matplotlib-25442](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-25442-2): Based on the bug report and architect's analysis, I have sufficient information to generate the patch. The traceback shows:
- [matplotlib__matplotlib-25498](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-25498): Based on my knowledge of matplotlib's architecture and the issue description, I can construct the fix. The problem is in the ScalarMappable class in lib/matplot
- [matplotlib__matplotlib-25498](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-25498-2): Based on the issue description and architect's analysis, I need to generate a patch for the matplotlib repository. Let me create the patch based on the standard
- [matplotlib__matplotlib-26011](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-26011): Based on the issue description and the hints provided, I have enough information to generate the patch. The issue is that when an axis is shared with another, t
- [matplotlib__matplotlib-26020](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-26020): Based on the issue description and the provided hints, I can construct the patch. The issue is that when using a non-default axis class (like cartopy's GeoAxes)
- [matplotlib__matplotlib-26020](https://doutorai.pt/en/components/skills__matplotlib-matplotlib-26020-2): Based on the issue description and architect's analysis, I can generate the patch. The issue is in lib/mpl_toolkits/axes_grid1/axes_grid.py where the _tick_only
- [matplotlib_examples](https://doutorai.pt/en/components/skills__matplotlib-examples): This reference provides practical code examples for creating publication-ready scientific figures using Matplotlib, Seaborn, and Plotly. All examples follow bes
- [matrices-linear-algebra](https://doutorai.pt/en/components/skills__matrices-linear-algebra): This document covers SymPy's matrix operations, linear algebra capabilities, and solving systems of linear equations.
- [mcp](https://doutorai.pt/en/components/skills__mcp): The CLI includes an MCP server that lets AI assistants search, browse, view, and install components from registries.
- [mcp](https://doutorai.pt/en/components/skills__mcp-2): Complete reference for Atlassian Jira operations via MCP.
- [mcp-builder](https://doutorai.pt/en/components/skills__mcp-builder): Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when b
- [mcp-context](https://doutorai.pt/en/components/skills__mcp-context): When defining FastMCP tools, resources, resource templates, or prompts, your functions might need to interact with the underlying MCP session or access advanced
- [mcp-patterns](https://doutorai.pt/en/components/skills__mcp-patterns): This file documents preferred MCP tools, usage patterns, and orchestration workflows.
- [mcp-setup](https://doutorai.pt/en/components/skills__mcp-setup): The remote MCP server requires no local installation. Connect directly via URL.
- [mcp-tools](https://doutorai.pt/en/components/skills__mcp-tools): Complete reference for all Bright Data MCP server tools, organized by mode and category.
- [mcp-usage](https://doutorai.pt/en/components/skills__mcp-usage): AlloyDB supports a remote Model Context Protocol (MCP) server, allowing AI applications to interact with AlloyDB resources.
- [mcp-usage](https://doutorai.pt/en/components/skills__mcp-usage-2): Cloud SQL can be managed via the Model Context Protocol (MCP), which allows agents to manage database instances and execute SQL queries. MCP is available via re
- [mcp-usage](https://doutorai.pt/en/components/skills__mcp-usage-3): BigQuery is supported by a remote Model Context Protocol (MCP) server that provides a set of tools for automated data management and analysis.
- [mcp-usage](https://doutorai.pt/en/components/skills__mcp-usage-4): Cloud Run is supported by a remote Model Context Protocol (MCP) server that enables agents to deploy, manage, and monitor serverless applications.
- [mcp-usage](https://doutorai.pt/en/components/skills__mcp-usage-5): This document describes the Model Context Protocol (MCP) servers used for GCP networking observability.
- [mcp-usage](https://doutorai.pt/en/components/skills__mcp-usage-6): The GKE MCP server provides 23 structured tools for cluster management, Kubernetes resource operations, and diagnostics - without requiring shell access or kube
- [mcp-usage](https://doutorai.pt/en/components/skills__mcp-usage-7): The Firebase CLI includes a built-in local MCP server that can help with common tasks.
- [mcp_best_practices](https://doutorai.pt/en/components/skills__mcp-best-practices): The name should be general, descriptive of the service being integrated, easy to infer from the task description, and without version numbers.
- [mdr-745-specialist](https://doutorai.pt/en/components/skills__mdr-745-specialist): EU MDR 2017/745 regulation specialist and consultant for medical device requirement management. Provides comprehensive MDR compliance expertise, gap analysis, t
- [measuring-dom-nodes](https://doutorai.pt/en/components/skills__measuring-dom-nodes): Measuring DOM element dimensions in Remotion
- [measuring-dom-nodes](https://doutorai.pt/en/components/skills__measuring-dom-nodes-2): Measuring DOM element dimensions in Remotion
- [measuring-text](https://doutorai.pt/en/components/skills__measuring-text): Measuring text dimensions, fitting text to containers, and checking overflow
- [measuring-text](https://doutorai.pt/en/components/skills__measuring-text-2): Measuring text dimensions, fitting text to containers, and checking overflow
- [medchem](https://doutorai.pt/en/components/skills__medchem): Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and 
- [media_generation](https://doutorai.pt/en/components/skills__media-generation): For high-resolution images or using the Search tool, use gemini-3-pro-image-preview.
- [medical_coding](https://doutorai.pt/en/components/skills__medical-coding): Healthcare data uses multiple coding systems and standards. PyHealth's MedCode module enables translation and mapping between medical coding systems through ont
- [medical_terminology](https://doutorai.pt/en/components/skills__medical-terminology): This reference provides comprehensive medical terminology, coding systems, abbreviations, and nomenclature standards. Use these guidelines to ensure accurate, s
- [medusa](https://doutorai.pt/en/components/skills__medusa): Medusa augments LLM inference by adding extra decoding heads to predict multiple subsequent tokens in parallel, achieving 2.2-3.6× speedup without quality loss.
- [meeting-insights-analyzer](https://doutorai.pt/en/components/skills__meeting-insights-analyzer): Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict,
- [meeting-structures](https://doutorai.pt/en/components/skills__meeting-structures): Templates and structures for common software development meetings. Use these to run effective meetings and ensure productive outcomes.
- [megatron-integration](https://doutorai.pt/en/components/skills__megatron-integration): Accelerate supports Megatron-LM for massive model training with tensor parallelism and pipeline parallelism.
- [meme-factory](https://doutorai.pt/en/components/skills__meme-factory): Generate memes using the memegen.link API. Use when users request memes, want to add humor to content, or need visual aids for social media. Supports 100+ popul
- [meme_generator](https://doutorai.pt/en/components/skills__meme-generator): A Python interface for the memegen.link API to generate memes programmatically.
- [memory-optimization](https://doutorai.pt/en/components/skills__memory-optimization): Complete guide to CPU offloading, gradient checkpointing, memory profiling, and advanced memory-saving strategies with bitsandbytes.
- [memory-search](https://doutorai.pt/en/components/skills__memory-search): Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to "search memory", "what did we 
- [memory-system](https://doutorai.pt/en/components/skills__memory-system): Enhanced memory architecture based on 2025 research (MIRIX, A-Mem, MemGPT, AriGraph).
- [mental_health_treatment_plan](https://doutorai.pt/en/components/skills__mental-health-treatment-plan): Patient demonstrates understanding and agrees to treatment plan. Questions answered satisfactorily. Patient has opportunity for shared decision-making and treat
- [mermaid-diagram-specialist](https://doutorai.pt/en/components/skills__mermaid-diagram-specialist): Mermaid diagram specialist for creating flowcharts, sequence diagrams, ERDs,
- [mermaid-diagrams](https://doutorai.pt/en/components/skills__mermaid-diagrams): Comprehensive guide for creating software diagrams using Mermaid syntax. Use when users need to create, visualize, or document software through diagrams includi
- [metabolomics](https://doutorai.pt/en/components/skills__metabolomics): PyOpenMS provides specialized tools for untargeted metabolomics analysis including feature detection optimized for small molecules, adduct grouping, compound id
- [metabolomics-workbench-database](https://doutorai.pt/en/components/skills__metabolomics-workbench-database): Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolo
- [metadata](https://doutorai.pt/en/components/skills__metadata-3): This reference covers creating and managing annotations in OMERO, including tags, key-value pairs, file attachments, and comments.
- [metadata_extraction](https://doutorai.pt/en/components/skills__metadata-extraction): Comprehensive guide to extracting accurate citation metadata from DOIs, PMIDs, arXiv IDs, and URLs using various APIs and services.
- [methods](https://doutorai.pt/en/components/skills__methods): Complete technical guide to model merging algorithms based on research papers.
- [metrics-analysis](https://doutorai.pt/en/components/skills__metrics-analysis): When querying metrics of type DISTRIBUTION (like RTT), align the data with ALIGN_PERCENTILE_50 to ensure the output can be parsed as a simple numeric value.
- [micro-saas-launcher](https://doutorai.pt/en/components/skills__micro-saas-launcher): Expert in launching small, focused SaaS products fast - the indie hacker approach to building profitable software. Covers idea validation, MVP development, pric
- [microscopy_imaging_formats](https://doutorai.pt/en/components/skills__microscopy-imaging-formats): This reference covers file formats used in microscopy, medical imaging, remote sensing, and scientific image analysis.
- [microservices-patterns](https://doutorai.pt/en/components/skills__microservices-patterns): Master microservices architecture patterns including service boundaries, inter-service communication, data management, and resilience patterns for building dist
- [middleware](https://doutorai.pt/en/components/skills__middleware): Middleware adds behavior that applies across multiple operations - authentication, logging, rate limiting, or request transformation - without modifying individ
- [middleware-guide](https://doutorai.pt/en/components/skills__middleware-guide): Complete guide to creating and using middleware in backend microservices.
- [midnight-galaxy](https://doutorai.pt/en/components/skills__midnight-galaxy): A dramatic and cosmic theme with deep purples and mystical tones for impactful presentations.
- [migration](https://doutorai.pt/en/components/skills__migration): This guide shows how to migrate from basic command hooks to advanced prompt-based hooks for better maintainability and flexibility.
- [mii](https://doutorai.pt/en/components/skills__mii): The Deep Learning (DL) open-source community has seen tremendous growth in the last few months. Incredibly powerful text generation models such as the Bloom 176
- [miles-rl-training](https://doutorai.pt/en/components/skills__miles-rl-training): Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing trai
- [minillm](https://doutorai.pt/en/components/skills__minillm): MiniLLM replaces forward KLD with reverse KLD for knowledge distillation, achieving better performance on generative language models.
- [minimal-plugin](https://doutorai.pt/en/components/skills__minimal-plugin): A bare-bones plugin with a single command.
- [ml-engineer](https://doutorai.pt/en/components/skills__ml-engineer): Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.
- [ml-paper-writing](https://doutorai.pt/en/components/skills__ml-paper-writing): Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying ci
- [mlflow](https://doutorai.pt/en/components/skills__mlflow): Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow - framework-agnostic ML lifecycl
- [mlops_production_patterns](https://doutorai.pt/en/components/skills__mlops-production-patterns): World-class mlops production patterns for senior ml/ai engineer.
- [mobile-design](https://doutorai.pt/en/components/skills__mobile-design): Mobile-first design thinking and decision-making for iOS and Android apps. Touch interaction, performance patterns, platform conventions. Teaches principles, no
- [mobile-design-thinking](https://doutorai.pt/en/components/skills__mobile-design-thinking): The following patterns are "defaults" that AIs learned from training data. Before using any of these, QUESTION them and CONSIDER ALTERNATIVES!
- [mobile-games](https://doutorai.pt/en/components/skills__mobile-games): Mobile game development principles. Touch input, battery, performance, app stores.
- [mobile_audit](https://doutorai.pt/en/components/skills__mobile-audit): if saturated_count > 10: self.warnings.append(f"[Mobile Color] {filename}: {saturated_count} highly saturated colors detected. Desaturated colors save battery o
- [mobjects](https://doutorai.pt/en/components/skills__mobjects): Mobjects (Mathematical Objects) are the building blocks of Manim animations. They represent anything that can be displayed on screen.
- [modal](https://doutorai.pt/en/components/skills__modal): Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-
- [modal-serverless-gpu](https://doutorai.pt/en/components/skills__modal-serverless-gpu): Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, o
- [model-expectations](https://doutorai.pt/en/components/skills__model-expectations): This document describes expected behavior differences across Claude models when using the session-handoff skill.
- [model-merging](https://doutorai.pt/en/components/skills__model-merging): Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific exp
- [model-migration](https://doutorai.pt/en/components/skills__model-migration): How to move existing code to newer Claude models. Covers breaking changes, deprecated parameters, and drop-in replacements for retired models.
- [model-pruning](https://doutorai.pt/en/components/skills__model-pruning): Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsi
- [model-registry](https://doutorai.pt/en/components/skills__model-registry): Complete guide to MLflow Model Registry for versioning, lifecycle management, and collaboration.
- [model_comparison](https://doutorai.pt/en/components/skills__model-comparison): Guide to different Perplexity models available through OpenRouter and when to use each.
- [model_comparison](https://doutorai.pt/en/components/skills__model-comparison-2): Utilities for comparing multiple Bayesian models using information criteria and cross-validation metrics.
- [model_diagnostics](https://doutorai.pt/en/components/skills__model-diagnostics): Comprehensive diagnostic checks for PyMC models. Run this after sampling to validate results before interpretation.
- [model_evaluation](https://doutorai.pt/en/components/skills__model-evaluation): Comprehensive guide for evaluating models, tuning hyperparameters, and selecting the best model using scikit-learn's model selection tools.
- [model_tuning](https://doutorai.pt/en/components/skills__model-tuning): Supervised Fine-Tuning or Preference Tuning using your own datasets.
- [models](https://doutorai.pt/en/components/skills__models): Guide to selecting and using sentence-transformers models.
- [models](https://doutorai.pt/en/components/skills__models-2): PyHealth provides 33+ models for healthcare prediction tasks, ranging from simple baselines to state-of-the-art deep learning architectures. Models are organize
- [models](https://doutorai.pt/en/components/skills__models-3): The transformers library provides flexible model loading with automatic architecture detection, device management, and configuration control.
- [models](https://doutorai.pt/en/components/skills__models-4): For live capability data - context window, max output tokens, feature support (thinking, vision, effort, structured outputs, etc.) - query the Models API instea
- [models-atac-seq](https://doutorai.pt/en/components/skills__models-atac-seq): This document covers models for analyzing single-cell ATAC-seq and chromatin accessibility data in scvi-tools.
- [models-multimodal](https://doutorai.pt/en/components/skills__models-multimodal): This document covers models for joint analysis of multiple data modalities in scvi-tools.
- [models-scrna-seq](https://doutorai.pt/en/components/skills__models-scrna-seq): This document covers core models for analyzing single-cell RNA sequencing data in scvi-tools.
- [models-spatial](https://doutorai.pt/en/components/skills__models-spatial): This document covers models for analyzing spatially-resolved transcriptomics data in scvi-tools.
- [models-specialized](https://doutorai.pt/en/components/skills__models-specialized): This document covers models for specialized single-cell data modalities in scvi-tools.
- [models_architectures](https://doutorai.pt/en/components/skills__models-architectures): TorchDrug provides a comprehensive collection of pre-built model architectures for various graph-based learning tasks. This reference catalogs all available mod
- [modern-minimalist](https://doutorai.pt/en/components/skills__modern-minimalist): A clean and contemporary theme with a sophisticated grayscale palette for maximum versatility.
- [module_reference](https://doutorai.pt/en/components/skills__module-reference): Comprehensive parameter reference for all gget modules.
- [modules](https://doutorai.pt/en/components/skills__modules): Complete guide to DSPy's built-in modules for language model programming.
- [moe-training](https://doutorai.pt/en/components/skills__moe-training): Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense mode
- [molecular_generation](https://doutorai.pt/en/components/skills__molecular-generation): This script demonstrates how to use TDC oracles for molecular generation tasks including goal-directed generation and distribution learning.
- [molecular_generation](https://doutorai.pt/en/components/skills__molecular-generation-2): Molecular generation involves creating novel molecular structures with desired properties. TorchDrug supports both unconditional generation (exploring chemical 
- [molecular_properties](https://doutorai.pt/en/components/skills__molecular-properties): Calculate comprehensive molecular properties and descriptors for molecules. Supports single molecules or batch processing from files.
- [molecular_property_prediction](https://doutorai.pt/en/components/skills__molecular-property-prediction): Molecular property prediction involves predicting chemical, physical, or biological properties of molecules from their structure. TorchDrug provides comprehensi
- [molfeat](https://doutorai.pt/en/components/skills__molfeat): Molecular featurization for ML (100+ featurizers). ECFP, MACCS, descriptors, pretrained models (ChemBERTa), convert SMILES to features, for QSAR and molecular M
- [monitor-explain-analyze](https://doutorai.pt/en/components/skills__monitor-explain-analyze): EXPLAIN ANALYZE executes the query and shows actual timings, revealing the true performance bottlenecks.
- [monitor-explain-analyze](https://doutorai.pt/en/components/skills__monitor-explain-analyze-2): EXPLAIN ANALYZE executes the query and shows actual timings, revealing the true performance bottlenecks.
- [monitor-pg-stat-statements](https://doutorai.pt/en/components/skills__monitor-pg-stat-statements): pg_stat_statements tracks execution statistics for all queries, helping identify slow and frequent queries.
- [monitor-pg-stat-statements](https://doutorai.pt/en/components/skills__monitor-pg-stat-statements-2): pg_stat_statements tracks execution statistics for all queries, helping identify slow and frequent queries.
- [monitor-vacuum-analyze](https://doutorai.pt/en/components/skills__monitor-vacuum-analyze): Outdated statistics cause the query planner to make poor decisions. VACUUM reclaims space, ANALYZE updates statistics.
- [monitor-vacuum-analyze](https://doutorai.pt/en/components/skills__monitor-vacuum-analyze-2): Outdated statistics cause the query planner to make poor decisions. VACUUM reclaims space, ANALYZE updates statistics.
- [monitoring](https://doutorai.pt/en/components/skills__monitoring): This guide covers techniques for collecting data and monitoring simulation behavior in SimPy.
- [monitoring](https://doutorai.pt/en/components/skills__monitoring-2): Comprehensive guide to implementing observability, metrics collection, alerting strategies, and dashboard design for IT operations.
- [monorepo](https://doutorai.pt/en/components/skills__monorepo): Railway supports two types of monorepo deployments with different configuration approaches.
- [monorepo-architect](https://doutorai.pt/en/components/skills__monorepo-architect): Expert in monorepo architecture, build systems, and dependency management at scale. Masters Nx, Turborepo, Bazel, and Lerna for efficient multi-project developm
- [monorepo-turborepo](https://doutorai.pt/en/components/skills__monorepo-turborepo): Turborepo monorepo template principles. pnpm workspaces, shared packages.
- [moodle-external-api-development](https://doutorai.pt/en/components/skills__moodle-external-api-development): Create custom external web service APIs for Moodle LMS. Use when implementing web services for course management, user tracking, quiz operations, or custom plug
- [motion-canvas](https://doutorai.pt/en/components/skills__motion-canvas): Complete production-ready guide for Motion Canvas with ESM/CommonJS workarounds, full setup templates, and troubleshooting for programmatic video creation using
- [move-code-quality](https://doutorai.pt/en/components/skills__move-code-quality): Analyzes Move language packages against the official Move Book Code Quality Checklist. Use this skill when reviewing Move code, checking Move 2024 Edition compl
- [mui](https://doutorai.pt/en/components/skills__mui): Material-UI v7 component library patterns including sx prop styling, theme integration, responsive design, and MUI-specific hooks. Use when working with MUI com
- [multi-gpu](https://doutorai.pt/en/components/skills__multi-gpu): Comprehensive guide to scaling TensorRT-LLM across multiple GPUs and nodes.
- [multi-node](https://doutorai.pt/en/components/skills__multi-node): Ray Train runs on a Ray cluster with one head node and multiple worker nodes.
- [multi-node-training](https://doutorai.pt/en/components/skills__multi-node-training): Complete guide to distributed Ray cluster training with OpenRLHF across multiple machines.
- [multiparametric](https://doutorai.pt/en/components/skills__multiparametric): PathML provides specialized support for multiparametric imaging technologies that simultaneously measure multiple markers at single-cell resolution. These techn
- [multiplayer](https://doutorai.pt/en/components/skills__multiplayer): Multiplayer game development principles. Architecture, networking, synchronization.
- [mwaskom__seaborn-2848](https://doutorai.pt/en/components/skills__mwaskom-seaborn-2848): Since I don't have access to web tools or the seaborn repository locally, I need to analyze the issue from the context provided and generate a patch based on my
- [mwaskom__seaborn-2848](https://doutorai.pt/en/components/skills__mwaskom-seaborn-2848-2): Based on my knowledge of the seaborn codebase and the issue description, I can generate the patch. The issue is in PairGrid's map methods where hue is delegated
- [mwaskom__seaborn-3010](https://doutorai.pt/en/components/skills__mwaskom-seaborn-3010): This task is about fixing a GitHub issue in the mwaskom/seaborn repository. The current directory doesn't contain the seaborn codebase. Based on the traceback, 
- [mwaskom__seaborn-3190](https://doutorai.pt/en/components/skills__mwaskom-seaborn-3190): I found the exact patch. The fix is very simple - just wrap axis.convert_units((vmin, vmax)) with map(float, ...) to ensure the values are converted to floats b
- [mwaskom__seaborn-3407](https://doutorai.pt/en/components/skills__mwaskom-seaborn-3407): Based on my knowledge of seaborn's codebase and the error described, I can create the patch. The issue is that when a DataFrame has a MultiIndex for columns, th
- [mwaskom__seaborn-3407](https://doutorai.pt/en/components/skills__mwaskom-seaborn-3407-2): The fix should flatten MultiIndex columns to string representations at the start of PairGrid.__init__.
- [n8n-code-javascript](https://doutorai.pt/en/components/skills__n8n-code-javascript): Write JavaScript code in n8n Code nodes. Use when writing JavaScript in n8n, using $input/$json/$node syntax, making HTTP requests with $helpers, working with d
- [n8n-code-python](https://doutorai.pt/en/components/skills__n8n-code-python): Write Python code in n8n Code nodes. Use when writing Python in n8n, using _input/_json/_node syntax, working with standard library, or need to understand Pytho
- [n8n-expression-syntax](https://doutorai.pt/en/components/skills__n8n-expression-syntax): Validate n8n expression syntax and fix common errors. Use when writing n8n expressions, using {{}} syntax, accessing $json/$node variables, troubleshooting expr
- [n8n-mcp-tools-expert](https://doutorai.pt/en/components/skills__n8n-mcp-tools-expert): Expert guide for using n8n-mcp MCP tools effectively. Use when searching for nodes, validating configurations, accessing templates, managing workflows, or using
- [n8n-node-configuration](https://doutorai.pt/en/components/skills__n8n-node-configuration): Operation-aware node configuration guidance. Use when configuring nodes, understanding property dependencies, determining required fields, choosing between get_
- [n8n-validation-expert](https://doutorai.pt/en/components/skills__n8n-validation-expert): Interpret validation errors and guide fixing them. Use when encountering validation errors, validation warnings, false positives, operator structure issues, or 
- [n8n-workflow-patterns](https://doutorai.pt/en/components/skills__n8n-workflow-patterns): Proven architectural patterns for building n8n workflows.
- [n8n-workflow-patterns](https://doutorai.pt/en/components/skills__n8n-workflow-patterns-2): Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, plann
- [naming-analyzer](https://doutorai.pt/en/components/skills__naming-analyzer): Suggest better variable, function, and class names based on context and conventions.
- [nanogpt](https://doutorai.pt/en/components/skills__nanogpt): Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfec
- [natbib](https://doutorai.pt/en/components/skills__natbib): %% %% This is file natbib.sty', %% generated with the docstrip utility. %% %% The original source files were: %% %% natbib.dtx (with options: package,all') %% =
- [natbib](https://doutorai.pt/en/components/skills__natbib-2): %% %% This is file natbib.sty', %% generated with the docstrip utility. %% %% The original source files were: %% %% natbib.dtx (with options: package,all') %% =
- [nature_article](https://doutorai.pt/en/components/skills__nature-article): % Abstract \begin{abstract} oindent Write a concise abstract of 150-200 words summarizing the main findings, significance, and conclusions of your work. The abs
- [navigation_sections](https://doutorai.pt/en/components/skills__navigation-sections): Zafiro provides powerful abstractions for managing application-wide navigation and modular UI sections.
- [nemo-curator](https://doutorai.pt/en/components/skills__nemo-curator): GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), 
- [nemo-evaluator-sdk](https://doutorai.pt/en/components/skills__nemo-evaluator-sdk): Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluatio
- [nemo-guardrails](https://doutorai.pt/en/components/skills__nemo-guardrails): NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filte
- [neo4j-import](https://doutorai.pt/en/components/skills__neo4j-import): Use these steps when persisting the ownership graph to Neo4j.
- [neon-auth](https://doutorai.pt/en/components/skills__neon-auth): Neon Auth provides authentication for your application. It's available as:
- [neon-cli](https://doutorai.pt/en/components/skills__neon-cli): The Neon CLI is a command-line interface for managing Neon Serverless Postgres directly from your terminal. It provides the same capabilities as the Neon Platfo
- [neon-drizzle](https://doutorai.pt/en/components/skills__neon-drizzle): Integration patterns, configurations, and optimizations for using Drizzle ORM with Neon Postgres.
- [neon-instagres](https://doutorai.pt/en/components/skills__neon-instagres): Instantly provision production-ready Postgres databases with Neon Instagres. Use when setting up databases, when users mention PostgreSQL/Postgres, database set
- [neon-js](https://doutorai.pt/en/components/skills__neon-js): The @neondatabase/neon-js SDK provides a unified client for Neon Auth and Data API. It combines authentication handling with PostgREST-compatible database queri
- [neon-platform-api](https://doutorai.pt/en/components/skills__neon-platform-api): The Neon Platform API allows you to manage Neon projects, branches, databases, and resources programmatically. You can use the REST API directly or through offi
- [neon-postgres](https://doutorai.pt/en/components/skills__neon-postgres): Expert patterns for Neon serverless Postgres, branching, connection pooling, and Prisma/Drizzle integration Use when: neon database, serverless postgres, databa
- [neon-python-sdk](https://doutorai.pt/en/components/skills__neon-python-sdk): The neon-api Python SDK is a Pythonic wrapper around the Neon REST API. It provides methods for managing all Neon resources, including projects, branches, endpo
- [neon-serverless](https://doutorai.pt/en/components/skills__neon-serverless): Patterns and best practices for connecting to Neon databases in serverless environments using the @neondatabase/serverless driver. The driver connects over HTTP
- [neon-typescript-sdk](https://doutorai.pt/en/components/skills__neon-typescript-sdk): The @neondatabase/api-client TypeScript SDK is a typed wrapper around the Neon REST API. It provides methods for managing all Neon resources, including projects
- [nestjs-expert](https://doutorai.pt/en/components/skills__nestjs-expert): Nest.js framework expert specializing in module architecture, dependency injection, middleware, guards, interceptors, testing with Jest/Supertest, TypeORM/Mongo
- [netlify](https://doutorai.pt/en/components/skills__netlify): PNG IHDR d d pT lIDATx۱r Q âC2]XiecR sl ± ,@(A]oA ~=&e E zQw $l6u4dtr F->j)/0HY( R& 2-:Z- A fF+,k |m ާO\ sK/- ב >Wȍ J 1m ADjk:ݞ$@o;@D #s6H M .i z.9 1I. r P El'q
- [netlify-deploy](https://doutorai.pt/en/components/skills__netlify-deploy): Deploy web projects to Netlify using the Netlify CLI (`npx netlify`). Use when the user asks to deploy, host, publish, or link a site/repo on Netlify, including
- [netlify-toml](https://doutorai.pt/en/components/skills__netlify-toml): Configuration file for Netlify builds and deployments.
- [network](https://doutorai.pt/en/components/skills__network): Common network and API errors with diagnosis and solutions.
- [networking](https://doutorai.pt/en/components/skills__networking): WARP users can access these IPs without VPN.
- [networks](https://doutorai.pt/en/components/skills__networks): Aeon provides neural network architectures specifically designed for time series tasks. These networks serve as building blocks for classification, regression, 
- [networkx](https://doutorai.pt/en/components/skills__networkx): Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analy
- [neurips](https://doutorai.pt/en/components/skills__neurips): % partial rewrite of the LaTeX2e package for submissions to the % Conference on Neural Information Processing Systems (NeurIPS): % % - uses more LaTeX conventio
- [neurips_article](https://doutorai.pt/en/components/skills__neurips-article): \begin{abstract} Write a concise abstract (150-250 words) summarizing your contributions. The abstract should clearly state: (1) the problem you address, (2) yo
- [neurokit2](https://doutorai.pt/en/components/skills__neurokit2): Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when process
- [neuropixels-analysis](https://doutorai.pt/en/components/skills__neuropixels-analysis): Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation
- [neuropixels_pipeline](https://doutorai.pt/en/components/skills__neuropixels-pipeline): Based on SpikeInterface, Allen Institute, and IBL recommendations.
- [nextjs-app-router-patterns](https://doutorai.pt/en/components/skills__nextjs-app-router-patterns): Comprehensive patterns for Next.js 14+ App Router architecture, Server Components, and modern full-stack React development.
- [nextjs-best-practices](https://doutorai.pt/en/components/skills__nextjs-best-practices): Next.js App Router principles. Server Components, data fetching, routing patterns.
- [nextjs-fullstack](https://doutorai.pt/en/components/skills__nextjs-fullstack): Next.js full-stack template principles. App Router, Prisma, Tailwind.
- [nextjs-saas](https://doutorai.pt/en/components/skills__nextjs-saas): Next.js SaaS template principles. Auth, payments, email.
- [nextjs-static](https://doutorai.pt/en/components/skills__nextjs-static): Next.js static site template principles. Landing pages, portfolios, marketing.
- [nextjs-supabase-auth](https://doutorai.pt/en/components/skills__nextjs-supabase-auth): Expert integration of Supabase Auth with Next.js App Router Use when: supabase auth next, authentication next.js, login supabase, auth middleware, protected rou
- [nextjs_optimization_guide](https://doutorai.pt/en/components/skills__nextjs-optimization-guide): This reference guide provides comprehensive information for senior frontend.
- [nih_guidelines](https://doutorai.pt/en/components/skills__nih-guidelines): NIH proposals are evaluated using scored criteria (1-9 scale, 1 = exceptional, 9 = poor) and additional review considerations (not scored but discussed).
- [nih_specific_aims](https://doutorai.pt/en/components/skills__nih-specific-aims): extbf{[Disease/condition]} affects extbf{[number]} people worldwide and results in extbf{[burden: mortality, morbidity, cost]}. extbf{[Current treatment/underst
- [nih_specific_aims_template](https://doutorai.pt/en/components/skills__nih-specific-aims-template): This hypothesis is based on [rationale]: our preliminary data showing [key finding 1], [key finding 2], and [key finding 3] (Figures 1-2, Table 1). [Why this ev
- [nnsight-remote-interpretability](https://doutorai.pt/en/components/skills__nnsight-remote-interpretability): Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpre
- [node](https://doutorai.pt/en/components/skills__node): The Node endpoint retrieves property relationships and values from the Data Commons knowledge graph. It returns information about directed edges (properties) co
- [node_mcp_server](https://doutorai.pt/en/components/skills__node-mcp-server): This document provides Node/TypeScript-specific best practices and examples for implementing MCP servers using the MCP TypeScript SDK. It covers project structu
- [nodejs](https://doutorai.pt/en/components/skills__nodejs): Common Node.js errors with diagnosis and solutions.
- [nodejs-backend-patterns](https://doutorai.pt/en/components/skills__nodejs-backend-patterns): Comprehensive guidance for building scalable, maintainable, and production-ready Node.js backend applications with modern frameworks, architectural patterns, an
- [nodejs-best-practices](https://doutorai.pt/en/components/skills__nodejs-best-practices): Node.js development principles and decision-making. Framework selection, async patterns, security, and architecture. Teaches thinking, not copying.
- [noise](https://doutorai.pt/en/components/skills__noise): This guide covers noise models, noisy simulation, characterization, and error mitigation in Cirq.
- [normalization_methods](https://doutorai.pt/en/components/skills__normalization-methods): This document explains the various normalization methods available in deepTools and when to use each one.
- [nosql-expert](https://doutorai.pt/en/components/skills__nosql-expert): Expert guidance for distributed NoSQL databases (Cassandra, DynamoDB). Focuses on mental models, query-first modeling, single-table design, and avoiding hot par
- [notebook-structure](https://doutorai.pt/en/components/skills__notebook-structure): Prefer scaffolding from the bundled templates or new_notebook.py (for example, $CODEX_HOME/skills/jupyter-notebook/scripts/new_notebook.py) instead of hand-auth
- [notebook_manager](https://doutorai.pt/en/components/skills__notebook-manager): elif args.command == 'list': notebooks = library.list_notebooks() if notebooks: print(" 📚 Notebook Library:") for notebook in notebooks: active = " [ACTIVE]" if
- [notebook_operations](https://doutorai.pt/en/components/skills__notebook-operations): Utilities for listing, backing up, and managing LabArchives notebooks.
- [notebooklm](https://doutorai.pt/en/components/skills__notebooklm): Use this skill to query your Google NotebookLM notebooks directly from Claude Code for source-grounded, citation-backed answers from Gemini. Browser automation,
- [notion-knowledge-capture](https://doutorai.pt/en/components/skills__notion-knowledge-capture): Capture conversations and decisions into structured Notion pages; use when turning chats/notes into wiki entries, how-tos, decisions, or FAQs with proper linkin
- [notion-meeting-intelligence](https://doutorai.pt/en/components/skills__notion-meeting-intelligence): Prepare meeting materials with Notion context and Codex research; use when gathering context, drafting agendas/pre-reads, and tailoring materials to attendees.
- [notion-research-documentation](https://doutorai.pt/en/components/skills__notion-research-documentation): Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or rep
- [notion-spec-to-implementation](https://doutorai.pt/en/components/skills__notion-spec-to-implementation): Turn Notion specs into implementation plans, tasks, and progress tracking; use when implementing PRDs/feature specs and creating Notion plans + tasks from them.
- [notion-template-business](https://doutorai.pt/en/components/skills__notion-template-business): Expert in building and selling Notion templates as a business - not just making templates, but building a sustainable digital product business. Covers template 
- [nowait-reasoning-optimizer](https://doutorai.pt/en/components/skills__nowait-reasoning-optimizer): Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Q
- [nowait_processor](https://doutorai.pt/en/components/skills__nowait-processor): Implements the NOWAIT technique for efficient reasoning by suppressing self-reflection tokens during inference.
- [nsf_project_summary_template](https://doutorai.pt/en/components/skills__nsf-project-summary-template): This should be accessible to a broad audience including non-scientists. Avoid jargon.]
- [nsf_proposal_template](https://doutorai.pt/en/components/skills__nsf-proposal-template): \subsection{Overview} Provide a concise 1-2 paragraph description of the proposed research. This should be understandable to a scientifically literate reader wh
- [nuxt-app](https://doutorai.pt/en/components/skills__nuxt-app): Nuxt 3 full-stack template. Vue 3, Pinia, Tailwind, Prisma.
- [oauth-proxy](https://doutorai.pt/en/components/skills__oauth-proxy): The OAuth proxy enables FastMCP servers to authenticate with OAuth providers that don't support Dynamic Client Registration (DCR). This includes virtually all t
- [object_detection_optimization](https://doutorai.pt/en/components/skills__object-detection-optimization): World-class object detection optimization for senior computer vision engineer.
- [objects_interface](https://doutorai.pt/en/components/skills__objects-interface): The seaborn.objects interface provides a modern, declarative API for building visualizations through composition. This guide covers the complete objects interfa
- [observability-engineer](https://doutorai.pt/en/components/skills__observability-engineer): Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response wo
- [observation](https://doutorai.pt/en/components/skills__observation): The Observation API retrieves statistical observations - data points linking entities, variables, and specific dates. Examples include: - "USA population in 202
- [obsidian-bases](https://doutorai.pt/en/components/skills__obsidian-bases): Create and edit Obsidian Bases (.base files) with views, filters, formulas, and summaries. Use when working with .base files, creating database-like views of no
- [obsidian-clipper-template-creator](https://doutorai.pt/en/components/skills__obsidian-clipper-template-creator): Guide for creating templates for the Obsidian Web Clipper. Use when you want to create a new clipping template, understand available variables, or format clippe
- [obsidian-markdown](https://doutorai.pt/en/components/skills__obsidian-markdown): Create and edit Obsidian Flavored Markdown with wikilinks, embeds, callouts, properties, and other Obsidian-specific syntax. Use when working with .md files in 
- [ocean-depths](https://doutorai.pt/en/components/skills__ocean-depths): A professional and calming maritime theme that evokes the serenity of deep ocean waters.
- [offer-comparison-analyzer](https://doutorai.pt/en/components/skills__offer-comparison-analyzer): Compare multiple job offers side-by-side with total compensation analysis. Use when the user has multiple offers and wants to compare salary, equity, benefits, 
- [official-themes](https://doutorai.pt/en/components/skills__official-themes): Explanation of the 3 official themes included in Marp Core.
- [oidc-proxy](https://doutorai.pt/en/components/skills__oidc-proxy): The OIDC proxy enables FastMCP servers to authenticate with OIDC providers that don't support Dynamic Client Registration (DCR) out of the box. This includes OA
- [omero-integration](https://doutorai.pt/en/components/skills__omero-integration): Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content sc
- [onboarding-cro](https://doutorai.pt/en/components/skills__onboarding-cro): When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flo
- [one_page_treatment_plan](https://doutorai.pt/en/components/skills__one-page-treatment-plan): \section{RATIONALE} {\small [1-3 sentences explaining why this regimen is appropriate for this patient. Include key pathophysiology, guideline alignment, or mol
- [online-rl](https://doutorai.pt/en/components/skills__online-rl): Guide to online reinforcement learning with PPO, GRPO, RLOO, and OnlineDPO.
- [ontologies](https://doutorai.pt/en/components/skills__ontologies): This document covers biological ontology management in LaminDB through the Bionty plugin, including accessing, searching, and annotating data with standardized 
- [ooxml](https://doutorai.pt/en/components/skills__ooxml): Note: Don't renumber remaining slides - keep their original IDs and filenames.
- [ooxml](https://doutorai.pt/en/components/skills__ooxml-2): Use the Document class from scripts/document.py for all tracked changes and comments. It automatically handles infrastructure setup (people.xml, RSIDs, settings
- [ooxml](https://doutorai.pt/en/components/skills__ooxml-3): Note: Don't renumber remaining slides - keep their original IDs and filenames.
- [ooxml](https://doutorai.pt/en/components/skills__ooxml-4): Use the Document class from scripts/document.py for all tracked changes and comments. It automatically handles infrastructure setup (people.xml, RSIDs, settings
- [openai](https://doutorai.pt/en/components/skills__openai-10): interface: display_name: "Audio Transcribe" short_description: "Transcribe audio using OpenAI, with optional speaker diarization when requested. Prefer the bund
- [openai](https://doutorai.pt/en/components/skills__openai-21): interface: display_name: "Vercel Deploy" short_description: "Deploy apps and agents with zero configuration on Vercel. No account required." icon_small: "./asse
- [openai-patterns](https://doutorai.pt/en/components/skills__openai-patterns): Research-backed patterns from OpenAI's Agents SDK, Deep Research, and autonomous agent frameworks.
- [openalex-database](https://doutorai.pt/en/components/skills__openalex-database): Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, fin
- [openalex_client](https://doutorai.pt/en/components/skills__openalex-client): if response.status_code == 200: return response.json() elif response.status_code == 403: # Rate limited wait_time = 2 attempt print(f"Rate limited. Waiting {wai
- [openapi-to-typescript](https://doutorai.pt/en/components/skills__openapi-to-typescript): Converts OpenAPI 3.0 JSON/YAML to TypeScript interfaces and type guards. This skill should be used when the user asks to generate types from OpenAPI, convert sc
- [openrlhf-training](https://doutorai.pt/en/components/skills__openrlhf-training): High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× fast
- [openrouter_setup](https://doutorai.pt/en/components/skills__openrouter-setup): Complete guide to setting up and using OpenRouter for Perplexity model access.
- [opentargets-database](https://doutorai.pt/en/components/skills__opentargets-database): Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeu
- [opentelemetry](https://doutorai.pt/en/components/skills__opentelemetry): FastMCP includes native OpenTelemetry instrumentation for observability. Traces are automatically generated for tool, prompt, resource, and resource template op
- [opentrons-integration](https://doutorai.pt/en/components/skills__opentrons-integration): Lab automation platform for Flex/OT-2 robots. Write Protocol API v2 protocols, liquid handling, hardware modules (heater-shaker, thermocycler), labware manageme
- [operations](https://doutorai.pt/en/components/skills__operations): This document outlines the rules for managing and monitoring long-running operations in Neon, including branch creation and compute management.
- [operations](https://doutorai.pt/en/components/skills__operations-2): This reference covers all common Polars operations with comprehensive examples.
- [operators](https://doutorai.pt/en/components/skills__operators): Comprehensive reference for genetic operators in pymoo.
- [optimization](https://doutorai.pt/en/components/skills__optimization): Comprehensive guide to optimizing LLM inference with TensorRT-LLM.
- [optimization](https://doutorai.pt/en/components/skills__optimization-2): Maximize llama.cpp inference speed and efficiency.
- [optimization](https://doutorai.pt/en/components/skills__optimization-3): Reuse computed KV cache for common prompt prefixes.
- [optimize-prompt](https://doutorai.pt/en/components/skills__optimize-prompt): Automatically test and optimize prompts using A/B testing and metrics tracking.
- [optimizers](https://doutorai.pt/en/components/skills__optimizers): Complete guide to DSPy's optimization algorithms for improving prompts and model weights.
- [optimizing-attention-flash](https://doutorai.pt/en/components/skills__optimizing-attention-flash): Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>
- [oracles](https://doutorai.pt/en/components/skills__oracles): Oracles are functions that evaluate the quality of generated molecules across specific dimensions. TDC provides 17+ oracle functions for molecular optimization 
- [organizations](https://doutorai.pt/en/components/skills__organizations): This section provides rules for managing organizations, their members, invitations, and organization API keys. Organizations allow multiple users to collaborate
- [oss](https://doutorai.pt/en/components/skills__oss): Use this template for projects intended for public use and contribution.
- [other](https://doutorai.pt/en/components/skills__other): LLaMA Factory is an easy-to-use and efficient platform for training and fine-tuning large language models. With LLaMA Factory, you can fine-tune hundreds of pre
- [other](https://doutorai.pt/en/components/skills__other-2): Mixed precision training uses lower precision data types to reduce memory usage and increase training speed while maintaining model quality. Axolotl supports se
- [other](https://doutorai.pt/en/components/skills__other-3): The implementation of torch.nn.parallel.DistributedDataParallel evolves over time. This design note is written based on the state as of v1.4.
- [other](https://doutorai.pt/en/components/skills__other-4): Training advanced deep learning models is challenging. Beyond model design, model scientists also need to set up the state-of-the-art training techniques such a
- [other](https://doutorai.pt/en/components/skills__other-5): This reference covers FDA substance-related and other specialized API endpoints accessible through openFDA.
- [outcome_analysis](https://doutorai.pt/en/components/skills__outcome-analysis): Rigorous outcome analysis is essential for clinical decision support documents. This guide covers survival analysis, response assessment, statistical testing, a
- [outlines](https://doutorai.pt/en/components/skills__outlines): Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize in
- [output-patterns](https://doutorai.pt/en/components/skills__output-patterns): Use these patterns when skills need to produce consistent, high-quality output.
- [output-patterns](https://doutorai.pt/en/components/skills__output-patterns-2): Use these patterns when skills need to produce consistent, high-quality output.
- [output_analysis](https://doutorai.pt/en/components/skills__output-analysis): FluidSim automatically saves several types of output during simulations.
- [overlap](https://doutorai.pt/en/components/skills__overlap): The overlaprs module provides efficient overlap detection between genomic intervals using the Integrated Genome Database (IGD) data structure.
- [package](https://doutorai.pt/en/components/skills__package): Roier SEO Skill - Lighthouse audit scripts
- [package](https://doutorai.pt/en/components/skills__package-2): General-purpose browser automation with Playwright for Claude Code with auto-detection and smart test management
- [package](https://doutorai.pt/en/components/skills__package-4): Todo app backend with Express and SQLite
- [page-cro](https://doutorai.pt/en/components/skills__page-cro): When the user wants to optimize, improve, or increase conversions on any marketing page - including homepage, landing pages, pricing pages, feature pages, or bl
- [pagination](https://doutorai.pt/en/components/skills__pagination): When a server exposes many tools, resources, or prompts, returning them all in a single response can be impractical. MCP supports pagination for list operations
- [paid-ads](https://doutorai.pt/en/components/skills__paid-ads): When the user wants help with paid advertising campaigns on Google Ads, Meta (Facebook/Instagram), LinkedIn, Twitter/X, or other ad platforms. Also use when the
- [pain_management_plan](https://doutorai.pt/en/components/skills__pain-management-plan): extbf{Realistic Expectations Discussed}: Complete pain elimination unlikely; goal is meaningful pain reduction and improved function.
- [pallets__flask-4045](https://doutorai.pt/en/components/skills__pallets-flask-4045): It seems the Flask repository is not in the current working directory. Since this is a GitHub issue from pallets/flask, I need to work with the Flask source cod
- [pallets__flask-4045](https://doutorai.pt/en/components/skills__pallets-flask-4045-2): This appears to be a question about generating a patch for the Flask repository (pallets/flask), but I'm in a different repository. Based on the architect's ana
- [pallets__flask-4992](https://doutorai.pt/en/components/skills__pallets-flask-4992): Based on the GitHub issue, I need to generate a patch for Flask's Config.from_file() method. The issue asks to add a file mode parameter (suggested as text=True
- [pallets__flask-4992](https://doutorai.pt/en/components/skills__pallets-flask-4992-2): Based on the issue description and standard Flask codebase knowledge, I'll generate the patch. The from_file method in Flask's Config class typically opens file
- [pallets__flask-5063](https://doutorai.pt/en/components/skills__pallets-flask-5063): Based on the context and the GitHub issue, I need to generate a patch for the Flask routes command to show subdomain information. Let me construct this patch ba
- [pallets__flask-5063](https://doutorai.pt/en/components/skills__pallets-flask-5063-2): This appears to be a benchmark repository, not the Flask source code. I need to generate a patch for the Flask repository (pallets/flask). Let me look at the Fl
- [pandas_migration](https://doutorai.pt/en/components/skills__pandas-migration): This guide helps you migrate from pandas to Polars with comprehensive operation mappings and key differences.
- [paper-2-web](https://doutorai.pt/en/components/skills__paper-2-web): This skill should be used when converting academic papers into promotional and presentation formats including interactive websites (Paper2Web), presentation vid
- [paper2poster](https://doutorai.pt/en/components/skills__paper2poster): Paper2Poster automatically generates professional academic posters from research papers. The system extracts key content, designs visually appealing layouts, an
- [paper2video](https://doutorai.pt/en/components/skills__paper2video): Paper2Video generates presentation videos from LaTeX sources, transforming academic papers into engaging video presentations. The system processes papers throug
- [paper2web](https://doutorai.pt/en/components/skills__paper2web): Paper2Web converts academic papers into interactive, explorable academic homepages. Unlike traditional approaches (direct generation, template-based, or HTML co
- [parallel-agents](https://doutorai.pt/en/components/skills__parallel-agents): Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple
- [parallelism-guide](https://doutorai.pt/en/components/skills__parallelism-guide): Complete guide to parallelism in Megatron-Core: when to use each strategy, performance characteristics, and configuration examples.
- [parameters](https://doutorai.pt/en/components/skills__parameters): The Parameters object is hierarchical and organized into logical groups. Access using dot notation:
- [parameters_reference](https://doutorai.pt/en/components/skills__parameters-reference): This document provides comprehensive details on all DiffDock configuration parameters and command-line options.
- [parsing-techniques](https://doutorai.pt/en/components/skills__parsing-techniques): Complete guide to parsing .claude/plugin-name.local.md files in bash scripts.
- [patent_search](https://doutorai.pt/en/components/skills__patent-search): Provides functions for searching and retrieving patent data using the USPTO PatentSearch API (ElasticSearch-based system, replaced legacy PatentsView in May 202
- [patentsearch_api](https://doutorai.pt/en/components/skills__patentsearch-api): The PatentSearch API is USPTO's modern ElasticSearch-based patent search system that replaced the legacy PatentsView API in May 2025. It provides access to pate
- [pathml](https://doutorai.pt/en/components/skills__pathml): Computational pathology toolkit for analyzing whole-slide images (WSI) and multiparametric imaging data. Use this skill when working with histopathology slides,
- [pathology_report_template](https://doutorai.pt/en/components/skills__pathology-report-template): Example: "72-year-old woman with enlarging pigmented lesion on left forearm. Clinical concern for melanoma. Previous biopsy showed atypical melanocytic prolifer
- [patient_cohort_analysis](https://doutorai.pt/en/components/skills__patient-cohort-analysis): Patient cohort analysis involves systematically studying groups of patients to identify patterns, compare outcomes, and derive clinical insights. In pharmaceuti
- [patient_documentation](https://doutorai.pt/en/components/skills__patient-documentation): SOAP (Subjective, Objective, Assessment, Plan) is the standard format for progress notes in clinical practice.
- [patterns](https://doutorai.pt/en/components/skills__patterns): Qiskit Patterns provide a general framework for solving domain-specific quantum computing problems in four stages: Map, Optimize, Execute, and Post-process.
- [patterns](https://doutorai.pt/en/components/skills__patterns-2): This reference provides common, proven patterns for implementing Claude Code hooks. Use these patterns as starting points for typical hook use cases.
- [patterns](https://doutorai.pt/en/components/skills__patterns-4): Custom tools require an MCP server. Use ClaudeSDKClient for full control (custom SDK MCP tools require ClaudeSDKClient - query() only supports external stdio/ht
- [patterns](https://doutorai.pt/en/components/skills__patterns-5): Lightweight unit testing - provides bindings without full Worker runtime.
- [patterns](https://doutorai.pt/en/components/skills__patterns-6): Use Worker's hostname for efficient caching - avoid overriding hostname unnecessarily.
- [patterns](https://doutorai.pt/en/components/skills__patterns-12): Architecture patterns, multi-environment setups, and real-world use cases.
- [patterns](https://doutorai.pt/en/components/skills__patterns-16): See durable-objects skill for full implementation.
- [patterns](https://doutorai.pt/en/components/skills__patterns-17): Production-ready patterns for implementing Cloudflare TURN in WebRTC applications.
- [patterns](https://doutorai.pt/en/components/skills__patterns-18): Real-world patterns and examples for TCP Sockets in Cloudflare Workers.
- [patterns](https://doutorai.pt/en/components/skills__patterns-19): Common patterns, use cases, and integration examples for R2 SQL.
- [patterns](https://doutorai.pt/en/components/skills__patterns-20): See README.md, configuration.md, api.md.
- [patterns](https://doutorai.pt/en/components/skills__patterns-25): Change env.SECRET (direct) to await env.SECRET.get() (async).
- [patterns](https://doutorai.pt/en/components/skills__patterns-27): For Cloudflare for SaaS: Store worker name in custom hostname custom_metadata, retrieve in dispatch worker to route requests. Requires custom hostnames as subdo
- [patterns](https://doutorai.pt/en/components/skills__patterns-28): Practical patterns for R2 Data Catalog with PyIceberg.
- [patterns](https://doutorai.pt/en/components/skills__patterns-30): Email submission (port 587). WARNING: See gotchas.md
- [patterns](https://doutorai.pt/en/components/skills__patterns-31): Test files must be included in modules = [...] config.
- [patterns](https://doutorai.pt/en/components/skills__patterns-33): C3 detects workspace config (package.json workspaces or pnpm-workspace.yaml).
- [patterns](https://doutorai.pt/en/components/skills__patterns-37): Pipelines are immutable. Use versioning:
- [patterns](https://doutorai.pt/en/components/skills__patterns-48): Run same config on multiple machines. Cloudflare automatically load balances. Long-lived connections (WebSocket, SSH) may drop during updates.
- [patterns](https://doutorai.pt/en/components/skills__patterns-52): High priority: max_batch_size: 5, max_batch_timeout: 1. Low priority: max_batch_size: 100, max_batch_timeout: 30.
- [patterns](https://doutorai.pt/en/components/skills__patterns-53): Common workflows, full-stack flows, and best practices.
- [patterns](https://doutorai.pt/en/components/skills__patterns-55): Dashboard → Core Web Vitals → Click metric → Debug View shows top 5 problematic elements.
- [patterns](https://doutorai.pt/en/components/skills__patterns-57): Single DO ~1K req/s max. Shard for higher throughput:
- [patterns](https://doutorai.pt/en/components/skills__patterns-59): For deprecated frameworks, see gotchas.md for migration options.
- [patterns](https://doutorai.pt/en/components/skills__patterns-63): Layered security stack: DDoS + WAF + Rate Limiting + Bot Management.
- [patterns](https://doutorai.pt/en/components/skills__patterns-64): The RefreshableCollection pattern is used to manage lists that can be refreshed via a command, maintaining an internal SourceCache/SourceList and exposing a Rea
- [patterns](https://doutorai.pt/en/components/skills__patterns-65): This document describes the common patterns for slash commands, helping you choose the right structure for your workflow.
- [paywall-upgrade-cro](https://doutorai.pt/en/components/skills__paywall-upgrade-cro): When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade
- [pc-games](https://doutorai.pt/en/components/skills__pc-games): PC and console game development principles. Engine selection, platform features, optimization strategies.
- [pcr_setup_template](https://doutorai.pt/en/components/skills__pcr-setup-template): This template demonstrates how to set up PCR reactions using the Thermocycler module. Includes master mix distribution, sample addition, and PCR cycling.
- [pdb-database](https://doutorai.pt/en/components/skills__pdb-database): Access RCSB PDB for 3D protein/nucleic acid structures. Search by text/sequence/structure, download coordinates (PDB/mmCIF), retrieve metadata, for structural b
- [pdf](https://doutorai.pt/en/components/skills__pdf): PDF manipulation toolkit. Extract text/tables, create PDFs, merge/split, fill forms, for programmatic document processing and analysis.
- [pdf](https://doutorai.pt/en/components/skills__pdf-2): Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple P
- [pdf](https://doutorai.pt/en/components/skills__pdf-3): Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple P
- [pdf](https://doutorai.pt/en/components/skills__pdf-4): Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple P
- [pdf-fill-studio](https://doutorai.pt/en/components/skills__pdf-fill-studio): Fill any PDF locally and place each value precisely in a visual editor. Use when the user wants to fill out a PDF form, enter data into a PDF, complete a tax/in
- [pdf-official](https://doutorai.pt/en/components/skills__pdf-official): Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple P
- [pdf_to_images](https://doutorai.pt/en/components/skills__pdf-to-images): Converts presentation PDFs to images for visual inspection and review. Supports multiple output formats and resolutions.
- [peds_api](https://doutorai.pt/en/components/skills__peds-api): The Patent Examination Data System (PEDS) provides access to USPTO patent application and filing status records. It contains bibliographic data, published docum
- [peds_client](https://doutorai.pt/en/components/skills__peds-client): Provides functions for retrieving patent examination data using the uspto-opendata-python library.
- [peer-review](https://doutorai.pt/en/components/skills__peer-review): Systematic peer review toolkit. Evaluate methodology, statistics, design, reproducibility, ethics, figure integrity, reporting standards, for manuscript and gra
- [peer_review_standards](https://doutorai.pt/en/components/skills__peer-review-standards): Peer review ensures that clinical manuscripts meet standards for scientific rigor, ethical conduct, and clear communication before publication.
- [peft-fine-tuning](https://doutorai.pt/en/components/skills__peft-fine-tuning): Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need t
- [penetration_testing_guide](https://doutorai.pt/en/components/skills__penetration-testing-guide): This reference guide provides comprehensive information for senior security.
- [pennylane](https://doutorai.pt/en/components/skills__pennylane): Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automa
- [performance](https://doutorai.pt/en/components/skills__performance-2): This reference covers Vaex's performance features including lazy evaluation, caching, memory management, async operations, and optimization strategies for proce
- [performance](https://doutorai.pt/en/components/skills__performance-3): Patterns for optimizing React component performance, preventing unnecessary re-renders, and avoiding memory leaks.
- [performance](https://doutorai.pt/en/components/skills__performance-4): Optimize web performance for faster loading and better user experience. Use when asked to "speed up my site", "optimize performance", "reduce load time", "fix s
- [performance-optimizer](https://doutorai.pt/en/components/skills__performance-optimizer): Identifies and fixes performance bottlenecks in code, databases, and APIs. Measures before and after to prove improvements.
- [performance-profiling](https://doutorai.pt/en/components/skills__performance-profiling): Performance profiling principles. Measurement, analysis, and optimization techniques.
- [perioperative_care_plan](https://doutorai.pt/en/components/skills__perioperative-care-plan): extbf{HIPAA Notice}: De-identify all protected health information before sharing.
- [perplexity](https://doutorai.pt/en/components/skills__perplexity): Web search and research using Perplexity AI. Use when user says "search", "find", "look up", "ask", "research", or "what's the latest" for generic queries. NOT 
- [perplexity-search](https://doutorai.pt/en/components/skills__perplexity-search): Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web sea
- [perplexity_search](https://doutorai.pt/en/components/skills__perplexity-search-2): This script performs AI-powered web searches using Perplexity models through LiteLLM and OpenRouter. It provides real-time, grounded answers with source citatio
- [personal](https://doutorai.pt/en/components/skills__personal): Use this template for side projects, portfolio pieces, and experiments. Balance between documenting for future-you and showcasing for others.
- [personal-tool-builder](https://doutorai.pt/en/components/skills__personal-tool-builder): Expert in building custom tools that solve your own problems first. The best products often start as personal tools - scratch your own itch, build for yourself,
- [persuasion-principles](https://doutorai.pt/en/components/skills__persuasion-principles): LLMs respond to the same persuasion principles as humans. Understanding this psychology helps you design more effective skills - not to manipulate, but to ensur
- [persuasion-principles](https://doutorai.pt/en/components/skills__persuasion-principles-2): LLMs respond to the same persuasion principles as humans. Understanding this psychology helps you design more effective skills - not to manipulate, but to ensur
- [phase_diagram_generator](https://doutorai.pt/en/components/skills__phase-diagram-generator): This script generates phase diagrams for chemical systems using data from the Materials Project database via pymatgen's MPRester.
- [phoenix-observability](https://doutorai.pt/en/components/skills__phoenix-observability): Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations
- [photo-avatars](https://doutorai.pt/en/components/skills__photo-avatars): Creating avatars from photos (talking photos) for HeyGen
- [php-pro](https://doutorai.pt/en/components/skills__php-pro): Write idiomatic PHP code with generators, iterators, SPL data
- [phylogenetics](https://doutorai.pt/en/components/skills__phylogenetics): Bio.Phylo provides a unified toolkit for reading, writing, analyzing, and visualizing phylogenetic trees. It supports multiple file formats including Newick, NE
- [physics-mechanics](https://doutorai.pt/en/components/skills__physics-mechanics): This document covers SymPy's physics modules including classical mechanics, quantum mechanics, vector analysis, units, optics, continuum mechanics, and control 
- [pinecone](https://doutorai.pt/en/components/skills__pinecone): Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Lo
- [pipeline](https://doutorai.pt/en/components/skills__pipeline): Complete guide to normalizers, pre-tokenizers, models, post-processors, and decoders.
- [pipelines](https://doutorai.pt/en/components/skills__pipelines): Pipelines provide the simplest way to use pre-trained models for inference. They abstract away tokenization, model loading, and post-processing, offering a unif
- [pipelines_and_composition](https://doutorai.pt/en/components/skills__pipelines-and-composition): Pipelines chain multiple processing steps into a single estimator, preventing data leakage and simplifying code. They enable reproducible workflows and seamless
- [plaid-fintech](https://doutorai.pt/en/components/skills__plaid-fintech): Expert patterns for Plaid API integration including Link token flows, transactions sync, identity verification, Auth for ACH, balance checks, webhook handling, 
- [plan-writing](https://doutorai.pt/en/components/skills__plan-writing): Structured task planning with clear breakdowns, dependencies, and verification criteria. Use when implementing features, refactoring, or any multi-step work.
- [planning](https://doutorai.pt/en/components/skills__planning): Create and manage persistent markdown planning files for structured task execution. Use when the user asks to "create a plan", "track progress", "start a resear
- [planning-with-files](https://doutorai.pt/en/components/skills__planning-with-files): Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, re
- [planning-with-files](https://doutorai.pt/en/components/skills__planning-with-files-2): Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-
- [playwright](https://doutorai.pt/en/components/skills__playwright-2): PNG IHDR d d IDATx1oF EQ.YX "1At+$0 { K3 ]M dkt@@ q P Z҉d ! yǣ ~#u>߽{GQ} (n , Zּ $I4Z_t~? r:YmK8O(d T q ۓw ׯ[ O "5 'DRY$d2 $ZhL&ƎY1u4M]yT뺲,,I( ~EHq ÇS j z~ԩňan
- [playwright-e2e-builder](https://doutorai.pt/en/components/skills__playwright-e2e-builder): Plan and build comprehensive Playwright E2E test suites with Page Object Model, authentication state persistence, custom fixtures, visual regression, and CI int
- [playwright-java](https://doutorai.pt/en/components/skills__playwright-java): Scaffold, write, debug, and enhance enterprise-grade Playwright E2E tests in Java using Page Object Model, JUnit 5, Allure reporting, and parallel execution.
- [playwright-skill](https://doutorai.pt/en/components/skills__playwright-skill): Complete browser automation with Playwright. Auto-detects dev servers, writes clean test scripts to /tmp. Test pages, fill forms, take screenshots, check respon
- [plos_one](https://doutorai.pt/en/components/skills__plos-one): % Corresponding author \affil[$\dagger$]{Corresponding author. E-mail: [email protected]}
- [plot_template](https://doutorai.pt/en/components/skills__plot-template): Comprehensive template demonstrating various plot types and best practices. Use this as a starting point for creating publication-quality visualizations.
- [plot_types](https://doutorai.pt/en/components/skills__plot-types): Comprehensive guide to different plot types in matplotlib with examples and use cases.
- [plotly](https://doutorai.pt/en/components/skills__plotly): Interactive scientific and statistical data visualization library for Python. Use when creating charts, plots, or visualizations including scatter plots, line c
- [plotly-express](https://doutorai.pt/en/components/skills__plotly-express): Plotly Express (px) is a high-level interface for creating data visualizations with minimal code (typically 1-5 lines).
- [plots](https://doutorai.pt/en/components/skills__plots): This document provides comprehensive information about all SHAP plotting functions, their parameters, use cases, and best practices for visualizing model explan
- [plotting_guide](https://doutorai.pt/en/components/skills__plotting-guide): Comprehensive guide for creating publication-quality visualizations with scanpy.
- [plotting_guide](https://doutorai.pt/en/components/skills__plotting-guide-2): Comprehensive guide for creating publication-quality visualizations from Neuropixels data.
- [plugin-commands](https://doutorai.pt/en/components/skills__plugin-commands): Practical examples of commands designed for Claude Code plugins, demonstrating plugin-specific patterns and features.
- [plugin-features-reference](https://doutorai.pt/en/components/skills__plugin-features-reference): This reference covers features and patterns specific to commands bundled in Claude Code plugins.
- [plugin-forge](https://doutorai.pt/en/components/skills__plugin-forge): Create and manage Claude Code plugins with proper structure, manifests, and marketplace integration. Use when creating plugins for a marketplace, adding plugin 
- [polars](https://doutorai.pt/en/components/skills__polars): Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis wor
- [policies](https://doutorai.pt/en/components/skills__policies): PufferLib policies are standard PyTorch modules with optional utilities for observation processing and LSTM integration. The framework provides default architec
- [popup-cro](https://doutorai.pt/en/components/skills__popup-cro): When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent,
- [portfolio-case-study-writer](https://doutorai.pt/en/components/skills__portfolio-case-study-writer): Transform resume bullets into detailed portfolio case studies with context, action, and outcome. Use when the user mentions portfolio, case study, project write
- [post-deploy-checks](https://doutorai.pt/en/components/skills__post-deploy-checks): Use this after any deploy or service creation. Keep it short; stop when a check fails.
- [poster_content_guide](https://doutorai.pt/en/components/skills__poster-content-guide): Content is king in research posters. This guide covers writing strategies, section-specific guidance, visual-text balance, and best practices for communicating 
- [poster_content_guide](https://doutorai.pt/en/components/skills__poster-content-guide-2): Content is king in research posters. This guide covers writing strategies, section-specific guidance, visual-text balance, and best practices for communicating 
- [poster_design_principles](https://doutorai.pt/en/components/skills__poster-design-principles): Effective poster design balances visual appeal, readability, and scientific content. This guide covers typography, color theory, visual hierarchy, accessibility
- [poster_design_principles](https://doutorai.pt/en/components/skills__poster-design-principles-2): Effective poster design balances visual appeal, readability, and scientific content. This guide covers typography, color theory, visual hierarchy, accessibility
- [poster_html_template](https://doutorai.pt/en/components/skills__poster-html-template): Introduction Background: Context about your research problem. Objective: What you aimed to accomplish. Methods Study design overview Key methodology Analysis ap
- [poster_layout_design](https://doutorai.pt/en/components/skills__poster-layout-design): Effective poster layout organizes content for maximum impact and comprehension. This guide covers grid systems, spatial organization, visual flow, and layout pa
- [poster_layout_design](https://doutorai.pt/en/components/skills__poster-layout-design-2): Effective poster layout organizes content for maximum impact and comprehension. This guide covers grid systems, spatial organization, visual flow, and layout pa
- [poster_quality_checklist](https://doutorai.pt/en/components/skills__poster-quality-checklist): Use this comprehensive checklist before printing or presenting your research poster.
- [poster_quality_checklist](https://doutorai.pt/en/components/skills__poster-quality-checklist-2): Use this comprehensive checklist before printing or presenting your research poster.
- [posters_guidelines](https://doutorai.pt/en/components/skills__posters-guidelines): Comprehensive guidelines for creating effective academic research posters including sizing, layout, typography, and design best practices.
- [postgresql](https://doutorai.pt/en/components/skills__postgresql): Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features
- [postgresql-optimization](https://doutorai.pt/en/components/skills__postgresql-optimization): PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.
- [powerpoint_design_guide](https://doutorai.pt/en/components/skills__powerpoint-design-guide): This guide provides comprehensive instructions for creating professional scientific presentations using PowerPoint, with emphasis on integration with the pptx s
- [powershell-windows](https://doutorai.pt/en/components/skills__powershell-windows): PowerShell Windows patterns. Critical pitfalls, operator syntax, error handling.
- [ppg](https://doutorai.pt/en/components/skills__ppg): Photoplethysmography (PPG) measures blood volume changes in microvascular tissue using optical sensors. PPG is widely used in wearable devices, pulse oximeters,
- [pptx](https://doutorai.pt/en/components/skills__pptx): Presentation toolkit (.pptx). Create/edit slides, layouts, content, speaker notes, comments, for programmatic presentation creation and modification.
- [pptx](https://doutorai.pt/en/components/skills__pptx-5): Use this skill any time a .pptx file is involved in any way - as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; rea
- [pptx-official](https://doutorai.pt/en/components/skills__pptx-official): Use this skill any time a .pptx file is involved in any way - as input, output, or both. This includes: creating slide decks, pitch decks, or presentations; rea
- [pptxgenjs](https://doutorai.pt/en/components/skills__pptxgenjs): To cast a shadow upward (e.g. on a footer bar), use angle: 270 with a positive offset - do not use a negative offset.
- [pptxgenjs](https://doutorai.pt/en/components/skills__pptxgenjs-2): To cast a shadow upward (e.g. on a footer bar), use angle: 270 with a positive offset - do not use a negative offset.
- [predict_solubility](https://doutorai.pt/en/components/skills__predict-solubility): This script trains a model to predict aqueous solubility from SMILES strings using the Delaney (ESOL) dataset as an example. Can be adapted for custom datasets.
- [preparation-template](https://doutorai.pt/en/components/skills__preparation-template): Use this worksheet to prepare before any challenging conversation.
- [prepare_batch_csv](https://doutorai.pt/en/components/skills__prepare-batch-csv): This script helps prepare and validate CSV files for DiffDock batch processing. It checks for required columns, validates file paths, and ensures SMILES strings
- [preprocessing](https://doutorai.pt/en/components/skills__preprocessing-2): PyHealth provides comprehensive data processing utilities to transform raw healthcare data into model-ready formats. Processors handle feature extraction, seque
- [preprocessing](https://doutorai.pt/en/components/skills__preprocessing-3): Data preprocessing transforms raw data into a format suitable for machine learning models. This includes scaling, encoding, handling missing values, and feature
- [preprocessing](https://doutorai.pt/en/components/skills__preprocessing-4): PathML provides a modular preprocessing architecture based on composable transforms organized into pipelines. Transforms are individual operations that modify i
- [presentation_structure](https://doutorai.pt/en/components/skills__presentation-structure): Effective scientific presentations follow a clear narrative structure that guides the audience through your research story. This guide provides structure templa
- [pricing-strategy](https://doutorai.pt/en/components/skills__pricing-strategy): When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'f
- [primitives](https://doutorai.pt/en/components/skills__primitives): Primitives are the fundamental building blocks for executing quantum circuits. Qiskit provides two main primitives: Sampler (for measuring bitstrings) and Estim
- [prisma-expert](https://doutorai.pt/en/components/skills__prisma-expert): Prisma ORM expert for schema design, migrations, query optimization, relations modeling, and database operations. Use PROACTIVELY for Prisma schema issues, migr
- [problems](https://doutorai.pt/en/components/skills__problems): Comprehensive reference for benchmark optimization problems in pymoo.
- [process-interaction](https://doutorai.pt/en/components/skills__process-interaction): This guide covers the mechanisms for processes to interact and synchronize in SimPy simulations.
- [product-manager-toolkit](https://doutorai.pt/en/components/skills__product-manager-toolkit): Comprehensive toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market str
- [product-strategist](https://doutorai.pt/en/components/skills__product-strategist): Strategic product leadership toolkit for Head of Product including OKR cascade generation, market analysis, vision setting, and team scaling. Use for strategic 
- [production-code-audit](https://doutorai.pt/en/components/skills__production-code-audit): Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level
- [production-examples](https://doutorai.pt/en/components/skills__production-examples): Real-world production deployments using Megatron-Core and Megatron-LM.
- [production-patterns](https://doutorai.pt/en/components/skills__production-patterns): Practitioner-tested patterns from Hacker News discussions and real-world deployments. These patterns represent what actually works in production, not theoretica
- [production_vision_systems](https://doutorai.pt/en/components/skills__production-vision-systems): World-class production vision systems for senior computer vision engineer.
- [products](https://doutorai.pt/en/components/skills__products): No,Product Type,Keywords,Primary Style Recommendation,Secondary Styles,Landing Page Pattern,Dashboard Style (if applicable),Color Palette Focus,Key Consideratio
- [professional-communication](https://doutorai.pt/en/components/skills__professional-communication): Guide technical communication for software developers. Covers email structure, team messaging etiquette, meeting agendas, and adapting messages for technical vs
- [profiling](https://doutorai.pt/en/components/skills__profiling): Complete guide to profiling and optimizing ML models with TensorBoard.
- [programmatic-seo](https://doutorai.pt/en/components/skills__programmatic-seo): When the user wants to create SEO-driven pages at scale using templates and data. Also use when the user mentions "programmatic SEO," "template pages," "pages a
- [progress-reporting](https://doutorai.pt/en/components/skills__progress-reporting): Progress reporting allows MCP tools to notify clients about the progress of long-running operations. Clients can display progress indicators and provide better 
- [progressive-web-app](https://doutorai.pt/en/components/skills__progressive-web-app): Build Progressive Web Apps (PWAs) with offline support, installability, and caching strategies. Trigger whenever the user mentions PWA, service workers, web app
- [projects](https://doutorai.pt/en/components/skills__projects): This document outlines the rules for managing Neon projects programmatically. It covers creation, retrieval, updates, and deletion.
- [prometheus-configuration](https://doutorai.pt/en/components/skills__prometheus-configuration): Complete guide to Prometheus setup, metric collection, scrape configuration, and recording rules.
- [prompt-caching](https://doutorai.pt/en/components/skills__prompt-caching): Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache pr
- [prompt-caching](https://doutorai.pt/en/components/skills__prompt-caching-2): This file covers how to design prompt-building code for effective caching. For language-specific syntax, see the ## Prompt Caching section in each language's RE
- [prompt-engineer](https://doutorai.pt/en/components/skills__prompt-engineer): Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use 
- [prompt-engineering](https://doutorai.pt/en/components/skills__prompt-engineering): Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or
- [prompt-engineering-patterns](https://doutorai.pt/en/components/skills__prompt-engineering-patterns): Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
- [prompt-library](https://doutorai.pt/en/components/skills__prompt-library): Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use wh
- [prompt-snippets](https://doutorai.pt/en/components/skills__prompt-snippets): Only apply these snippets if the user explicitly requests them or reports a specific issue. By default, the migration should only update model strings.
- [prompt-template](https://doutorai.pt/en/components/skills__prompt-template): This reference provides a disciplined, repo-grounded prompt that produces AppSec-usable threat models. Use it when you need a reliable output contract and a con
- [prompt_engineering_patterns](https://doutorai.pt/en/components/skills__prompt-engineering-patterns-2): World-class prompt engineering patterns for senior prompt engineer.
- [prompting](https://doutorai.pt/en/components/skills__prompting-3): Edit: - text-localization: Change only the text; preserve layout, typography, spacing, and hierarchy; no extra words or reflow unless needed. - identity-preserv
- [prompts](https://doutorai.pt/en/components/skills__prompts): STT,Style Category,AI Prompt Keywords (Copy-Paste Ready),CSS/Technical Keywords,Implementation Checklist,Design System Variables 1,Minimalism & Swiss Style,"Des
- [protein_analysis_workflow](https://doutorai.pt/en/components/skills__protein-analysis-workflow): This script performs a comprehensive protein analysis pipeline: 1. UniProt search and identifier retrieval 2. FASTA sequence retrieval 3. BLAST similarity searc
- [protein_modeling](https://doutorai.pt/en/components/skills__protein-modeling): TorchDrug provides extensive support for protein-related tasks including sequence analysis, structure prediction, property prediction, and protein-protein inter
- [protein_optimization](https://doutorai.pt/en/components/skills__protein-optimization): Before submitting protein sequences for experimental testing, use computational tools to optimize sequences for improved expression, solubility, and stability. 
- [proteomics_metabolomics_formats](https://doutorai.pt/en/components/skills__proteomics-metabolomics-formats): This reference covers file formats specific to proteomics, metabolomics, lipidomics, and related omics workflows.
- [protocols_api](https://doutorai.pt/en/components/skills__protocols-api): The Protocols API is the core functionality of protocols.io, supporting the complete protocol lifecycle from creation to publication. This includes searching, c
- [protocolsio-integration](https://doutorai.pt/en/components/skills__protocolsio-integration): Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publ
- [providers](https://doutorai.pt/en/components/skills__providers): Guide to using Instructor with different LLM providers.
- [psf__requests-1963](https://doutorai.pt/en/components/skills__psf-requests-1963): Looking at the current code which has req = prepared_request at line 260, this suggests the FIX is to update req so that subsequent iterations use the modified 
- [psf__requests-2148](https://doutorai.pt/en/components/skills__psf-requests-2148): Now I have all the information I need. The fix needs to:
- [psf__requests-2148](https://doutorai.pt/en/components/skills__psf-requests-2148-2): This is a modern version of requests that may have already addressed the issue. Let me look at an older version from around the issue time (requests 2.3.0 era).
- [psf__requests-2317](https://doutorai.pt/en/components/skills__psf-requests-2317): Now I understand the issue and the fix. The problem was that in the old version of requests (2.4.3), there was a line:
- [psf__requests-2674](https://doutorai.pt/en/components/skills__psf-requests-2674): The issue is that in adapters.py, the code catches (_SSLError, _HTTPError) but only explicitly handles certain subclasses. When DecodeError or TimeoutError (the
- [psf__requests-3362](https://doutorai.pt/en/components/skills__psf-requests-3362): Now I understand the issue completely. The fix should modify stream_decode_response_unicode in utils.py to fall back to r.apparent_encoding when r.encoding is N
- [psf__requests-863](https://doutorai.pt/en/components/skills__psf-requests-863): Based on my understanding of the GitHub issue and the requests library, I need to generate a patch that modifies the register_hook method to accept lists in the
- [pubchem-database](https://doutorai.pt/en/components/skills__pubchem-database): Query PubChem via PUG-REST API/PubChemPy (110M+ compounds). Search by name/CID/SMILES, retrieve properties, similarity/substructure searches, bioactivity, for c
- [publication_guidelines](https://doutorai.pt/en/components/skills__publication-guidelines): Scientific figures must be clear, accurate, and accessible. Publication-ready figures follow these fundamental principles:
- [pubmed-database](https://doutorai.pt/en/components/skills__pubmed-database): Direct REST API access to PubMed. Advanced Boolean/MeSH queries, E-utilities API, batch processing, citation management. For Python workflows, prefer biopython 
- [pubmed_search](https://doutorai.pt/en/components/skills__pubmed-search): Comprehensive guide to searching PubMed for biomedical and life sciences literature, including MeSH terms, field tags, advanced search strategies, and E-utiliti
- [pufferlib](https://doutorai.pt/en/components/skills__pufferlib): This skill should be used when working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized para
- [pydantic-ai](https://doutorai.pt/en/components/skills__pydantic-ai): Build production-ready AI agents with PydanticAI - type-safe tool use, structured outputs, dependency injection, and multi-model support.
- [pydata__xarray-3364](https://doutorai.pt/en/components/skills__pydata-xarray-3364): Based on my knowledge of the xarray/xray codebase and the issue description, I need to create a patch that adds an option to handle missing variables when conca
- [pydata__xarray-3364](https://doutorai.pt/en/components/skills__pydata-xarray-3364-2): Based on my knowledge of the xarray codebase and the issue description, I can generate the appropriate patch. The issue is from the pydata/xarray repository and
- [pydata__xarray-4094](https://doutorai.pt/en/components/skills__pydata-xarray-4094): Based on my knowledge of xarray and the issue, I can analyze this problem. The to_unstacked_dataset method is in xarray/core/dataarray.py. The issue occurs beca
- [pydata__xarray-4248](https://doutorai.pt/en/components/skills__pydata-xarray-4248): Based on the context of the GitHub issue, I need to create a patch for the xarray repository. Since I don't have direct access to the xarray source, I'll create
- [pydata__xarray-4248](https://doutorai.pt/en/components/skills__pydata-xarray-4248-2): I don't have access to the xarray repository files in the local environment. However, based on my knowledge of the xarray codebase and the architect's analysis,
- [pydata__xarray-4493](https://doutorai.pt/en/components/skills__pydata-xarray-4493): Based on the issue description and the hints provided, I can generate the patch. The issue clearly states:
- [pydata__xarray-4493](https://doutorai.pt/en/components/skills__pydata-xarray-4493-2): Based on the architect's analysis and my knowledge of xarray's codebase structure, I can generate the patch. The issue is in xarray/core/variable.py in the as_c
- [pydata__xarray-5131](https://doutorai.pt/en/components/skills__pydata-xarray-5131): Now I have the relevant code. The issue is clear:
- [pydata__xarray-5131](https://doutorai.pt/en/components/skills__pydata-xarray-5131-2): Based on the issue description and my knowledge of the xarray codebase, I can generate the patch. The issue describes that the __repr__ method has a trailing wh
- [pydeseq2](https://doutorai.pt/en/components/skills__pydeseq2): Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq ana
- [pydicom](https://doutorai.pt/en/components/skills__pydicom): Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging
- [pyhealth](https://doutorai.pt/en/components/skills__pyhealth): Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working wi
- [pylabrobot](https://doutorai.pt/en/components/skills__pylabrobot): Laboratory automation toolkit for controlling liquid handlers, plate readers, pumps, heater shakers, incubators, centrifuges, and analytical equipment. Use this
- [pylint-dev__pylint-5859](https://doutorai.pt/en/components/skills__pylint-dev-pylint-5859): Based on my knowledge of the pylint codebase and the issue description, I can provide the fix. The bug is in pylint/checkers/misc.py where the --notes option va
- [pylint-dev__pylint-6506](https://doutorai.pt/en/components/skills__pylint-dev-pylint-6506): Based on the bug report and my understanding of the pylint codebase, here's the fix. The issue is that _UnrecognizedOptionError is raised but not caught, causin
- [pylint-dev__pylint-6506](https://doutorai.pt/en/components/skills__pylint-dev-pylint-6506-2): Based on the architect's analysis, I'll generate the patch for fixing the traceback issue. The fix involves catching _UnrecognizedOptionError in run.py and prin
- [pylint-dev__pylint-7080](https://doutorai.pt/en/components/skills__pylint-dev-pylint-7080): Based on the issue description and the hints provided, I have enough information to generate the patch. The issue is clear:
- [pylint-dev__pylint-7080](https://doutorai.pt/en/components/skills__pylint-dev-pylint-7080-2): Based on the architect's analysis and my understanding of pylint's codebase structure, I can generate the patch. The issue is that when using --recursive=y, pat
- [pylint-dev__pylint-7114](https://doutorai.pt/en/components/skills__pylint-dev-pylint-7114): The issue is in pylint's expand_modules.py file. The problem occurs because when pylint tries to resolve the module a, it finds both: - The directory a/ - The f
- [pylint-dev__pylint-7228](https://doutorai.pt/en/components/skills__pylint-dev-pylint-7228): I see the patches for other pylint issues (6506, 7080, 7114) but not the specific issue about the \p{Han} regex error. However, from the hints in the issue disc
- [pylint-dev__pylint-7228](https://doutorai.pt/en/components/skills__pylint-dev-pylint-7228-2): This appears to be a repository that doesn't contain the pylint source code directly. This is a task to generate a patch for the pylint-dev/pylint repository. L
- [pylint-dev__pylint-7993](https://doutorai.pt/en/components/skills__pylint-dev-pylint-7993): Based on my knowledge of this issue, I can provide the fix. The issue is in the pylint text reporter where a regex pattern is used to extract field names from t
- [pylint-dev__pylint-7993](https://doutorai.pt/en/components/skills__pylint-dev-pylint-7993-2): Based on the architect's analysis and my knowledge of the pylint codebase at version 2.12.2, I'll generate the patch. The issue is in the _msg_template_args val
- [pymatgen](https://doutorai.pt/en/components/skills__pymatgen): Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computat
- [pymc-bayesian-modeling](https://doutorai.pt/en/components/skills__pymc-bayesian-modeling): Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming
- [pymoo](https://doutorai.pt/en/components/skills__pymoo): Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimi
- [pyopenms](https://doutorai.pt/en/components/skills__pyopenms): Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzT
- [pysam](https://doutorai.pt/en/components/skills__pysam): Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing 
- [pytdc](https://doutorai.pt/en/components/skills__pytdc): Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharma
- [pytest-dev__pytest-11143](https://doutorai.pt/en/components/skills__pytest-dev-pytest-11143): Based on the error traceback in the issue, I can see the problem. The issue is at line 760 in _pytest/assertion/rewrite.py:
- [pytest-dev__pytest-11148](https://doutorai.pt/en/components/skills__pytest-dev-pytest-11148): Actually, let me reconsider. The actual issue is more subtle. Looking at this problem more carefully:
- [pytest-dev__pytest-11148](https://doutorai.pt/en/components/skills__pytest-dev-pytest-11148-2): Based on the architect's analysis and my knowledge of the pytest codebase structure for version 7.2, I'll generate the patch for the import_path() function in s
- [pytest-dev__pytest-5103](https://doutorai.pt/en/components/skills__pytest-dev-pytest-5103): This is a standalone task to generate a patch for the pytest-dev/pytest repository. I need to create the patch based on my knowledge of pytest's assertion rewri
- [pytest-dev__pytest-5103](https://doutorai.pt/en/components/skills__pytest-dev-pytest-5103-2): Since I cannot locate the pytest repository in the expected location, I need to generate the patch based on my knowledge of pytest's assertion rewriting module.
- [pytest-dev__pytest-5221](https://doutorai.pt/en/components/skills__pytest-dev-pytest-5221): Wait, let me reconsider the approach. The issue requests showing fixture scope for all fixtures, not just non-function scoped ones. Let me provide a more comple
- [pytest-dev__pytest-5227](https://doutorai.pt/en/components/skills__pytest-dev-pytest-5227): Based on the context of this task, I'm being asked to generate a patch for the pytest-dev/pytest repository. This is an issue about improving the default loggin
- [pytest-dev__pytest-5413](https://doutorai.pt/en/components/skills__pytest-dev-pytest-5413): It appears the pytest repository isn't cloned here. Based on the issue, I need to examine the ExceptionInfo class in src/_pytest/_code/code.py. Let me look at t
- [pytest-dev__pytest-5413](https://doutorai.pt/en/components/skills__pytest-dev-pytest-5413-2): Based on the architect's analysis and the issue description, I need to generate a patch for the pytest codebase. The fix is to remove the __str__ method from th
- [pytest-dev__pytest-5495](https://doutorai.pt/en/components/skills__pytest-dev-pytest-5495): Based on my knowledge of the pytest codebase and the issue description, I can provide the patch. The issue is in the _pytest/assertion/util.py file where the se
- [pytest-dev__pytest-5495](https://doutorai.pt/en/components/skills__pytest-dev-pytest-5495-2): Based on the architect's analysis and typical pytest code structure, I'll generate the patch. The issue is in the _compare_eq_sequence function where messages l
- [pytest-dev__pytest-5692](https://doutorai.pt/en/components/skills__pytest-dev-pytest-5692): This repository doesn't contain the pytest source code. Since this is asking about fixing a GitHub issue in pytest-dev/pytest, I need to provide a patch based o
- [pytest-dev__pytest-5692](https://doutorai.pt/en/components/skills__pytest-dev-pytest-5692-2): Wait, let me reconsider - the patch above has issues. Let me generate a cleaner, correct patch based on the typical structure of pytest's junitxml.py:
- [pytest-dev__pytest-6116](https://doutorai.pt/en/components/skills__pytest-dev-pytest-6116): Based on my knowledge of the pytest codebase, I can provide the patch. The --collect-only option is defined in src/_pytest/main.py in the pytest repository. The
- [pytest-dev__pytest-6116](https://doutorai.pt/en/components/skills__pytest-dev-pytest-6116-2): I can see the existing patch format. Let me verify the correct line numbers by checking the actual pytest source file structure. Based on the architect's analys
- [pytest-dev__pytest-7168](https://doutorai.pt/en/components/skills__pytest-dev-pytest-7168): This is not the pytest repository. I need to work on the pytest-dev/pytest repository. Since this is a GitHub issue about pytest, I need to understand the issue
- [pytest-dev__pytest-7220](https://doutorai.pt/en/components/skills__pytest-dev-pytest-7220): Based on the problem description, I need to understand the pytest issue. The issue is that when a fixture changes the working directory (os.chdir), error messag
- [pytest-dev__pytest-7373](https://doutorai.pt/en/components/skills__pytest-dev-pytest-7373): Based on my knowledge of the pytest codebase and the issue description, I can provide the patch. The issue mentions:
- [pytest-dev__pytest-7432](https://doutorai.pt/en/components/skills__pytest-dev-pytest-7432): This appears to be a question about fixing an issue in the pytest repository, but the pytest codebase is not present in the current working directory. Let me ex
- [pytest-dev__pytest-7432](https://doutorai.pt/en/components/skills__pytest-dev-pytest-7432-2): Now I can see the bug clearly. Looking at the pytest_runtest_makereport hook:
- [pytest-dev__pytest-7490](https://doutorai.pt/en/components/skills__pytest-dev-pytest-7490): The issue is that in pytest 6.0.0rc0, dynamically adding an xfail marker using request.node.add_marker(mark) no longer ignores the test failure as it did in pyt
- [pytest-dev__pytest-8365](https://doutorai.pt/en/components/skills__pytest-dev-pytest-8365): Based on the issue description and my knowledge of pytest's codebase, I can provide the fix. The issue is in src/_pytest/tmpdir.py where getpass.getuser() is us
- [pytest-dev__pytest-8365](https://doutorai.pt/en/components/skills__pytest-dev-pytest-8365-2): The pytest repository doesn't appear to be checked out in this workspace. Based on the architect's analysis and the issue description, I'll generate the patch f
- [pytest-dev__pytest-8906](https://doutorai.pt/en/components/skills__pytest-dev-pytest-8906): Based on the GitHub issue discussion, I understand the problem and the proposed solution. The issue is about improving the error message when pytest.skip() is c
- [pytest-dev__pytest-8906](https://doutorai.pt/en/components/skills__pytest-dev-pytest-8906-2): This message is typically in the pytest collection/runner code rather than outcomes.py itself. Let me provide the patch based on the standard pytest codebase st
- [pytest-dev__pytest-9359](https://doutorai.pt/en/components/skills__pytest-dev-pytest-9359): This is a different repository. Let me understand the pytest issue better and create the patch based on my knowledge of the pytest codebase and the issue descri
- [pytest-dev__pytest-9359](https://doutorai.pt/en/components/skills__pytest-dev-pytest-9359-2): Based on my knowledge of the pytest codebase and the issue description, I can generate the appropriate patch. The issue is in the getstatementrange_ast() functi
- [python](https://doutorai.pt/en/components/skills__python): Common Python errors with diagnosis and solutions.
- [python-api](https://doutorai.pt/en/components/skills__python-api): Comprehensive reference for gtars Python bindings.
- [python-django-web-server-security](https://doutorai.pt/en/components/skills__python-django-web-server-security): It is intentionally written as a set of normative requirements (“MUST/SHOULD/MAY”) plus audit rules (what bad patterns look like, how to detect them, and how to
- [python-fastapi](https://doutorai.pt/en/components/skills__python-fastapi): FastAPI REST API template principles. SQLAlchemy, Pydantic, Alembic.
- [python-fastapi-web-server-security](https://doutorai.pt/en/components/skills__python-fastapi-web-server-security): It is intentionally written as a set of normative requirements (“MUST/SHOULD/MAY”) plus audit rules (what bad patterns look like, how to detect them, and how to
- [python-flask-web-server-security](https://doutorai.pt/en/components/skills__python-flask-web-server-security): This document is designed as a security spec that supports: 1) Secure-by-default code generation for new Flask code. 2) Security review / vulnerability hunting 
- [python-patterns](https://doutorai.pt/en/components/skills__python-patterns): Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.
- [python-pro](https://doutorai.pt/en/components/skills__python-pro): Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem inc
- [python-sdk](https://doutorai.pt/en/components/skills__python-sdk): The dxpy library provides Python bindings to interact with the DNAnexus Platform. It's available both within the DNAnexus Execution Environment (for apps runnin
- [python-testing-patterns](https://doutorai.pt/en/components/skills__python-testing-patterns): Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, o
- [python_mcp_server](https://doutorai.pt/en/components/skills__python-mcp-server): This document provides Python-specific best practices and examples for implementing MCP servers using the MCP Python SDK. It covers server setup, tool registrat
- [pytorch-fsdp](https://doutorai.pt/en/components/skills__pytorch-fsdp): Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2
- [pytorch-lightning](https://doutorai.pt/en/components/skills__pytorch-lightning): High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from lap
- [pytorch-lightning](https://doutorai.pt/en/components/skills__pytorch-lightning-2): Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callba
- [pyvene-interventions](https://doutorai.pt/en/components/skills__pyvene-interventions): Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, a
- [qa-test-planner](https://doutorai.pt/en/components/skills__qa-test-planner): Generate comprehensive test plans, manual test cases, regression test suites, and bug reports for QA engineers. Includes Figma MCP integration for design valida
- [qa_best_practices](https://doutorai.pt/en/components/skills__qa-best-practices): This reference guide provides comprehensive information for senior qa.
- [qc_analysis](https://doutorai.pt/en/components/skills__qc-analysis): Performs comprehensive quality control on single-cell RNA-seq data, including calculating metrics, generating QC plots, and filtering cells.
- [qdrant-vector-search](https://doutorai.pt/en/components/skills__qdrant-vector-search): High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, h
- [qiskit](https://doutorai.pt/en/components/skills__qiskit): Comprehensive quantum computing toolkit for building, optimizing, and executing quantum circuits. Use when working with quantum algorithms, simulations, or quan
- [qlora-training](https://doutorai.pt/en/components/skills__qlora-training): Complete guide to fine-tuning large language models using 4-bit quantization with QLoRA (Quantized Low-Rank Adaptation).
- [qms-audit-expert](https://doutorai.pt/en/components/skills__qms-audit-expert): Senior QMS Audit Expert for internal and external quality management system auditing. Provides ISO 13485 audit expertise, audit program management, nonconformit
- [quality-control](https://doutorai.pt/en/components/skills__quality-control): Quality gates, code review process, and severity blocking rules. Enhanced with 2025 research on anti-sycophancy, heterogeneous teams, and OpenAI Agents SDK patt
- [quality-documentation-manager](https://doutorai.pt/en/components/skills__quality-documentation-manager): Senior Quality Documentation Manager for comprehensive documentation control and regulatory document review. Provides document management system design, change 
- [quality-manager-qmr](https://doutorai.pt/en/components/skills__quality-manager-qmr): Senior Quality Manager Responsible Person (QMR) for HealthTech and MedTech companies. Provides overall quality system responsibility, regulatory compliance over
- [quality-manager-qms-iso13485](https://doutorai.pt/en/components/skills__quality-manager-qms-iso13485): ISO 13485 Quality Management System specialist for medical device companies. Provides QMS implementation, maintenance, process optimization, and compliance expe
- [quality_checklist](https://doutorai.pt/en/components/skills__quality-checklist-2): Use this checklist to ensure treatment plans meet professional standards for completeness, quality, safety, and regulatory compliance. Review each section befor
- [quantization](https://doutorai.pt/en/components/skills__quantization): Complete guide to GGUF quantization formats and model conversion.
- [quantization-formats](https://doutorai.pt/en/components/skills__quantization-formats): Complete guide to INT8, NF4, FP4 quantization formats, double quantization, and custom configurations in bitsandbytes.
- [quantizing-models-bitsandbytes](https://doutorai.pt/en/components/skills__quantizing-models-bitsandbytes): Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want fast
- [query-composite-indexes](https://doutorai.pt/en/components/skills__query-composite-indexes): When queries filter on multiple columns, a composite index is more efficient than separate single-column indexes.
- [query-composite-indexes](https://doutorai.pt/en/components/skills__query-composite-indexes-2): When queries filter on multiple columns, a composite index is more efficient than separate single-column indexes.
- [query-covering-indexes](https://doutorai.pt/en/components/skills__query-covering-indexes): Covering indexes include all columns needed by a query, enabling index-only scans that skip the table entirely.
- [query-covering-indexes](https://doutorai.pt/en/components/skills__query-covering-indexes-2): Covering indexes include all columns needed by a query, enabling index-only scans that skip the table entirely.
- [query-index-types](https://doutorai.pt/en/components/skills__query-index-types): Different index types excel at different query patterns. The default B-tree isn't always optimal.
- [query-index-types](https://doutorai.pt/en/components/skills__query-index-types-2): Different index types excel at different query patterns. The default B-tree isn't always optimal.
- [query-missing-indexes](https://doutorai.pt/en/components/skills__query-missing-indexes): Queries filtering or joining on unindexed columns cause full table scans, which become exponentially slower as tables grow.
- [query-missing-indexes](https://doutorai.pt/en/components/skills__query-missing-indexes-2): Queries filtering or joining on unindexed columns cause full table scans, which become exponentially slower as tables grow.
- [query-partial-indexes](https://doutorai.pt/en/components/skills__query-partial-indexes): Partial indexes only include rows matching a WHERE condition, making them smaller and faster when queries consistently filter on the same condition.
- [query-partial-indexes](https://doutorai.pt/en/components/skills__query-partial-indexes-2): Partial indexes only include rows matching a WHERE condition, making them smaller and faster when queries consistently filter on the same condition.
- [query_clinicaltrials](https://doutorai.pt/en/components/skills__query-clinicaltrials): A comprehensive Python script for querying the ClinicalTrials.gov API v2. Provides convenient functions for common query patterns including searching by conditi
- [query_clinpgx](https://doutorai.pt/en/components/skills__query-clinpgx): Provides ready-to-use functions for querying the ClinPGx database API. Includes rate limiting, error handling, and caching functionality.
- [query_engines](https://doutorai.pt/en/components/skills__query-engines): Complete guide to query engines, modes, and customization.
- [query_gene](https://doutorai.pt/en/components/skills__query-gene): This script provides access to ESearch, ESummary, and EFetch functions for searching and retrieving gene information.
- [query_helpers](https://doutorai.pt/en/components/skills__query-helpers): Provides high-level functions for typical research queries.
- [query_opentargets](https://doutorai.pt/en/components/skills__query-opentargets): This script provides reusable functions for querying the Open Targets Platform GraphQL API. Use these functions to retrieve target, disease, drug, and associati
- [query_syntax](https://doutorai.pt/en/components/skills__query-syntax-2): Comprehensive guide to UniProt search query syntax for constructing complex searches.
- [quick_visualize](https://doutorai.pt/en/components/skills__quick-visualize): Provides command-line interface for rapid tree visualization with customizable styles, layouts, and output formats.
- [quickstart](https://doutorai.pt/en/components/skills__quickstart): Welcome! This guide will help you quickly set up FastMCP, run your first MCP server, and deploy a server to Prefect Horizon.
- [quickstart](https://doutorai.pt/en/components/skills__quickstart-2): Use this when the user needs the shortest path from install to a working vault.
- [quota](https://doutorai.pt/en/components/skills__quota): Credit system, usage limits, and checking remaining quota for HeyGen
- [qutip](https://doutorai.pt/en/components/skills__qutip): Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, b
- [radiology_report_template](https://doutorai.pt/en/components/skills__radiology-report-template): Example: "Rule out pulmonary embolism in patient with acute dyspnea and chest pain. History of recent surgery.
- [radix-attention](https://doutorai.pt/en/components/skills__radix-attention): Complete guide to RadixAttention - SGLang's key innovation for automatic prefix caching.
- [raffle-winner-picker](https://doutorai.pt/en/components/skills__raffle-winner-picker): Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
- [rag](https://doutorai.pt/en/components/skills__rag): Complete guide to Retrieval-Augmented Generation with LangChain.
- [rag-engineer](https://doutorai.pt/en/components/skills__rag-engineer): Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM a
- [rag-implementation](https://doutorai.pt/en/components/skills__rag-implementation): Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector sea
- [rag_system_architecture](https://doutorai.pt/en/components/skills__rag-system-architecture): World-class rag system architecture for senior ml/ai engineer.
- [railpack](https://doutorai.pt/en/components/skills__railpack): Railpack is Railway's default builder. Zero-config for most projects.
- [railway-database](https://doutorai.pt/en/components/skills__railway-database): Add official Railway database services (Postgres, Redis, MySQL, MongoDB). Use when user wants to add a database, says "add postgres", "add redis", "add database
- [railway-deploy](https://doutorai.pt/en/components/skills__railway-deploy): Deploy code to Railway using "railway up". Use when user wants to push code, says "railway up", "deploy", "ship", or "push". For initial setup or creating servi
- [railway-deployment](https://doutorai.pt/en/components/skills__railway-deployment): Manage Railway deployments - view logs, redeploy, restart, or remove deployments. Use for deployment lifecycle (remove, stop, redeploy, restart), deployment vis
- [railway-docs](https://doutorai.pt/en/components/skills__railway-docs): Fetch up-to-date Railway documentation to answer questions accurately. Use when user asks about Railway features, how Railway works, or shares a docs.railway.co
- [railway-domain](https://doutorai.pt/en/components/skills__railway-domain): Add, view, or remove domains for Railway services. Use when user wants to add a domain, generate a railway domain, check current domains, get the URL for a serv
- [railway-environment](https://doutorai.pt/en/components/skills__railway-environment): Query, stage, and apply configuration changes for Railway environments. Use for ANY variable or env var operations, service configuration (source, build setting
- [railway-metrics](https://doutorai.pt/en/components/skills__railway-metrics): Query resource usage metrics for Railway services. Use when user asks about resource usage, CPU, memory, network, disk, or service performance like "how much me
- [railway-new](https://doutorai.pt/en/components/skills__railway-new): Create Railway projects, services, and databases with proper configuration. Use when user says "setup", "deploy to railway", "initialize", "create project", "cr
- [railway-projects](https://doutorai.pt/en/components/skills__railway-projects): List, switch, and configure Railway projects. Use when user wants to list all projects, switch projects, rename a project, enable/disable PR deploys, make a pro
- [railway-service](https://doutorai.pt/en/components/skills__railway-service): Check service status, rename services, change service icons, link services, or create services with Docker images. For creating services with local code, prefer
- [railway-status](https://doutorai.pt/en/components/skills__railway-status): Check current Railway project status for this directory. Use when user asks "railway status", "is it running", "what's deployed", "deployment status", or about 
- [railway-templates](https://doutorai.pt/en/components/skills__railway-templates): Search and deploy services from Railway's template marketplace. Use when user wants to add a service from a template, find templates for a specific use case, or
- [ray-data](https://doutorai.pt/en/components/skills__ray-data): Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow.
- [ray-train](https://doutorai.pt/en/components/skills__ray-train): Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray
- [rdkit](https://doutorai.pt/en/components/skills__rdkit): Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generatio
- [react](https://doutorai.pt/en/components/skills__react-2): Common React/Next.js errors with diagnosis and solutions.
- [react-19-patterns](https://doutorai.pt/en/components/skills__react-19-patterns): React 19 introduces breaking changes and new APIs requiring updated TypeScript patterns.
- [react-best-practices](https://doutorai.pt/en/components/skills__react-best-practices): Comprehensive React and Next.js performance optimization guide with 40+ rules for eliminating waterfalls, optimizing bundles, and improving rendering. Use when 
- [react-component-performance](https://doutorai.pt/en/components/skills__react-component-performance): Diagnose slow React components and suggest targeted performance fixes.
- [react-dev](https://doutorai.pt/en/components/skills__react-dev): This skill should be used when building React components with TypeScript, typing hooks, handling events, or when React TypeScript, React 19, Server Components a
- [react-native-app](https://doutorai.pt/en/components/skills__react-native-app): React Native mobile app template principles. Expo, TypeScript, navigation.
- [react-native-architecture](https://doutorai.pt/en/components/skills__react-native-architecture): Production-ready patterns for React Native development with Expo, including navigation, state management, native modules, and offline-first architecture.
- [react-patterns](https://doutorai.pt/en/components/skills__react-patterns): Modern React patterns and principles. Hooks, composition, performance, TypeScript best practices.
- [react-performance-guidelines](https://doutorai.pt/en/components/skills__react-performance-guidelines): Comprehensive performance optimization guide for React and Next.js applications, designed for AI agents and LLMs. Contains 40+ rules across 8 categories, priori
- [react-router](https://doutorai.pt/en/components/skills__react-router): React Router is a modern routing library that provides flexible client-side and server-side routing for React applications. It offers two primary modes of opera
- [react-state-management](https://doutorai.pt/en/components/skills__react-state-management): Master modern React state management with Redux Toolkit, Zustand, Jotai, and React Query. Use when setting up global state, managing server state, or choosing b
- [react-ui-patterns](https://doutorai.pt/en/components/skills__react-ui-patterns): Modern React UI patterns for loading states, error handling, and data fetching. Use when building UI components, handling async data, or managing UI states.
- [react-useeffect](https://doutorai.pt/en/components/skills__react-useeffect): React useEffect best practices from official docs. Use when writing/reviewing useEffect, useState for derived values, data fetching, or state synchronization. T
- [react_patterns](https://doutorai.pt/en/components/skills__react-patterns-2): This reference guide provides comprehensive information for senior frontend.
- [reactions_data](https://doutorai.pt/en/components/skills__reactions-data): The reactions module enables programmatic application of chemical transformations using SMARTS reaction patterns.
- [reactome-database](https://doutorai.pt/en/components/skills__reactome-database): Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biolo
- [reactome_query](https://doutorai.pt/en/components/skills__reactome-query): This script provides convenient command-line access to common Reactome operations.
- [real-time](https://doutorai.pt/en/components/skills__real-time): This guide covers real-time simulation capabilities in SimPy, where simulation time is synchronized with wall-clock time.
- [real-world-examples](https://doutorai.pt/en/components/skills__real-world-examples): Detailed analysis of how production plugins use the .claude/plugin-name.local.md pattern.
- [rearrange](https://doutorai.pt/en/components/skills__rearrange): This will create output.pptx using slides from template.pptx in the specified order. Slides can be repeated (e.g., 34 appears twice).
- [rearrange](https://doutorai.pt/en/components/skills__rearrange-2): This will create output.pptx using slides from template.pptx in the specified order. Slides can be repeated (e.g., 34 appears twice).
- [receiving-code-review](https://doutorai.pt/en/components/skills__receiving-code-review): Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical 
- [record-full-demo](https://doutorai.pt/en/components/skills__record-full-demo): for i in 10 9 8 7 6 5 4 3 2 1; do printf "\rStarting in %d... " $i sleep 1 done echo
- [red-team-tactics](https://doutorai.pt/en/components/skills__red-team-tactics): Red team tactics principles based on MITRE ATT&CK. Attack phases, detection evasion, reporting.
- [reducing-entropy](https://doutorai.pt/en/components/skills__reducing-entropy): Manual-only skill for minimizing total codebase size. Only activate when explicitly requested by user. Measures success by final code amount, not effort. Bias t
- [reference](https://doutorai.pt/en/components/skills__reference-2): This skill is based on context engineering principles from Manus, the AI agent company acquired by Meta for $2 billion in December 2025.
- [reference](https://doutorai.pt/en/components/skills__reference-3): This skill is based on the context engineering principles from Manus, the AI agent company acquired by Meta for $2 billion in December 2025.
- [reference](https://doutorai.pt/en/components/skills__reference-4): This document contains advanced PDF processing features, detailed examples, and additional libraries not covered in the main skill instructions.
- [reference](https://doutorai.pt/en/components/skills__reference-5): This document contains advanced PDF processing features, detailed examples, and additional libraries not covered in the main skill instructions.
- [reference](https://doutorai.pt/en/components/skills__reference-6): This document contains advanced PDF processing features, detailed examples, and additional libraries not covered in the main skill instructions.
- [reference](https://doutorai.pt/en/components/skills__reference-7): This document contains advanced PDF processing features, detailed examples, and additional libraries not covered in the main skill instructions.
- [reference](https://doutorai.pt/en/components/skills__reference-8): This document contains advanced PDF processing features, detailed examples, and additional libraries not covered in the main skill instructions.
- [reference-list-builder](https://doutorai.pt/en/components/skills__reference-list-builder): Format professional references properly and prepare reference materials. Use when the user mentions references, reference list, references page, or preparing re
- [referencing-docs](https://doutorai.pt/en/components/skills__referencing-docs): The Neon documentation is the source of truth for all Neon-related information. Always verify Neon-related claims, configurations, and best practices against th
- [referral-program](https://doutorai.pt/en/components/skills__referral-program): When the user wants to create, optimize, or analyze a referral program, affiliate program, or word-of-mouth strategy. Also use when the user mentions 'referral,
- [refget](https://doutorai.pt/en/components/skills__refget): The refget module handles reference sequence retrieval and digest computation, following the refget protocol for sequence identification.
- [region2vec](https://doutorai.pt/en/components/skills__region2vec): Region2Vec generates unsupervised embeddings of genomic regions and region sets from BED files. It maps genomic regions to a vocabulary, creates sentences throu
- [regression](https://doutorai.pt/en/components/skills__regression): Aeon provides time series regressors across 9 categories for predicting continuous values from temporal sequences.
- [regression_testing](https://doutorai.pt/en/components/skills__regression-testing): Comprehensive guide to regression testing strategies and execution.
- [regulatory-affairs-head](https://doutorai.pt/en/components/skills__regulatory-affairs-head): Senior Regulatory Affairs Manager expertise for HealthTech and MedTech companies. Provides strategic regulatory guidance, submission management, regulatory path
- [regulatory_compliance](https://doutorai.pt/en/components/skills__regulatory-compliance): Treatment plans must comply with multiple federal and state regulations governing healthcare documentation, patient privacy, billing practices, and quality stan
- [regulatory_compliance](https://doutorai.pt/en/components/skills__regulatory-compliance-2): HIPAA Privacy Rule protects individually identifiable health information (Protected Health Information, PHI). All clinical reports must comply with HIPAA requir
- [rehabilitation_treatment_plan](https://doutorai.pt/en/components/skills__rehabilitation-treatment-plan): extbf{HIPAA Notice}: De-identify per Safe Harbor method. Remove all 18 HIPAA identifiers before sharing.
- [remote-async-communication](https://doutorai.pt/en/components/skills__remote-async-communication): Guide to effective communication in remote-first and async-first environments.
- [remote-oauth](https://doutorai.pt/en/components/skills__remote-oauth): Remote OAuth integration allows your FastMCP server to leverage external identity providers that support Dynamic Client Registration (DCR). With DCR, MCP client
- [remotion](https://doutorai.pt/en/components/skills__remotion): Best practices and comprehensive guide for Remotion - programmatic video creation in React with animations, compositions, and media handling
- [remotion-best-practices](https://doutorai.pt/en/components/skills__remotion-best-practices): Best practices for Remotion - Video creation in React
- [remotion-integration](https://doutorai.pt/en/components/skills__remotion-integration): Using HeyGen avatar videos in Remotion compositions
- [render-deploy](https://doutorai.pt/en/components/skills__render-deploy): Deploy applications to Render by analyzing codebases, generating render.yaml Blueprints, and providing Dashboard deeplinks. Use when the user wants to deploy, h
- [render-graphs](https://doutorai.pt/en/components/skills__render-graphs): / Render graphviz diagrams from a skill's SKILL.md to SVG files. Usage: ./render-graphs.js # Render each diagram separately ./render-graphs.js --combine # Combi
- [render-graphs](https://doutorai.pt/en/components/skills__render-graphs-2): / Render graphviz diagrams from a skill's SKILL.md to SVG files. Usage: ./render-graphs.js # Render each diagram separately ./render-graphs.js --combine # Combi
- [render_docx](https://doutorai.pt/en/components/skills__render-docx): Images are named as page- . with pages starting at 1. """ makedirs(out_dir, exist_ok=True) doc_path = abspath(doc_path) stem = splitext(basename(doc_path))[0]
- [rendering-activity](https://doutorai.pt/en/components/skills__rendering-activity): Use React's to preserve state/DOM for expensive components that frequently toggle visibility.
- [rendering-activity](https://doutorai.pt/en/components/skills__rendering-activity-2): Use React's to preserve state/DOM for expensive components that frequently toggle visibility.
- [rendering-animate-svg-wrapper](https://doutorai.pt/en/components/skills__rendering-animate-svg-wrapper): Many browsers don't have hardware acceleration for CSS3 animations on SVG elements. Wrap SVG in a and animate the wrapper instead.
- [rendering-animate-svg-wrapper](https://doutorai.pt/en/components/skills__rendering-animate-svg-wrapper-2): Many browsers don't have hardware acceleration for CSS3 animations on SVG elements. Wrap SVG in a and animate the wrapper instead.
- [rendering-conditional-render](https://doutorai.pt/en/components/skills__rendering-conditional-render): Use explicit ternary operators (? :) instead of && for conditional rendering when the condition can be 0, NaN, or other falsy values that render.
- [rendering-conditional-render](https://doutorai.pt/en/components/skills__rendering-conditional-render-2): Use explicit ternary operators (? :) instead of && for conditional rendering when the condition can be 0, NaN, or other falsy values that render.
- [rendering-content-visibility](https://doutorai.pt/en/components/skills__rendering-content-visibility): Apply content-visibility: auto to defer off-screen rendering.
- [rendering-content-visibility](https://doutorai.pt/en/components/skills__rendering-content-visibility-2): Apply content-visibility: auto to defer off-screen rendering.
- [rendering-hoist-jsx](https://doutorai.pt/en/components/skills__rendering-hoist-jsx): Extract static JSX outside components to avoid re-creation.
- [rendering-hoist-jsx](https://doutorai.pt/en/components/skills__rendering-hoist-jsx-2): Extract static JSX outside components to avoid re-creation.
- [rendering-hydration-no-flicker](https://doutorai.pt/en/components/skills__rendering-hydration-no-flicker): When rendering content that depends on client-side storage (localStorage, cookies), avoid both SSR breakage and post-hydration flickering by injecting a synchro
- [rendering-hydration-no-flicker](https://doutorai.pt/en/components/skills__rendering-hydration-no-flicker-2): When rendering content that depends on client-side storage (localStorage, cookies), avoid both SSR breakage and post-hydration flickering by injecting a synchro
- [rendering-svg-precision](https://doutorai.pt/en/components/skills__rendering-svg-precision): Reduce SVG coordinate precision to decrease file size. The optimal precision depends on the viewBox size, but in general reducing precision should be considered
- [rendering-svg-precision](https://doutorai.pt/en/components/skills__rendering-svg-precision-2): Reduce SVG coordinate precision to decrease file size. The optimal precision depends on the viewBox size, but in general reducing precision should be considered
- [replace](https://doutorai.pt/en/components/skills__replace): The replacements JSON should have the structure output by inventory.py. ALL text shapes identified by inventory.py will have their text cleared unless "paragrap
- [replace](https://doutorai.pt/en/components/skills__replace-2): The replacements JSON should have the structure output by inventory.py. ALL text shapes identified by inventory.py will have their text cleared unless "paragrap
- [repo-workflow](https://doutorai.pt/en/components/skills__repo-workflow): Use this when the user wants to compile a codebase into durable module pages, graph artifacts, and reviewable outputs.
- [report_structure_guide](https://doutorai.pt/en/components/skills__report-structure-guide): Detailed guidance for writing each section of a comprehensive market research report.
- [report_template](https://doutorai.pt/en/components/skills__report-template): This report provides a comprehensive exploratory data analysis of the file {FILENAME}. The analysis includes file type identification, format-specific metadata 
- [reporting_guidelines](https://doutorai.pt/en/components/skills__reporting-guidelines): Reporting guidelines are evidence-based recommendations for what information should be included when reporting specific types of research studies. They provide 
- [reporting_standards](https://doutorai.pt/en/components/skills__reporting-standards): This document catalogs major reporting standards and guidelines across scientific disciplines. When reviewing manuscripts, verify that authors have followed the
- [reporting_standards](https://doutorai.pt/en/components/skills__reporting-standards-2): This document provides guidelines for reporting statistical analyses according to APA (American Psychological Association) style and general best practices for 
- [requesting-code-review](https://doutorai.pt/en/components/skills__requesting-code-review): Use when completing tasks, implementing major features, or before merging to verify work meets requirements
- [requirements-clarity](https://doutorai.pt/en/components/skills__requirements-clarity): Clarify ambiguous requirements through focused dialogue before implementation. Use when requirements are unclear, features are complex (>2 days), or involve cro
- [rerender-defer-reads](https://doutorai.pt/en/components/skills__rerender-defer-reads): Don't subscribe to dynamic state (searchParams, localStorage) if you only read it inside callbacks.
- [rerender-defer-reads](https://doutorai.pt/en/components/skills__rerender-defer-reads-2): Don't subscribe to dynamic state (searchParams, localStorage) if you only read it inside callbacks.
- [rerender-dependencies](https://doutorai.pt/en/components/skills__rerender-dependencies): Specify primitive dependencies instead of objects to minimize effect re-runs.
- [rerender-dependencies](https://doutorai.pt/en/components/skills__rerender-dependencies-2): Specify primitive dependencies instead of objects to minimize effect re-runs.
- [rerender-derived-state](https://doutorai.pt/en/components/skills__rerender-derived-state): Subscribe to derived boolean state instead of continuous values to reduce re-render frequency.
- [rerender-derived-state](https://doutorai.pt/en/components/skills__rerender-derived-state-2): Subscribe to derived boolean state instead of continuous values to reduce re-render frequency.
- [rerender-functional-setstate](https://doutorai.pt/en/components/skills__rerender-functional-setstate): When updating state based on the current state value, use the functional update form of setState instead of directly referencing the state variable. This preven
- [rerender-lazy-state-init](https://doutorai.pt/en/components/skills__rerender-lazy-state-init): Pass a function to useState for expensive initial values. Without the function form, the initializer runs on every render even though the value is only used onc
- [rerender-lazy-state-init](https://doutorai.pt/en/components/skills__rerender-lazy-state-init-2): Pass a function to useState for expensive initial values. Without the function form, the initializer runs on every render even though the value is only used onc
- [rerender-memo](https://doutorai.pt/en/components/skills__rerender-memo): Extract expensive work into memoized components to enable early returns before computation.
- [rerender-memo](https://doutorai.pt/en/components/skills__rerender-memo-2): Extract expensive work into memoized components to enable early returns before computation.
- [rerender-transitions](https://doutorai.pt/en/components/skills__rerender-transitions): Mark frequent, non-urgent state updates as transitions to maintain UI responsiveness.
- [rerender-transitions](https://doutorai.pt/en/components/skills__rerender-transitions-2): Mark frequent, non-urgent state updates as transitions to maintain UI responsiveness.
- [research-and-citations](https://doutorai.pt/en/components/skills__research-and-citations): Web research is not a one-time pass at the start of the blueprint. It runs continuously through every phase. Whenever you make a claim about an external library
- [research-engineer](https://doutorai.pt/en/components/skills__research-engineer): An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, 
- [research-grants](https://doutorai.pt/en/components/skills__research-grants): Write competitive research proposals for NSF, NIH, DOE, and DARPA. Agency-specific formatting, review criteria, budget preparation, broader impacts, significanc
- [research-lookup](https://doutorai.pt/en/components/skills__research-lookup): Look up current research information using Perplexity's Sonar Pro Search or Sonar Reasoning Pro models through OpenRouter. Automatically selects the best model 
- [research-protocol](https://doutorai.pt/en/components/skills__research-protocol): This document defines the research decision and execution flow.
- [research-workflow](https://doutorai.pt/en/components/skills__research-workflow): Use this when the user is collecting papers, articles, books, datasets, slide decks, screenshots, or other mixed research sources into one vault.
- [research_lookup](https://doutorai.pt/en/components/skills__research-lookup-2): IMPORTANT INSTRUCTIONS: 1. Focus on ACADEMIC and SCIENTIFIC sources (peer-reviewed papers, reputable journals, institutional research) 2. Include RECENT informa
- [research_lookup](https://doutorai.pt/en/components/skills__research-lookup-3): IMPORTANT INSTRUCTIONS: 1. Focus on ACADEMIC and SCIENTIFIC sources (peer-reviewed papers, reputable journals, institutional research) 2. Include RECENT informa
- [research_pipeline](https://doutorai.pt/en/components/skills__research-pipeline): The main class for orchestrating research workflows.
- [resolve](https://doutorai.pt/en/components/skills__resolve): The Resolve API identifies Data Commons IDs (DCIDs) for entities in the knowledge graph. DCIDs are required for most queries in the Data Commons API, so resolut
- [resource-configuration](https://doutorai.pt/en/components/skills__resource-configuration): Contact Latch support for quota increases if needed.
- [resource_monitor](https://doutorai.pt/en/components/skills__resource-monitor): This module provides reusable classes and functions for monitoring SimPy resources during simulation. Includes utilities for tracking queue lengths, utilization
- [resources](https://doutorai.pt/en/components/skills__resources): Containers can exceed minimum if worker has available resources.
- [resources](https://doutorai.pt/en/components/skills__resources-2): Resources in PyLabRobot represent laboratory equipment, labware, or components used in protocols. The resource system provides a hierarchical structure for mana
- [resources](https://doutorai.pt/en/components/skills__resources-3): This guide covers all resource types in SimPy for modeling congestion points and resource allocation.
- [resources-and-templates](https://doutorai.pt/en/components/skills__resources-and-templates): Resources represent data or files that an MCP client can read, and resource templates extend this concept by allowing clients to request dynamically generated r
- [resume-ats-optimizer](https://doutorai.pt/en/components/skills__resume-ats-optimizer): Optimize resumes for Applicant Tracking Systems, check ATS compatibility, and analyze keyword match. Use when the user mentions ATS, applicant tracking, keyword
- [resume-bullet-writer](https://doutorai.pt/en/components/skills__resume-bullet-writer): Transform weak resume bullets into achievement-focused statements with metrics and impact. Use when the user wants to rewrite bullets, strengthen accomplishment
- [resume-checklist](https://doutorai.pt/en/components/skills__resume-checklist): Follow this checklist when resuming work from a handoff document to ensure zero-ambiguity continuation.
- [resume-formatter](https://doutorai.pt/en/components/skills__resume-formatter): Ensure ATS-friendly formatting and create clean, scannable layouts. Use when the user mentions resume format, layout, template, design, or fixing formatting iss
- [resume-quantifier](https://doutorai.pt/en/components/skills__resume-quantifier): Find opportunities to add metrics and estimate numbers when exact data is unavailable. Use when the user wants to quantify achievements, add numbers, percentage
- [resume-section-builder](https://doutorai.pt/en/components/skills__resume-section-builder): Create targeted resume sections optimized for different experience levels and roles. Use when the user wants to build or rewrite specific sections like summary,
- [resume-tailor](https://doutorai.pt/en/components/skills__resume-tailor): Customize resume for specific job postings while maintaining truthfulness. Use when the user wants to tailor, customize, or target their resume for a specific j
- [resume-version-manager](https://doutorai.pt/en/components/skills__resume-version-manager): Track different resume versions, maintain a master resume, and manage tailored variants. Use when the user mentions managing multiple resume versions, master re
- [retrosynthesis](https://doutorai.pt/en/components/skills__retrosynthesis): Retrosynthesis is the process of planning synthetic routes from target molecules back to commercially available starting materials. TorchDrug provides tools for
- [review_template](https://doutorai.pt/en/components/skills__review-template): APA: Author, A. A., & Author, B. B. (Year). Title. Journal, volume(issue), pages. https://doi.org/xx.xxxx
- [reviewer-guidelines](https://doutorai.pt/en/components/skills__reviewer-guidelines): This reference documents how reviewers evaluate papers at major ML/AI conferences, helping authors anticipate and address reviewer concerns.
- [reward-modeling](https://doutorai.pt/en/components/skills__reward-modeling): Guide to training reward models with TRL for RLHF pipelines.
- [reward_functions_library](https://doutorai.pt/en/components/skills__reward-functions-library): A collection of battle-tested reward functions for common GRPO training scenarios. Copy and adapt these for your specific use case.
- [risk-management-specialist](https://doutorai.pt/en/components/skills__risk-management-specialist): Senior Risk Management specialist for medical device companies implementing ISO 14971 risk management throughout product lifecycle. Provides risk analysis, risk
- [risk_calculator](https://doutorai.pt/en/components/skills__risk-calculator): Calculates risk scores using both qualitative and quantitative methodologies. Supports risk matrix, ALE calculations, and cost-benefit analysis for controls.
- [roier-seo](https://doutorai.pt/en/components/skills__roier-seo): Technical SEO auditor and fixer. Runs Lighthouse/PageSpeed audits on websites or local dev servers, analyzes SEO/performance/accessibility scores, and automatic
- [rois](https://doutorai.pt/en/components/skills__rois): This reference covers creating, retrieving, and analyzing ROIs in OMERO.
- [root-cause](https://doutorai.pt/en/components/skills__root-cause): Techniques for finding the real cause of errors, not just symptoms.
- [root-cause-tracing](https://doutorai.pt/en/components/skills__root-cause-tracing): Bugs often manifest deep in the call stack (git init in wrong directory, file created in wrong location, database opened with wrong path). Your instinct is to f
- [rope](https://doutorai.pt/en/components/skills__rope): Complete technical guide based on RoFormer paper (arXiv 2104.09864) and HuggingFace transformers implementation.
- [routing-and-controllers](https://doutorai.pt/en/components/skills__routing-and-controllers): Complete guide to clean route definitions and controller patterns.
- [routing-guide](https://doutorai.pt/en/components/skills__routing-guide): TanStack Router implementation with folder-based routing and lazy loading patterns.
- [rsp](https://doutorai.pt/en/components/skills__rsp): Respiratory signal processing in NeuroKit2 enables analysis of breathing patterns, respiratory rate, amplitude, and variability. Respiration is closely linked t
- [ruby-pro](https://doutorai.pt/en/components/skills__ruby-pro): Write idiomatic Ruby code with metaprogramming, Rails patterns, and performance optimization. Specializes in Ruby on Rails, gem development, and testing framewo
- [rules_catalog](https://doutorai.pt/en/components/skills__rules-catalog): Comprehensive catalog of all available medicinal chemistry rules, structural alerts, and filters in medchem.
- [run-benchmarks](https://doutorai.pt/en/components/skills__run-benchmarks): INSTRUCTIONS: 1. Output the COMPLETE function including the signature and docstring shown above 2. Fill in the implementation after the docstring 3. Use proper 
- [run-demo](https://doutorai.pt/en/components/skills__run-demo): info "Loki Mode will then:" info " 1. Read and analyze the PRD" info " 2. Create .loki/ directory for state management" info " 3. Generate tasks and add to queu
- [run_deseq2_analysis](https://doutorai.pt/en/components/skills__run-deseq2-analysis): This script performs a complete differential expression analysis using PyDESeq2. It can be used as a template for standard RNA-seq DEA workflows.
- [run_eval](https://doutorai.pt/en/components/skills__run-eval): Tests whether a skill's description causes Claude to trigger (read the skill) for a set of queries. Outputs results as JSON.
- [run_eval](https://doutorai.pt/en/components/skills__run-eval-2): Tests whether a skill's description causes Claude to trigger (read the skill) for a set of queries. Outputs results as JSON.
- [run_eval](https://doutorai.pt/en/components/skills__run-eval-3): Tests whether a skill's description causes Claude to trigger (read the skill) for a set of queries. Outputs results as JSON.
- [run_loop](https://doutorai.pt/en/components/skills__run-loop): Combines run_eval.py and improve_description.py in a loop, tracking history and returning the best description found. Supports train/test split to prevent overf
- [run_loop](https://doutorai.pt/en/components/skills__run-loop-2): Combines run_eval.py and improve_description.py in a loop, tracking history and returning the best description found. Supports train/test split to prevent overf
- [run_loop](https://doutorai.pt/en/components/skills__run-loop-3): Combines run_eval.py and improve_description.py in a loop, tracking history and returning the best description found. Supports train/test split to prevent overf
- [runtimes](https://doutorai.pt/en/components/skills__runtimes): Complete guide to available runtimes on Render, including versions, configuration, and best practices for each language.
- [rust](https://doutorai.pt/en/components/skills__rust): Common Rust errors with diagnosis and solutions.
- [rust-cli-builder](https://doutorai.pt/en/components/skills__rust-cli-builder): Plan and build production-ready Rust CLI tools using clap for argument parsing, with subcommands, config file support, colored output, and proper error handling
- [rust-pro](https://doutorai.pt/en/components/skills__rust-pro): Master Rust 1.75+ with modern async patterns, advanced type system features, and production-ready systems programming.
- [rwkv-architecture](https://doutorai.pt/en/components/skills__rwkv-architecture): RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation
- [rwkv7](https://doutorai.pt/en/components/skills__rwkv7): RWKV-7 is the latest version released in March 2025, introducing multimodal capabilities and improved scaling to 14B+ parameters.
- [saas-multi-tenant](https://doutorai.pt/en/components/skills__saas-multi-tenant): Design and implement multi-tenant SaaS architectures with row-level security, tenant-scoped queries, shared-schema isolation, and safe cross-tenant admin patter
- [safety](https://doutorai.pt/en/components/skills__safety): You can adjust safety settings to control the thresholds for harmful content generation. Standard safety filters are applied by default.
- [salary-negotiation-prep](https://doutorai.pt/en/components/skills__salary-negotiation-prep): Research market rates, build negotiation strategy, and create counter-offer scripts. Use when the user mentions salary negotiation, counter offer, compensation 
- [salesforce-development](https://doutorai.pt/en/components/skills__salesforce-development): Expert patterns for Salesforce platform development including Lightning Web Components (LWC), Apex triggers and classes, REST/Bulk APIs, Connected Apps, and Sal
- [sample-prompts](https://doutorai.pt/en/components/skills__sample-prompts): Use these as starting points. Keep user-provided requirements and constraints; do not invent new creative elements.
- [sample-prompts](https://doutorai.pt/en/components/skills__sample-prompts-2): These are short instruction blocks. Use only the lines you need and keep them consistent with the input text.
- [sample-prompts](https://doutorai.pt/en/components/skills__sample-prompts-3): Use these as starting points (recipes only). Keep user-provided requirements; do not invent new creative elements.
- [sampling](https://doutorai.pt/en/components/skills__sampling): LLM sampling allows your MCP tools to request text generation from an LLM during execution. This enables tools to leverage AI capabilities for analysis, generat
- [sampling_inference](https://doutorai.pt/en/components/skills__sampling-inference): This reference covers the sampling algorithms and inference methods available in PyMC for posterior inference.
- [sast-configuration](https://doutorai.pt/en/components/skills__sast-configuration): Static Application Security Testing (SAST) tool setup, configuration, and custom rule creation for comprehensive security scanning across multiple programming l
- [scala-pro](https://doutorai.pt/en/components/skills__scala-pro): Master enterprise-grade Scala development with functional programming, distributed systems, and big data processing. Expert in Apache Pekko, Akka, Spark, ZIO/Ca
- [scale-reference](https://doutorai.pt/en/components/skills__scale-reference): Comprehensive guide to all d3 scale types with examples and use cases.
- [scaling](https://doutorai.pt/en/components/skills__scaling): Every Modal Function corresponds to an autoscaling pool of containers. Modal's autoscaler: - Spins up containers when no capacity available - Spins down contain
- [scanpy](https://doutorai.pt/en/components/skills__scanpy): Single-cell RNA-seq analysis. Load .h5ad/10X data, QC, normalization, PCA/UMAP/t-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scR
- [scembed](https://doutorai.pt/en/components/skills__scembed): scEmbed trains Region2Vec models on single-cell ATAC-seq datasets to generate cell embeddings for clustering and analysis. It provides an unsupervised machine l
- [scenes](https://doutorai.pt/en/components/skills__scenes): Scenes are the canvas for your animations. All Manim code is organized within Scene classes, and each scene represents a complete animation sequence.
- [scheduled-jobs](https://doutorai.pt/en/components/skills__scheduled-jobs): Schedule functions to run automatically at regular intervals or specific times.
- [scheduled_tasks](https://doutorai.pt/en/components/skills__scheduled-tasks): Use search_templates({query: "schedule"}) to find more!
- [schedulers](https://doutorai.pt/en/components/skills__schedulers): Dask provides multiple task schedulers, each suited to different workloads. The scheduler determines how tasks are executed: sequentially, in parallel threads, 
- [schema-data-types](https://doutorai.pt/en/components/skills__schema-data-types): Using the right data types reduces storage, improves query performance, and prevents bugs.
- [schema-data-types](https://doutorai.pt/en/components/skills__schema-data-types-2): Using the right data types reduces storage, improves query performance, and prevents bugs.
- [schema-design-checklist](https://doutorai.pt/en/components/skills__schema-design-checklist): Complete checklist for designing and reviewing database schemas.
- [schema-foreign-key-indexes](https://doutorai.pt/en/components/skills__schema-foreign-key-indexes): Postgres does not automatically index foreign key columns. Missing indexes cause slow JOINs and CASCADE operations.
- [schema-foreign-key-indexes](https://doutorai.pt/en/components/skills__schema-foreign-key-indexes-2): Postgres does not automatically index foreign key columns. Missing indexes cause slow JOINs and CASCADE operations.
- [schema-lowercase-identifiers](https://doutorai.pt/en/components/skills__schema-lowercase-identifiers): PostgreSQL folds unquoted identifiers to lowercase. Quoted mixed-case identifiers require quotes forever and cause issues with tools, ORMs, and AI assistants th
- [schema-lowercase-identifiers](https://doutorai.pt/en/components/skills__schema-lowercase-identifiers-2): PostgreSQL folds unquoted identifiers to lowercase. Quoted mixed-case identifiers require quotes forever and cause issues with tools, ORMs, and AI assistants th
- [schema-markup](https://doutorai.pt/en/components/skills__schema-markup): When the user wants to add, fix, or optimize schema markup and structured data on their site. Also use when the user mentions "schema markup," "structured data,
- [schema-partitioning](https://doutorai.pt/en/components/skills__schema-partitioning): Partitioning splits a large table into smaller pieces, improving query performance and maintenance operations.
- [schema-partitioning](https://doutorai.pt/en/components/skills__schema-partitioning-2): Partitioning splits a large table into smaller pieces, improving query performance and maintenance operations.
- [schema-primary-keys](https://doutorai.pt/en/components/skills__schema-primary-keys): Primary key choice affects insert performance, index size, and replication efficiency.
- [schema-primary-keys](https://doutorai.pt/en/components/skills__schema-primary-keys-2): Primary key choice affects insert performance, index size, and replication efficiency.
- [schemas](https://doutorai.pt/en/components/skills__schemas): This document defines the JSON schemas used by skill-creator.
- [schemas](https://doutorai.pt/en/components/skills__schemas-2): This document defines the JSON schemas used by skill-creator.
- [schemas](https://doutorai.pt/en/components/skills__schemas-3): This document defines the JSON schemas used by skill-creator.
- [scientific-brainstorming](https://doutorai.pt/en/components/skills__scientific-brainstorming): Research ideation partner. Generate hypotheses, explore interdisciplinary connections, challenge assumptions, develop methodologies, identify research gaps, for
- [scientific-critical-thinking](https://doutorai.pt/en/components/skills__scientific-critical-thinking): Evaluate research rigor. Assess methodology, experimental design, statistical validity, biases, confounding, evidence quality (GRADE, Cochrane ROB), for critica
- [scientific-schematics](https://doutorai.pt/en/components/skills__scientific-schematics): Create publication-quality scientific diagrams using Nano Banana Pro AI with smart iterative refinement. Uses Gemini 3 Pro for quality review. Only regenerates 
- [scientific-slides](https://doutorai.pt/en/components/skills__scientific-slides): Build slide decks and presentations for research talks. Use this for making PowerPoint slides, conference presentations, seminar talks, research presentations, 
- [scientific-visualization](https://doutorai.pt/en/components/skills__scientific-visualization): Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for jour
- [scientific-writing](https://doutorai.pt/en/components/skills__scientific-writing): Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process: (1) create sect
- [scikit-bio](https://doutorai.pt/en/components/skills__scikit-bio): Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick 
- [scikit-learn](https://doutorai.pt/en/components/skills__scikit-learn): Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensi
- [scikit-learn__scikit-learn-10297](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-10297): Based on the GitHub issue, I can provide the patch for fixing the store_cv_values parameter issue in RidgeClassifierCV. This is a well-known scikit-learn issue 
- [scikit-learn__scikit-learn-10297](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-10297-2): Based on the issue description, I can generate the patch for the scikit-learn repository. The issue is that RidgeClassifierCV is missing the store_cv_values par
- [scikit-learn__scikit-learn-10508](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-10508): Based on the issue description and error traceback, I can generate the fix. The problem is in sklearn/preprocessing/label.py at the transform method (around lin
- [scikit-learn__scikit-learn-10508](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-10508-2): Based on the architect's analysis and the issue description, I need to generate a patch for the scikit-learn repository. The file path is sklearn/preprocessing/
- [scikit-learn__scikit-learn-10949](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-10949): Based on the issue description and my knowledge of the scikit-learn codebase, I can provide the fix. The issue is in sklearn/utils/validation.py where the check
- [scikit-learn__scikit-learn-10949](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-10949-2): Based on the architect's analysis and my knowledge of scikit-learn's codebase, I can generate the patch. The issue is that when a pandas DataFrame is passed to 
- [scikit-learn__scikit-learn-11040](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-11040): Based on my knowledge of scikit-learn's codebase structure and the issue description, I can generate the patch. The issue is about validating n_neighbors as an 
- [scikit-learn__scikit-learn-11040](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-11040-2): I'll generate the patch based on my knowledge of the scikit-learn codebase structure. The validation for n_neighbors should be added in the base.py file in the 
- [scikit-learn__scikit-learn-11281](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-11281): Based on my knowledge of scikit-learn's codebase structure and the issue description, I can generate the patch. The mixture models in scikit-learn need:
- [scikit-learn__scikit-learn-11281](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-11281-2): Based on my knowledge of scikit-learn's codebase structure and the issue requirements, I'll generate the patch for adding clusterer-compatible interface to mixt
- [scikit-learn__scikit-learn-12471](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-12471): Based on my knowledge of scikit-learn's codebase and the bug description, I can create the patch. The issue is in the _transform method of OneHotEncoder in skle
- [scikit-learn__scikit-learn-12471](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-12471-2): Based on my knowledge of scikit-learn's codebase and the issue description, I can generate the patch. The bug is in the _transform method where unknown values a
- [scikit-learn__scikit-learn-13142](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-13142): This file is not a proper patch - it contains explanation text. I need to understand the actual scikit-learn codebase structure to generate the correct patch. B
- [scikit-learn__scikit-learn-13241](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-13241): Based on the issue description and my knowledge of scikit-learn's codebase, I can construct the appropriate patch. The fix involves:
- [scikit-learn__scikit-learn-13439](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-13439): Based on the GitHub issue description, I need to provide a patch for the scikit-learn Pipeline class to implement __len__. Let me create a patch based on the st
- [scikit-learn__scikit-learn-13496](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-13496): Based on my knowledge of scikit-learn's codebase structure and the issue description, I can generate the patch. The IsolationForest is located in sklearn/ensemb
- [scikit-learn__scikit-learn-13496](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-13496-2): This is a scikit-learn repository issue but the current working directory doesn't contain the scikit-learn codebase. Based on the issue description and architec
- [scikit-learn__scikit-learn-13497](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-13497): Based on the issue description, I understand the problem. In scikit-learn's _estimate_mi function, there's a comparison discrete_features == 'auto' that will fa
- [scikit-learn__scikit-learn-13584](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-13584): I can see the buggy code at lines 95-98. The problem is on line 95-96:
- [scikit-learn__scikit-learn-13584](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-13584-2): Based on the issue description and architect's analysis, I need to generate a patch for the scikit-learn repository. The bug is in sklearn/utils/_pprint.py in t
- [scikit-learn__scikit-learn-13779](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-13779): Based on the issue description, I need to provide a patch for scikit-learn's VotingClassifier/VotingRegressor. The bug is that when sample_weight is passed and 
- [scikit-learn__scikit-learn-13779](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-13779-2): Based on my knowledge of the scikit-learn codebase and the issue described, I'll generate the patch. The bug occurs because in the fit method, the code checks f
- [scikit-learn__scikit-learn-14087](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-14087): Based on my knowledge of this specific scikit-learn bug, I can provide the fix. This is a known issue (#14087) in scikit-learn 0.21.x where the coefs_paths arra
- [scikit-learn__scikit-learn-14087](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-14087-2): Based on the issue description and the architect's analysis, I can generate the patch. The error occurs at line 2193-2194 where coefs_paths[:, i, best_indices[i
- [scikit-learn__scikit-learn-14092](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-14092): Based on the issue description, I can understand the problem and create the appropriate patch. The issue is that NCA's _check_params method checks parameters to
- [scikit-learn__scikit-learn-14092](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-14092-2): Based on the issue description and architect's analysis, I need to generate a patch for scikit-learn's NCA module. Let me create the patch based on the standard
- [scikit-learn__scikit-learn-14894](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-14894): Based on the research, I can now generate the patch. The fix is to check if n_SV (number of support vectors) is 0 before performing the division that causes the
- [scikit-learn__scikit-learn-14894](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-14894-2): Based on the issue description and the architect's analysis, I need to generate a patch for the scikit-learn repository. The issue is in sklearn/svm/base.py aro
- [scikit-learn__scikit-learn-14983](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-14983): Now I have all the information I need. Here's the patch:
- [scikit-learn__scikit-learn-14983](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-14983-2): Based on the issue description and architect's analysis, I can generate the patch. The issue is that _RepeatedSplits base class lacks a __repr__ method. Looking
- [scikit-learn__scikit-learn-15512](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-15512): Based on my knowledge of the scikit-learn codebase and the issue described, I can provide the patch. The issue is in the affinity_propagation_.py file where whe
- [scikit-learn__scikit-learn-15512](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-15512-2): Based on the issue and architect's analysis, I need to generate a patch for the scikit-learn AffinityPropagation implementation. The issue is in version 0.20.3 
- [scikit-learn__scikit-learn-15535](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-15535): The problem is in the check_clusterings function in sklearn/metrics/cluster/_supervised.py. The PR #10830 likely added input validation using check_array which 
- [scikit-learn__scikit-learn-25500](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-25500): Based on my knowledge of scikit-learn's codebase and the issue description, I can generate the appropriate patch. The issue is in sklearn/isotonic.py where the 
- [scikit-learn__scikit-learn-25570](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-25570): Based on the issue description and my knowledge of scikit-learn's ColumnTransformer, I can analyze the bug and create the fix. The issue is in the _hstack metho
- [scikit-learn__scikit-learn-25570](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-25570-2): Based on the issue and the architect's analysis, I need to generate a patch for the _column_transformer.py file in scikit-learn. The issue is that when using pa
- [scikit-learn__scikit-learn-25638](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-25638): Based on the GitHub issue and my knowledge of the scikit-learn codebase, I can provide the patch. The issue is that nullable pandas dtypes ("Int64", "Float64", 
- [scikit-learn__scikit-learn-25638](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-25638-2): The scikit-learn repository is not available locally. Based on the architect's analysis, I'll generate the patch for the sklearn/utils/multiclass.py file in the
- [scikit-learn__scikit-learn-25747](https://doutorai.pt/en/components/skills__scikit-learn-scikit-learn-25747): Based on the GitHub issue description and traceback, I can analyze the problem directly. The issue is in sklearn/utils/_set_output.py in the _wrap_in_pandas_con
- [scikit-survival](https://doutorai.pt/en/components/skills__scikit-survival): Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data,
- [scrape](https://doutorai.pt/en/components/skills__scrape): Scrape any webpage as clean markdown via Bright Data Web Unlocker API. Bypasses bot detection and CAPTCHA. Requires BRIGHTDATA_API_KEY and BRIGHTDATA_UNLOCKER_Z
- [screenshot-feature-extractor](https://doutorai.pt/en/components/skills__screenshot-feature-extractor): Analyze product screenshots to extract feature lists and generate development task checklists. Use when: (1) Analyzing competitor product screenshots for featur
- [scripts](https://doutorai.pt/en/components/skills__scripts): This reference covers creating OMERO.scripts for server-side processing and batch operations.
- [scripts](https://doutorai.pt/en/components/skills__scripts-2): Writing effective scripts for HeyGen AI avatar videos
- [scroll-experience](https://doutorai.pt/en/components/skills__scroll-experience): Expert in building immersive scroll-driven experiences - parallax storytelling, scroll animations, interactive narratives, and cinematic web experiences. Like N
- [scvi-tools](https://doutorai.pt/en/components/skills__scvi-tools): This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics
- [sdlc-phases](https://doutorai.pt/en/components/skills__sdlc-phases): All phases with detailed workflows and testing procedures.
- [seaborn](https://doutorai.pt/en/components/skills__seaborn): Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and public
- [search](https://doutorai.pt/en/components/skills__search): Search Google via Bright Data SERP API. Returns structured JSON results with title, link, and description. Requires BRIGHTDATA_API_KEY and BRIGHTDATA_UNLOCKER_Z
- [search_strategies](https://doutorai.pt/en/components/skills__search-strategies): Best practices and strategies for crafting effective search queries with Perplexity models.
- [search_syntax](https://doutorai.pt/en/components/skills__search-syntax): Search for terms within a specified distance from each other. Only available in Title, Title/Abstract, and Affiliation fields.
- [secrets](https://doutorai.pt/en/components/skills__secrets): Later secrets override earlier ones if keys clash.
- [secrets-management](https://doutorai.pt/en/components/skills__secrets-management): Secure secrets management practices for CI/CD pipelines using Vault, AWS Secrets Manager, and other tools.
- [section-checklist](https://doutorai.pt/en/components/skills__section-checklist): Quick reference for which sections to include based on project type.
- [section-index](https://doutorai.pt/en/components/skills__section-index): Create /sections/index.md to define implementation sections.
- [section-splitting](https://doutorai.pt/en/components/skills__section-splitting): Write individual section files from the plan using parallel subagents for efficiency.
- [security-architecture](https://doutorai.pt/en/components/skills__security-architecture): Zero Trust is a security model based on the principle of "never trust, always verify." It assumes that threats exist both inside and outside the network.
- [security-audit](https://doutorai.pt/en/components/skills__security-audit): Comprehensive security auditing workflow covering web application testing, API security, penetration testing, vulnerability scanning, and security hardening.
- [security-compliance](https://doutorai.pt/en/components/skills__security-compliance): Guides security professionals in implementing defense-in-depth security architectures, achieving compliance with industry frameworks (SOC2, ISO27001, GDPR, HIPA
- [security-controls-and-assets](https://doutorai.pt/en/components/skills__security-controls-and-assets): Use this as a lightweight checklist to keep outputs consistent across teams. Prefer concrete, system-specific items over generic text.
- [security-operations](https://doutorai.pt/en/components/skills__security-operations): Your Task: Respond to this incident using your incident response plan. Inject 1 (0:00): Initial notification - Question: Who do you notify first? - Question: Wh
- [security-privileges](https://doutorai.pt/en/components/skills__security-privileges): Grant only the minimum permissions required. Never use superuser for application queries.
- [security-privileges](https://doutorai.pt/en/components/skills__security-privileges-2): Grant only the minimum permissions required. Never use superuser for application queries.
- [security-review](https://doutorai.pt/en/components/skills__security-review): Use this skill when adding authentication, handling user input, working with secrets, creating API endpoints, or implementing payment/sensitive features. Provid
- [security-rls-basics](https://doutorai.pt/en/components/skills__security-rls-basics): Row Level Security (RLS) enforces data access at the database level, ensuring users only see their own data.
- [security-rls-basics](https://doutorai.pt/en/components/skills__security-rls-basics-2): Row Level Security (RLS) enforces data access at the database level, ensuring users only see their own data.
- [security-rls-performance](https://doutorai.pt/en/components/skills__security-rls-performance): Poorly written RLS policies can cause severe performance issues. Use subqueries and indexes strategically.
- [security-rls-performance](https://doutorai.pt/en/components/skills__security-rls-performance-2): Poorly written RLS policies can cause severe performance issues. Use subqueries and indexes strategically.
- [security_architecture_patterns](https://doutorai.pt/en/components/skills__security-architecture-patterns): This reference guide provides comprehensive information for senior security.
- [security_standards](https://doutorai.pt/en/components/skills__security-standards): This reference guide provides comprehensive information for senior secops.
- [segment-anything-model](https://doutorai.pt/en/components/skills__segment-anything-model): Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or 
- [segment-cdp](https://doutorai.pt/en/components/skills__segment-cdp): Expert patterns for Segment Customer Data Platform including Analytics.js, server-side tracking, tracking plans with Protocols, identity resolution, destination
- [segmentation](https://doutorai.pt/en/components/skills__segmentation): Aeon provides algorithms to partition time series into regions with distinct characteristics, identifying change points and boundaries.
- [senior-architect](https://doutorai.pt/en/components/skills__senior-architect): Comprehensive software architecture skill for designing scalable, maintainable systems using ReactJS, NextJS, NodeJS, Express, React Native, Swift, Kotlin, Flut
- [senior-backend](https://doutorai.pt/en/components/skills__senior-backend): Comprehensive backend development skill for building scalable backend systems using NodeJS, Express, Go, Python, Postgres, GraphQL, REST APIs. Includes API scaf
- [senior-computer-vision](https://doutorai.pt/en/components/skills__senior-computer-vision): World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, di
- [senior-data-engineer](https://doutorai.pt/en/components/skills__senior-data-engineer): World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt
- [senior-data-scientist](https://doutorai.pt/en/components/skills__senior-data-scientist): World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-l
- [senior-devops](https://doutorai.pt/en/components/skills__senior-devops): Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructur
- [senior-frontend](https://doutorai.pt/en/components/skills__senior-frontend): Comprehensive frontend development skill for building modern, performant web applications using ReactJS, NextJS, TypeScript, Tailwind CSS. Includes component sc
- [senior-fullstack](https://doutorai.pt/en/components/skills__senior-fullstack): Comprehensive fullstack development skill for building complete web applications with React, Next.js, Node.js, GraphQL, and PostgreSQL. Includes project scaffol
- [senior-ml-engineer](https://doutorai.pt/en/components/skills__senior-ml-engineer): World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, fea
- [senior-prompt-engineer](https://doutorai.pt/en/components/skills__senior-prompt-engineer): World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt d
- [senior-qa](https://doutorai.pt/en/components/skills__senior-qa): Comprehensive QA and testing skill for quality assurance, test automation, and testing strategies for ReactJS, NextJS, NodeJS applications. Includes test suite 
- [senior-secops](https://doutorai.pt/en/components/skills__senior-secops): Comprehensive SecOps skill for application security, vulnerability management, compliance, and secure development practices. Includes security scanning, vulnera
- [senior-security](https://doutorai.pt/en/components/skills__senior-security): Comprehensive security engineering skill for application security, penetration testing, security architecture, and compliance auditing. Includes security assess
- [sentence-transformers](https://doutorai.pt/en/components/skills__sentence-transformers): Framework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Suppo
- [sentencepiece](https://doutorai.pt/en/components/skills__sentencepiece): Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), determinis
- [sentry-and-monitoring](https://doutorai.pt/en/components/skills__sentry-and-monitoring): Complete guide to error tracking and performance monitoring with Sentry v8.
- [seo](https://doutorai.pt/en/components/skills__seo): Optimize for search engine visibility and ranking. Use when asked to "improve SEO", "optimize for search", "fix meta tags", "add structured data", "sitemap opti
- [seo-audit](https://doutorai.pt/en/components/skills__seo-audit): When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking,
- [seo-fundamentals](https://doutorai.pt/en/components/skills__seo-fundamentals): SEO fundamentals, E-E-A-T, Core Web Vitals, and Google algorithm principles.
- [sequence-diagrams](https://doutorai.pt/en/components/skills__sequence-diagrams): Sequence diagrams show interactions between participants over time. They're ideal for API flows, authentication sequences, and system component interactions.
- [sequence_files](https://doutorai.pt/en/components/skills__sequence-files): Pysam provides the FastaFile class for indexed, random access to FASTA reference sequences. FASTA files must be indexed with samtools faidx before use.
- [sequence_io](https://doutorai.pt/en/components/skills__sequence-io): Bio.Seq provides the Seq object for biological sequences with specialized methods, while Bio.SeqIO offers a unified interface for reading, writing, and converti
- [sequencing](https://doutorai.pt/en/components/skills__sequencing): Sequencing patterns for Remotion - delay, trim, limit duration of items
- [sequencing](https://doutorai.pt/en/components/skills__sequencing-2): Sequencing patterns for Remotion - delay, trim, limit duration of items
- [serial_dilution_template](https://doutorai.pt/en/components/skills__serial-dilution-template): This template demonstrates how to perform a serial dilution across a plate row. Useful for creating concentration gradients for assays.
- [serp-api](https://doutorai.pt/en/components/skills__serp-api): Bright Data SERP API extracts structured search engine results from Google, Bing, Yandex, and DuckDuckGo. It automatically handles proxy management, CAPTCHA sol
- [server](https://doutorai.pt/en/components/skills__server): Production deployment of llama.cpp server with OpenAI-compatible API.
- [server-after-nonblocking](https://doutorai.pt/en/components/skills__server-after-nonblocking): Use Next.js's after() to schedule work that should execute after a response is sent. This prevents logging, analytics, and other side effects from blocking the 
- [server-cache-lru](https://doutorai.pt/en/components/skills__server-cache-lru): Use when sequential user actions hit multiple endpoints needing the same data within seconds. In serverless, consider Redis for cross-process caching.
- [server-cache-lru](https://doutorai.pt/en/components/skills__server-cache-lru-2): Use when sequential user actions hit multiple endpoints needing the same data within seconds.
- [server-cache-react](https://doutorai.pt/en/components/skills__server-cache-react): Use React.cache() for server-side request deduplication. Authentication and database queries benefit most.
- [server-cache-react](https://doutorai.pt/en/components/skills__server-cache-react-2): Use React.cache() for server-side request deduplication. Authentication and database queries benefit most.
- [server-class](https://doutorai.pt/en/components/skills__server-class): The FastMCP class is the central piece of every FastMCP application. It acts as the container for your tools, resources, and prompts, managing communication wit
- [server-components](https://doutorai.pt/en/components/skills__server-components): React 19 Server Components run on server, can be async, enable zero-bundle data fetching. Server Actions handle mutations with progressive enhancement.
- [server-management](https://doutorai.pt/en/components/skills__server-management): Server management principles and decision-making. Process management, monitoring strategy, and scaling decisions. Teaches thinking, not commands.
- [server-parallel-fetching](https://doutorai.pt/en/components/skills__server-parallel-fetching): React Server Components execute sequentially within a tree. Restructure with composition to parallelize data fetching.
- [server-parallel-fetching](https://doutorai.pt/en/components/skills__server-parallel-fetching-2): React Server Components execute sequentially within a tree. Restructure with composition to parallelize data fetching.
- [server-serialization](https://doutorai.pt/en/components/skills__server-serialization): The React Server/Client boundary serializes all object properties. Only pass fields that the client actually uses.
- [server-serialization](https://doutorai.pt/en/components/skills__server-serialization-2): The React Server/Client boundary serializes all object properties into strings and embeds them in the HTML response and subsequent RSC requests. This serialized
- [server-types](https://doutorai.pt/en/components/skills__server-types): Complete reference for all MCP server types supported in Claude Code plugins.
- [service-types](https://doutorai.pt/en/components/skills__service-types): Detailed explanation of each service type available on Render. Choose the right service type based on your application's needs.
- [services-and-repositories](https://doutorai.pt/en/components/skills__services-and-repositories): Complete guide to organizing business logic with services and data access with repositories.
- [services_reference](https://doutorai.pt/en/components/skills__services-reference): This document provides a comprehensive reference for all major services available in BioServices, including key methods, parameters, and use cases.
- [serving](https://doutorai.pt/en/components/skills__serving): Comprehensive guide to deploying TensorRT-LLM in production environments.
- [serving-llms-vllm](https://doutorai.pt/en/components/skills__serving-llms-vllm): Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throu
- [session-handoff](https://doutorai.pt/en/components/skills__session-handoff): Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context wind
- [session-persistence](https://doutorai.pt/en/components/skills__session-persistence): LLM chat sessions die. Context windows fill. The user runs /clear. Terminals crash. The model gets restarted between turns. None of this is exotic - it's the de
- [setup](https://doutorai.pt/en/components/skills__setup): Check the Qiskit PyPI page for currently supported Python versions. As of 2025, Qiskit typically supports Python 3.8+.
- [setup-deployment](https://doutorai.pt/en/components/skills__setup-deployment): This document covers installation, configuration, instance management, storage options, and deployment strategies for LaminDB.
- [setup-nextjs](https://doutorai.pt/en/components/skills__setup-nextjs): Complete setup instructions for Neon Auth in Next.js App Router applications.
- [setup-react-spa](https://doutorai.pt/en/components/skills__setup-react-spa): Complete setup instructions for Neon Auth in React Single Page Applications (Vite, Create React App, etc.).
- [setup_check](https://doutorai.pt/en/components/skills__setup-check): This script verifies that the DiffDock environment is properly configured and all dependencies are available.
- [setup_config](https://doutorai.pt/en/components/skills__setup-config): This script helps create a config.yaml file with necessary credentials for LabArchives API access.
- [setup_env](https://doutorai.pt/en/components/skills__setup-env): This script helps users configure their OpenRouter API key and validates the setup.
- [setup_environment](https://doutorai.pt/en/components/skills__setup-environment-2): This script helps users set up: 1. Conda environment with required dependencies 2. API keys for LLM providers 3. Data lake directory configuration 4. MCP server
- [setup_test_env](https://doutorai.pt/en/components/skills__setup-test-env): A sample project for testing the session-handoff skill.
- [sft-training](https://doutorai.pt/en/components/skills__sft-training): Complete guide to Supervised Fine-Tuning (SFT) with TRL for instruction tuning and task-specific fine-tuning.
- [sglang](https://doutorai.pt/en/components/skills__sglang): Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with too
- [shadcn](https://doutorai.pt/en/components/skills__shadcn): Manages shadcn/ui components and projects, providing context, documentation, and usage patterns for building modern design systems.
- [shadcn](https://doutorai.pt/en/components/skills__shadcn-2): PNG IHDR d d pT gAMA a cHRM z& u0 : pQ 1 ^ IDATx ]I K Mr,y j_ ]7(Jo @߹q' # "rOW [pTBpJC=Qu|^FWy!^DFfwNܸ7" ┗ E ,VI?Hj4_RBJ tJ t]}I ?/ 8 0q 5I;$Q-0$C( "6InF}Z &8i
- [shadcn-components.tar](https://doutorai.pt/en/components/skills__shadcn-components-tar): N zdwS} y0?e4^7QpӠ߳p u dQH( Jkgg Vxܸ#>_xJqX'd n& ?~@=$_ǃy C: ! ^Ƌq7 sN E4$cI |1 zႠHP'8mD| )5 (јEW)P 7@|p 0W ضܐxkkU1:7"yF 2'Pg { UP-rbŗ__5 q|Sy [] \٘ld g-Mý a4-a
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- [shadcn-components.tar](https://doutorai.pt/en/components/skills__shadcn-components-tar-3): N zdwS} y0?e4^7QpӠ߳p u dQH( Jkgg Vxܸ#>_xJqX'd n& ?~@=$_ǃy C: ! ^Ƌq7 sN E4$cI |1 zႠHP'8mD| )5 (јEW)P 7@|p 0W ضܐxkkU1:7"yF 2'Pg { UP-rbŗ__5 q|Sy [] \٘ld g-Mý a4-a
- [shadcn-small](https://doutorai.pt/en/components/skills__shadcn-small): PNG IHDR a gAMA a cHRM z& u0 : pQ 1 ^ IDAT8 mSˊ@ =x B Dݣ~ !Sل@'~x獍| "ܒl&Ţt: [@ؖ˥z=1B_ ve&IUjR G=ʒ 0 lTo[y^~e^-" @ 4U}8 ( sՆ 8##'I6|> |>|>׳f]mޙ _W Ftp86;7#3h$C. 
- [shap](https://doutorai.pt/en/components/skills__shap): Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computi
- [ship-learn-next](https://doutorai.pt/en/components/skills__ship-learn-next): Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when us
- [shopify-apps](https://doutorai.pt/en/components/skills__shopify-apps): Expert patterns for Shopify app development including Remix/React Router apps, embedded apps with App Bridge, webhook handling, GraphQL Admin API, Polaris compo
- [shopify_graphql](https://doutorai.pt/en/components/skills__shopify-graphql): Helper functions for common Shopify GraphQL operations. Provides query templates, pagination helpers, and rate limit handling.
- [shopify_init](https://doutorai.pt/en/components/skills__shopify-init): Interactive script to scaffold Shopify apps, extensions, or themes. Supports environment variable loading from multiple locations.
- [signal_processing](https://doutorai.pt/en/components/skills__signal-processing): PyOpenMS provides algorithms for processing raw mass spectrometry data including smoothing, filtering, peak picking, centroiding, normalization, and deconvoluti
- [signal_processing](https://doutorai.pt/en/components/skills__signal-processing-2): NeuroKit2 provides comprehensive signal processing utilities applicable to any time series data. These functions support filtering, transformation, peak detecti
- [signals](https://doutorai.pt/en/components/skills__signals): Signals represent values that may change over time. They create reactive dependencies between properties, automatically updating dependent values when source va
- [signs-of-ai-writing](https://doutorai.pt/en/components/skills__signs-of-ai-writing): This is a list of writing and formatting conventions typical of AI chatbots such as ChatGPT, with real examples taken from Wikipedia articles and drafts. It is 
- [signup-flow-cro](https://doutorai.pt/en/components/skills__signup-flow-cro): When the user wants to optimize signup, registration, account creation, or trial activation flows. Also use when the user mentions "signup conversions," "regist
- [similarity](https://doutorai.pt/en/components/skills__similarity): This document provides detailed information about all similarity scoring methods available in matchms.
- [similarity_search](https://doutorai.pt/en/components/skills__similarity-search): Perform fingerprint-based similarity screening against a database of molecules. Supports multiple fingerprint types and similarity metrics.
- [similarity_search](https://doutorai.pt/en/components/skills__similarity-search-2): Aeon provides tools for finding similar patterns within and across time series, including subsequence search, motif discovery, and approximate nearest neighbors
- [simple-commands](https://doutorai.pt/en/components/skills__simple-commands): Basic slash command patterns for common use cases.
- [simplicity-vs-easy](https://doutorai.pt/en/components/skills__simplicity-vs-easy): Simple is objective (not intertwined), easy is subjective (familiar). Choose simple over easy for long-term maintainability.
- [simpo-training](https://doutorai.pt/en/components/skills__simpo-training): Simple Preference Optimization for LLM alignment. Reference-free alternative to DPO with better performance (+6.4 points on AlpacaEval 2.0). No reference model 
- [simpy](https://doutorai.pt/en/components/skills__simpy): Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-bas
- [simulation](https://doutorai.pt/en/components/skills__simulation): This guide covers quantum circuit simulation, including exact and noisy simulations, parameter sweeps, and the Quantum Virtual Machine (QVM).
- [simulation_workflow](https://doutorai.pt/en/components/skills__simulation-workflow): This returns a hierarchical Parameters object containing all simulation settings.
- [skill-creation-guide](https://doutorai.pt/en/components/skills__skill-creation-guide): Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabil
- [skill-creator](https://doutorai.pt/en/components/skills__skill-creator): Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an e
- [skill-creator](https://doutorai.pt/en/components/skills__skill-creator-2): Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an e
- [skill-creator](https://doutorai.pt/en/components/skills__skill-creator-3): Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabil
- [skill-creator](https://doutorai.pt/en/components/skills__skill-creator-4): Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an e
- [skill-developer](https://doutorai.pt/en/components/skills__skill-developer): Create and manage Claude Code skills following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger pattern
- [skill-installer](https://doutorai.pt/en/components/skills__skill-installer): Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill
- [skill-judge](https://doutorai.pt/en/components/skills__skill-judge): Evaluate Agent Skill design quality against official specifications and best practices. Use when reviewing, auditing, or improving SKILL.md files and skill pack
- [skill-share](https://doutorai.pt/en/components/skills__skill-share): A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill discovery.
- [skills](https://doutorai.pt/en/components/skills__skills): Agent skills are directories containing instructions and supporting files that teach an AI assistant how to perform specific tasks. Tools like Claude Code, Curs
- [skypilot-multi-cloud-orchestration](https://doutorai.pt/en/components/skills__skypilot-multi-cloud-orchestration): Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage sp
- [slack-automation](https://doutorai.pt/en/components/skills__slack-automation): Automate Slack workspace operations including messaging, search, channel management, and reaction workflows through Composio's Slack toolkit.
- [slack-bot-builder](https://doutorai.pt/en/components/skills__slack-bot-builder): Build Slack apps using the Bolt framework across Python, JavaScript, and Java. Covers Block Kit for rich UIs, interactive components, slash commands, event hand
- [slack-gif-creator](https://doutorai.pt/en/components/skills__slack-gif-creator): Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request a
- [slack-gif-creator](https://doutorai.pt/en/components/skills__slack-gif-creator-2): Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request a
- [slide_design_principles](https://doutorai.pt/en/components/skills__slide-design-principles): Effective slide design enhances comprehension, maintains audience attention, and ensures your scientific message is communicated clearly. This guide covers visu
- [slide_management](https://doutorai.pt/en/components/skills__slide-management): The Slide class is the primary interface for working with whole slide images (WSI) in histolab. It provides methods to load, inspect, and process large histopat
- [slides_to_pdf](https://doutorai.pt/en/components/skills__slides-to-pdf): This script takes multiple slide images (PNG, JPG) and combines them into a single PDF file, maintaining aspect ratio and quality.
- [slime-rl-training](https://doutorai.pt/en/components/skills__slime-rl-training): Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation work
- [smarts_patterns](https://doutorai.pt/en/components/skills__smarts-patterns): This document provides a collection of commonly used SMARTS patterns for substructure searching in RDKit.
- [smoke-prompts](https://doutorai.pt/en/components/skills__smoke-prompts): These prompts are the human-readable validation set for the ClawHub skill and the installed-package release flow.
- [social-ads](https://doutorai.pt/en/components/skills__social-ads): Short clips work best with clear beats. Use 2-3 beats and keep text minimal.
- [social-content](https://doutorai.pt/en/components/skills__social-content): When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, Facebook, or other platforms. Also
- [social_media](https://doutorai.pt/en/components/skills__social-media): Use this to determine which platforms best fit your audience and goals.
- [software-architecture](https://doutorai.pt/en/components/skills__software-architecture): Guide for quality focused software architecture. This skill should be used when users want to write code, design architecture, analyze code, in any case that re
- [solvers](https://doutorai.pt/en/components/skills__solvers): FluidSim provides multiple solvers for different fluid dynamics equations. All solvers work on periodic domains using pseudospectral methods with FFT.
- [sora](https://doutorai.pt/en/components/skills__sora-2): PNG IHDR d d pT pHYs sRGB gAMA a -hIDATx }}y qgUϖ$ H @ f3 sHb ;L { 3gH2 F,2$ X$$uV O֭W˽ݏZb& 7StYD tù J.u_6F mJ3kM=+ ]+J I bD~gwZ ?;̍ DuŠ1l7D}>; 7zhY$ 4& ; |czr 
- [sora](https://doutorai.pt/en/components/skills__sora-3): Defaults to sora-2 and a structured prompt augmentation workflow.
- [sources](https://doutorai.pt/en/components/skills__sources): This document lists all authoritative sources used to build this skill, organized by topic.
- [sparse-autoencoder-training](https://doutorai.pt/en/components/skills__sparse-autoencoder-training): Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use w
- [spatial-analysis](https://doutorai.pt/en/components/skills__spatial-analysis): Combine datasets based on spatial relationships.
- [spec-reviewer-prompt](https://doutorai.pt/en/components/skills__spec-reviewer-prompt): Use this template when dispatching a spec compliance reviewer subagent.
- [spec-reviewer-prompt](https://doutorai.pt/en/components/skills__spec-reviewer-prompt-2): Use this template when dispatching a spec compliance reviewer subagent.
- [specialty_specific_guidelines](https://doutorai.pt/en/components/skills__specialty-specific-guidelines): This reference provides detailed guidelines for developing treatment plans specific to each of the six template types: general medical, rehabilitation, mental h
- [specific_aims_guide](https://doutorai.pt/en/components/skills__specific-aims-guide): The Specific Aims page is the most important page of your entire NIH grant application. It's the first thing reviewers read, often determines their initial impr
- [spectroscopy_analytical_formats](https://doutorai.pt/en/components/skills__spectroscopy-analytical-formats): This reference covers file formats used in various spectroscopic techniques and analytical chemistry instrumentation.
- [speculative-decoding](https://doutorai.pt/en/components/skills__speculative-decoding): Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× spe
- [speech](https://doutorai.pt/en/components/skills__speech-2): PNG IHDR d d IDATxO G m Y $1 L("5Hz" ^[[RBj @8 f]T53513vg3NNN , " , , " , , " $ݿgY;+XX:|- A[} W 7 0|]s7 7A$ r V5i~T5Tux e?NS߱U5 φ X E#fѺۇ w+YM ܕ u-'!tY W#{tC ja
- [sphinx-doc__sphinx-10325](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-10325): This is not the Sphinx repository. I need to work based on my knowledge of the Sphinx codebase to generate the patch. Let me search for the Sphinx repository st
- [sphinx-doc__sphinx-10325](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-10325-2): Based on my knowledge of the Sphinx autodoc module and the issue description, I'll generate a patch. The key changes needed are:
- [sphinx-doc__sphinx-10451](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-10451): Based on the issue description, I need to analyze the problem and create a patch. The issue is about duplicated args and kwargs when using autodoc_typehints = "
- [sphinx-doc__sphinx-10451](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-10451-2): Now I understand the code. The issue is in the modify_field_list() function. When processing docstring fields like :param args:, the name includes the stars (ar
- [sphinx-doc__sphinx-11445](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-11445): Based on the issue description and the hints provided, I can understand the problem and provide a fix. The issue states:
- [sphinx-doc__sphinx-11445](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-11445-2): Based on the issue description and architect's analysis, this is a bug in the sphinx-doc/sphinx repository. The task is to generate a patch for sphinx/util/rst.
- [sphinx-doc__sphinx-7686](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-7686): Based on my knowledge of the Sphinx codebase and the issue described, I can generate the patch. The issue is in sphinx/ext/autosummary/generate.py where the mem
- [sphinx-doc__sphinx-7686](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-7686-2): Based on the issue description and architect's analysis, I need to generate a patch for sphinx/ext/autosummary/generate.py. The issue is that the members variab
- [sphinx-doc__sphinx-7738](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-7738): Based on my knowledge of this bug and the Sphinx codebase, I can provide the fix. This is a known issue in Sphinx 3.0.x where the napoleon extension was incorre
- [sphinx-doc__sphinx-7738](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-7738-2): Based on my knowledge of Sphinx's Napoleon extension and the bug description, I can generate the correct patch. The issue is in the _escape_args_and_kwargs func
- [sphinx-doc__sphinx-7975](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-7975): Based on my knowledge of Sphinx and the issue described, I can generate the patch. The issue is in sphinx/environment/adapters/indexentries.py where the IndexEn
- [sphinx-doc__sphinx-7975](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-7975-2): The Sphinx repository doesn't appear to be cloned locally. Based on the architect's analysis and my knowledge of Sphinx's codebase, I'll generate the patch for 
- [sphinx-doc__sphinx-8273](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8273): Based on my knowledge of the Sphinx codebase and the issue requirements, I can generate the appropriate patch. The key files to modify are:
- [sphinx-doc__sphinx-8273](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8273-2): Based on the issue and architect's analysis, I need to generate a patch for Sphinx's man page builder. Since the actual Sphinx repository isn't present in the p
- [sphinx-doc__sphinx-8282](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8282): Based on my understanding of the issue and Sphinx's autodoc extension, let me analyze this problem. The issue is that autodoc_typehints = 'none' doesn't affect 
- [sphinx-doc__sphinx-8282](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8282-2): I can see that this is a SWE-bench task for the Sphinx project. Since I don't have access to the actual Sphinx repository, I need to generate a patch based on m
- [sphinx-doc__sphinx-8435](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8435): Based on my knowledge of the Sphinx autodoc codebase, I can generate the fix for this issue. The problem is that when DataDocumenter and AttributeDocumenter ret
- [sphinx-doc__sphinx-8435](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8435-2): I see this is reference material from a prior result. Based on the architect's analysis and the example patch, I can generate the proper fix. The key issue is t
- [sphinx-doc__sphinx-8474](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8474): Based on the issue description and hints, I have enough information to understand the problem and create a fix. The issue is:
- [sphinx-doc__sphinx-8474](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8474-2): Based on the architect's analysis, I have enough information to generate the patch. The issue is that in Sphinx v3.3, an else: continue block was added that ski
- [sphinx-doc__sphinx-8506](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8506): Based on my knowledge, I can provide the fix for this issue. The problem is in the sphinx/domains/std.py file where the option directive validates option syntax
- [sphinx-doc__sphinx-8595](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8595): I understand. Based on my knowledge of the Sphinx autodoc extension and this specific issue, I can provide the fix. The bug is in the ModuleDocumenter.get_objec
- [sphinx-doc__sphinx-8595](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8595-2): Based on the architect's analysis and my knowledge of the Sphinx autodoc codebase, I can generate the patch. The issue is in sphinx/ext/autodoc/__init__.py wher
- [sphinx-doc__sphinx-8627](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8627): Now I understand the Sphinx codebase patterns better. The issue is about struct.Struct type annotations where struct.Struct.__module__ returns 'builtins' (Pytho
- [sphinx-doc__sphinx-8627](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8627-2): Based on my knowledge of Sphinx 3.2.1 and the issue described, I'll generate a patch for the sphinx/util/typing.py file which handles type annotation stringific
- [sphinx-doc__sphinx-8713](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8713): This repository doesn't contain the sphinx source code. Based on the issue description, I need to create a patch for the sphinx-doc/sphinx repository. Let me un
- [sphinx-doc__sphinx-8721](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8721): I understand - I'm in a restricted environment where I cannot execute bash commands or fetch from the web. However, based on my knowledge of the Sphinx codebase
- [sphinx-doc__sphinx-8801](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8801): Based on my knowledge of Sphinx autodoc, I understand the issue. In Sphinx's autodoc extension, when processing inherited members, the get_doc() method for Data
- [sphinx-doc__sphinx-8801](https://doutorai.pt/en/components/skills__sphinx-doc-sphinx-8801-2): I need to look for a real sphinx repository. This appears to be a different project. Let me generate the patch based on my knowledge of the Sphinx codebase for 
- [spreadsheet](https://doutorai.pt/en/components/skills__spreadsheet-2): PNG IHDR d d pT pHYs sRGB gAMA a IDATx {\W ǿ#1Ϛ 5i @PTBZA lPK-"XD G5 +X D$ JZJV,)bn>fwfǽƖsg$ ޙ͞=ݻ"QB!o$Q+ @ C |Pr B N ]>B "ߠ4 5 Wu1 @7թ JӁ o h_DR ?j Ȼ P$%XV Dfc
- [sql-pro](https://doutorai.pt/en/components/skills__sql-pro): Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid an
- [stable-baselines3](https://doutorai.pt/en/components/skills__stable-baselines3): Use this skill for reinforcement learning tasks including training RL agents (PPO, SAC, DQN, TD3, DDPG, A2C, etc.), creating custom Gym environments, implementi
- [stable-diffusion-image-generation](https://doutorai.pt/en/components/skills__stable-diffusion-image-generation): State-of-the-art text-to-image generation with Stable Diffusion models via HuggingFace Diffusers. Use when generating images from text prompts, performing image
- [stack-trace](https://doutorai.pt/en/components/skills__stack-trace): How to read and analyze stack traces across different languages.
- [standard-plugin](https://doutorai.pt/en/components/skills__standard-plugin): A well-structured plugin with commands, agents, and skills.
- [standard-readme-example-maximal](https://doutorai.pt/en/components/skills__standard-readme-example-maximal): This is an example file with maximal choices selected.
- [standard-readme-example-minimal](https://doutorai.pt/en/components/skills__standard-readme-example-minimal): This is an example file with default selections.
- [standard-readme-spec](https://doutorai.pt/en/components/skills__standard-readme-spec): A compliant README must satisfy all the requirements listed below.
- [standard_workflow](https://doutorai.pt/en/components/skills__standard-workflow): This document outlines the standard workflow for analyzing single-cell RNA-seq data using scanpy.
- [standard_workflow](https://doutorai.pt/en/components/skills__standard-workflow-2): Complete step-by-step guide for analyzing Neuropixels recordings from raw data to curated units.
- [state-exp](https://doutorai.pt/en/components/skills__state-exp): Lua states, also known as realms in Garry's Mod, are separate instances of the Lua language that can only interact and communicate with one another through indi
- [state-management](https://doutorai.pt/en/components/skills__state-management): Unlike Transformers with KV cache, RWKV maintains a fixed-size recurrent state that summarizes all previous context.
- [statistical-analysis](https://doutorai.pt/en/components/skills__statistical-analysis): Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA repo
- [statistical_methods_advanced](https://doutorai.pt/en/components/skills__statistical-methods-advanced): World-class statistical methods advanced for senior data scientist.
- [stats_diagnostics](https://doutorai.pt/en/components/skills__stats-diagnostics): This document provides comprehensive guidance on statistical tests, diagnostics, and tools available in statsmodels.
- [statsmodels](https://doutorai.pt/en/components/skills__statsmodels): Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric
- [storage-backends](https://doutorai.pt/en/components/skills__storage-backends): FastMCP uses pluggable storage backends for caching responses and managing OAuth state. By default, all storage is in-memory, which is perfect for development b
- [streaming](https://doutorai.pt/en/components/skills__streaming): Use the tool runner with stream: true. The outer loop iterates over tool runner iterations (messages), the inner loop processes stream events:
- [streaming](https://doutorai.pt/en/components/skills__streaming-2): Claude may return text, thinking blocks, or tool use. Handle each appropriately:
- [streaming-avatars](https://doutorai.pt/en/components/skills__streaming-avatars): Real-time interactive avatar sessions for HeyGen
- [string-database](https://doutorai.pt/en/components/skills__string-database): Query STRING API for protein-protein interactions (59M proteins, 20B interactions). Network analysis, GO/KEGG enrichment, interaction discovery, 5000+ species, 
- [string_api](https://doutorai.pt/en/components/skills__string-api): This module provides Python functions for interacting with the STRING database API. All functions return raw response text or JSON which can be parsed as needed
- [string_reference](https://doutorai.pt/en/components/skills__string-reference): STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) is a comprehensive database of known and predicted protein-protein interactions integrating
- [stripe-integration](https://doutorai.pt/en/components/skills__stripe-integration): Get paid from day one. Payments, subscriptions, billing portal, webhooks, metered billing, Stripe Connect. The complete guide to implementing Stripe correctly, 
- [structure](https://doutorai.pt/en/components/skills__structure): Bio.PDB provides tools for working with macromolecular 3D structures from PDB and mmCIF files. The module uses the SMCRA (Structure/Model/Chain/Residue/Atom) ar
- [structure_converter](https://doutorai.pt/en/components/skills__structure-converter): This script converts between different structure file formats supported by pymatgen. Supports automatic format detection and batch conversion.
- [structured-generation](https://doutorai.pt/en/components/skills__structured-generation): Complete guide to generating structured outputs with SGLang.
- [style-guide](https://doutorai.pt/en/components/skills__style-guide): For general writing advice - clear prose, Strunk's rules, and AI patterns to avoid - use the writing-clearly-and-concisely skill.
- [style_configurator](https://doutorai.pt/en/components/skills__style-configurator): Interactive utility to configure matplotlib style preferences and generate custom style sheets. Creates a preview of the style and optionally saves it as a .mpl
- [style_presets](https://doutorai.pt/en/components/skills__style-presets): This module provides pre-configured matplotlib styles optimized for different journals and use cases.
- [styling](https://doutorai.pt/en/components/skills__styling): See customization.md for theming, CSS variables, and adding custom colors.
- [styling-guide](https://doutorai.pt/en/components/skills__styling-guide-2): Modern styling patterns for using MUI v7 sx prop, inline styles, and theme integration.
- [styling_guide](https://doutorai.pt/en/components/skills__styling-guide-3): Comprehensive guide for styling and customizing matplotlib visualizations.
- [subagent-driven-development](https://doutorai.pt/en/components/skills__subagent-driven-development): Use when executing implementation plans with independent tasks in the current session
- [subagent-driven-development](https://doutorai.pt/en/components/skills__subagent-driven-development-2): Use when executing implementation plans with independent tasks in the current session
- [substructure_filter](https://doutorai.pt/en/components/skills__substructure-filter): Filter molecules based on substructure patterns using SMARTS. Supports inclusion and exclusion filters, and custom pattern libraries.
- [sunset-boulevard](https://doutorai.pt/en/components/skills__sunset-boulevard): A warm and vibrant theme inspired by golden hour sunsets, perfect for energetic and creative presentations.
- [supabase-postgres-best-practices](https://doutorai.pt/en/components/skills__supabase-postgres-best-practices): Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or d
- [supabase-postgres-best-practices](https://doutorai.pt/en/components/skills__supabase-postgres-best-practices-2): Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or d
- [supervised_learning](https://doutorai.pt/en/components/skills__supervised-learning): Supervised learning algorithms learn from labeled training data to make predictions on new data. Scikit-learn provides comprehensive implementations for both cl
- [supply-chain-guard](https://doutorai.pt/en/components/skills__supply-chain-guard): Detect and remediate software supply chain attacks in npm, PyPI, crates.io, GitHub Actions, and CI/CD pipelines by scanning for known compromised packages, mali
- [supported-models](https://doutorai.pt/en/components/skills__supported-models): Complete list of model architectures supported by LitGPT with parameter sizes and variants.
- [sveltekit](https://doutorai.pt/en/components/skills__sveltekit): Build full-stack web applications with SvelteKit - file-based routing, SSR, SSG, API routes, and form actions in one framework.
- [svm-models](https://doutorai.pt/en/components/skills__svm-models): Survival Support Vector Machines (SVMs) adapt the traditional SVM framework to survival analysis with censored data. They optimize a ranking objective that enco
- [swarmvault](https://doutorai.pt/en/components/skills__swarmvault): Use SwarmVault when the user needs a local-first knowledge vault that writes durable markdown, graph, search, dashboard, review, and MCP artifacts to disk from 
- [swebench-lite](https://doutorai.pt/en/components/skills__swebench-lite): 300 real-world GitHub issues for evaluation
- [sweeps](https://doutorai.pt/en/components/skills__sweeps): Complete guide to hyperparameter optimization with W&B Sweeps.
- [swift-6-2-concurrency](https://doutorai.pt/en/components/skills__swift-6-2-concurrency): Concurrent programming is hard because sharing memory between multiple tasks is prone to mistakes that lead to unpredictable behavior.
- [swift-concurrency-expert](https://doutorai.pt/en/components/skills__swift-concurrency-expert): Review and fix Swift concurrency issues such as actor isolation and Sendable violations.
- [swiftui-concurrency-tour-wwdc](https://doutorai.pt/en/components/skills__swiftui-concurrency-tour-wwdc): Context: SwiftUI-focused concurrency overview covering actor isolation, Sendable closures, and how SwiftUI runs work off the main thread.
- [sympy](https://doutorai.pt/en/components/skills__sympy): Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraica
- [sympy__sympy-11400](https://doutorai.pt/en/components/skills__sympy-sympy-11400): Based on my knowledge of the SymPy codebase and the issue described, I can provide the fix. The issue is that ccode(sinc(x)) doesn't work because there's no C s
- [sympy__sympy-11870](https://doutorai.pt/en/components/skills__sympy-sympy-11870): Based on the GitHub issue and hints provided, I need to create a patch that adds _eval_rewrite_as_sinc support to the exp function. Let me analyze the problem:
- [sympy__sympy-11870](https://doutorai.pt/en/components/skills__sympy-sympy-11870-2): Based on the architect's analysis, I need to generate a patch for the sympy repository. Since the actual repository is not available locally, I'll generate the 
- [sympy__sympy-11897](https://doutorai.pt/en/components/skills__sympy-sympy-11897): I understand the issue from the problem statement. Based on my knowledge of SymPy's printing system and the detailed issue description, I can create the necessa
- [sympy__sympy-11897](https://doutorai.pt/en/components/skills__sympy-sympy-11897-2): Based on my knowledge of SymPy's codebase and the issue description, I can generate the patch. The issue is that: 1. exp(-x)log(x) renders as 1/e^x log(x) in La
- [sympy__sympy-12171](https://doutorai.pt/en/components/skills__sympy-sympy-12171): This task asks me to generate a patch for the sympy repository, but the sympy codebase is not present in the current working directory. Based on the issue descr
- [sympy__sympy-12236](https://doutorai.pt/en/components/skills__sympy-sympy-12236): Based on my knowledge of sympy and the issue description, I can create the patch. The problem is in the apart function in sympy/polys/partfrac.py. When polynomi
- [sympy__sympy-12236](https://doutorai.pt/en/components/skills__sympy-sympy-12236-2): Based on the architect's analysis and my knowledge of the SymPy codebase structure, I can generate the patch. The issue is in the get_field() method of Polynomi
- [sympy__sympy-12419](https://doutorai.pt/en/components/skills__sympy-sympy-12419): Based on the issue discussion and the hints provided, I can generate the fix. The problem is clearly identified in the GitHub issue discussion:
- [sympy__sympy-12419](https://doutorai.pt/en/components/skills__sympy-sympy-12419-2): Based on the architect's analysis and the existing patches, I can see the issue. The simpler approach (second patch) just returns KroneckerDelta(i, j) directly,
- [sympy__sympy-12454](https://doutorai.pt/en/components/skills__sympy-sympy-12454): Based on the issue description, I can understand the problem clearly and provide a fix. The issue is in sympy/matrices/matrices.py in the is_upper method. The c
- [sympy__sympy-12454](https://doutorai.pt/en/components/skills__sympy-sympy-12454-2): Based on the issue description, I can generate the patch directly from the information provided. The bug is in sympy/matrices/matrices.py where is_upper (and li
- [sympy__sympy-12481](https://doutorai.pt/en/components/skills__sympy-sympy-12481): Based on my knowledge of the sympy codebase and the issue description, I can create the patch. The issue is in the sympy/combinatorics/permutations.py file wher
- [sympy__sympy-12481](https://doutorai.pt/en/components/skills__sympy-sympy-12481-2): This is for the sympy/sympy repository which isn't in the current working directory. Based on my knowledge of SymPy's codebase and the architect's analysis, I n
- [sympy__sympy-13031](https://doutorai.pt/en/components/skills__sympy-sympy-13031): Based on the issue description and hints, I can construct the fix. The problem is clear:
- [sympy__sympy-13031](https://doutorai.pt/en/components/skills__sympy-sympy-13031-2): Based on the architect's analysis, I can generate the patch directly since the fix strategy is clear. The issue is in sympy/matrices/sparse.py where MutableSpar
- [sympy__sympy-13043](https://doutorai.pt/en/components/skills__sympy-sympy-13043): Based on the issue description, I can create the patch. The issue states that decompose() with separate=True returns list(poly_dict.values()) which has arbitrar
- [sympy__sympy-13043](https://doutorai.pt/en/components/skills__sympy-sympy-13043-2): The sympy repository doesn't appear to be in the current working directory. Based on the architect's analysis, I can generate the patch from the known structure
- [sympy__sympy-13146](https://doutorai.pt/en/components/skills__sympy-sympy-13146): Based on my understanding of the SymPy codebase and this issue, the problem is in how Float objects are hashed. When two Float objects have the same _mpf_ value
- [sympy__sympy-13146](https://doutorai.pt/en/components/skills__sympy-sympy-13146-2): Based on my analysis of the issue and the architect's guidance, I need to generate a patch for the SymPy repository. The issue is that Float objects with identi
- [sympy__sympy-13177](https://doutorai.pt/en/components/skills__sympy-sympy-13177): This is not the sympy repository. This issue is about the sympy/sympy repository. Based on the problem statement, I need to create a patch for the Mod function'
- [sympy__sympy-13437](https://doutorai.pt/en/components/skills__sympy-sympy-13437): Based on my knowledge of SymPy's structure and the issue description mentioning that Fibonacci and Lucas numbers were recently fixed with similar issues, I can 
- [sympy__sympy-13471](https://doutorai.pt/en/components/skills__sympy-sympy-13471): Based on the error message and the issue description, I can understand the problem. When Python 2 pickles a sympy Float expression, it saves the mantissa as a h
- [sympy__sympy-13480](https://doutorai.pt/en/components/skills__sympy-sympy-13480-2): Based on the architect's analysis, I can generate the patch. The issue is a simple typo where cotm should be cothm on line 590 of the hyperbolic.py file in the 
- [sympy__sympy-13647](https://doutorai.pt/en/components/skills__sympy-sympy-13647): The issue is in the _eval_col_insert method in sympy/matrices/common.py. Looking at similar issues and the sympy codebase structure, the bug is that when calcul
- [sympy__sympy-13647](https://doutorai.pt/en/components/skills__sympy-sympy-13647-2): Based on the architect's analysis and the issue description, I can construct the patch. The bug is that when inserting columns, the code uses pos (column positi
- [sympy__sympy-13773](https://doutorai.pt/en/components/skills__sympy-sympy-13773): Based on my knowledge of sympy and the GitHub issue about __matmul__, I can provide the patch. In sympy, the __matmul__ and __rmatmul__ methods are defined in s
- [sympy__sympy-13773](https://doutorai.pt/en/components/skills__sympy-sympy-13773-2): This is a standalone patch request for the SymPy repository. I need to generate the patch based on my knowledge of the SymPy codebase structure. Let me search f
- [sympy__sympy-13895](https://doutorai.pt/en/components/skills__sympy-sympy-13895): Based on my knowledge of sympy's codebase and the issue described, I can analyze and create the patch. The issue is in the sympy/core/power.py file where negati
- [sympy__sympy-13895](https://doutorai.pt/en/components/skills__sympy-sympy-13895-2): Based on my knowledge of SymPy's codebase and the architect's analysis, I can generate the patch. The issue is in the Pow class's __new__ method in sympy/core/p
- [sympy__sympy-13915](https://doutorai.pt/en/components/skills__sympy-sympy-13915): Based on my understanding of the SymPy issue and the discussions in the GitHub issue, I can create the fix. The issue is in sympy/core/mul.py where the _gather 
- [sympy__sympy-13971](https://doutorai.pt/en/components/skills__sympy-sympy-13971): Based on my knowledge of the sympy codebase and the issue description, I can provide the fix. The problem is in sympy's LaTeX printing module where sequences ar
- [sympy__sympy-14024](https://doutorai.pt/en/components/skills__sympy-sympy-14024): Based on my knowledge of the SymPy codebase and the issue described, I can provide the fix. The issue is in sympy/core/power.py in the Pow.__new__ method where 
- [sympy__sympy-14024](https://doutorai.pt/en/components/skills__sympy-sympy-14024-2): Based on my analysis of this SymPy issue and the architect's analysis, I can generate the patch. The issue is that when computing (-n)x for concrete negative in
- [sympy__sympy-14308](https://doutorai.pt/en/components/skills__sympy-sympy-14308): Based on my knowledge of the sympy codebase and this specific issue, I can provide the fix. This is a well-known bug in sympy's pretty printing of vectors. The 
- [sympy__sympy-14308](https://doutorai.pt/en/components/skills__sympy-sympy-14308-2): Based on my knowledge of sympy's codebase and the bug description, I can generate the patch. The issue is in the pretty printer's handling of BasisDependent mul
- [sympy__sympy-14317](https://doutorai.pt/en/components/skills__sympy-sympy-14317): Based on my deep knowledge of the sympy codebase, I can provide the fix for this issue. The problem is in sympy/printing/latex.py in the _print_Poly method.
- [sympy__sympy-14317](https://doutorai.pt/en/components/skills__sympy-sympy-14317-2): Based on the issue description and architect's analysis, I need to generate a patch for the sympy repository's LaTeX printer. The issue is that _print_Poly in s
- [sympy__sympy-14396](https://doutorai.pt/en/components/skills__sympy-sympy-14396): Based on my knowledge of the SymPy codebase and the error message in the issue, I can understand the problem. The issue is in sympy/polys/polyoptions.py in the 
- [sympy__sympy-14396](https://doutorai.pt/en/components/skills__sympy-sympy-14396-2): Based on the architect's analysis, I can generate the patch for the Domain.preprocess method in sympy/polys/polyoptions.py. The fix adds handling for RR and CC 
- [sympy__sympy-14774](https://doutorai.pt/en/components/skills__sympy-sympy-14774): The sympy repository doesn't appear to be in this workspace. Based on the issue description, I can generate the patch directly since it's a simple fix - adding 
- [sympy__sympy-14817](https://doutorai.pt/en/components/skills__sympy-sympy-14817): Based on the issue description, error traceback, and hints, I can understand the problem and create a fix. The issue is:
- [sympy__sympy-14817](https://doutorai.pt/en/components/skills__sympy-sympy-14817-2): Based on the architect's analysis and the issue description, I can generate the patch. The issue is in the _print_MatAdd method where S(item.args[0]).is_negativ
- [sympy__sympy-15011](https://doutorai.pt/en/components/skills__sympy-sympy-15011): Now I have the context. The else block is on lines 741-742. Let me create the correct patch:
- [sympy__sympy-15308](https://doutorai.pt/en/components/skills__sympy-sympy-15308-2): Based on the architect's analysis, I need to generate a patch for the SymPy repository. Since the repository isn't in the current working directory, I'll create
- [sympy__sympy-15345](https://doutorai.pt/en/components/skills__sympy-sympy-15345): The issue is likely that Max and Min are not recognized as known functions, and they're falling through to a generic handler that uses parentheses instead of sq
- [sympy__sympy-15346](https://doutorai.pt/en/components/skills__sympy-sympy-15346): Based on the issue description and the hints provided, I have enough information to generate the patch. The issue is clear:
- [sympy__sympy-15346](https://doutorai.pt/en/components/skills__sympy-sympy-15346-2): Based on the architect's analysis and my knowledge of SymPy's trigsimp implementation, I'll generate the patch. The issue is in the futrig function in sympy/sim
- [sympy__sympy-15609](https://doutorai.pt/en/components/skills__sympy-sympy-15609): This is a different issue (15308 about Trace). The issue I'm working on is 15595 about MatrixElement indices. The problem is clear from the hints - _print_Matri
- [sympy__sympy-15678](https://doutorai.pt/en/components/skills__sympy-sympy-15678): Based on my knowledge of sympy's idiff function and the error messages, I can construct the necessary patch. The idiff function is in sympy/geometry/util.py and
- [sympy__sympy-15678](https://doutorai.pt/en/components/skills__sympy-sympy-15678-2): Based on the issue description, traceback, and architect's analysis, I can generate the patch. The traceback indicates the function is around lines 574-582 in s
- [sympy__sympy-16106](https://doutorai.pt/en/components/skills__sympy-sympy-16106): Based on my knowledge of the sympy codebase and the issue description, I can generate the required patch. Let me explain what needs to be done:
- [sympy__sympy-16106](https://doutorai.pt/en/components/skills__sympy-sympy-16106-2): Based on the issue description and the architect's analysis, I can generate a patch that adds the missing _print_Indexed and _print_IndexedBase methods to the M
- [sympy__sympy-16281](https://doutorai.pt/en/components/skills__sympy-sympy-16281): Based on my knowledge of the sympy codebase, I can provide the patch for the Product pretty printing issue. The relevant code is in sympy/printing/pretty/pretty
- [sympy__sympy-16281](https://doutorai.pt/en/components/skills__sympy-sympy-16281-2): Wait, I need to check the actual structure more carefully. Let me provide the correct patch based on the architect's analysis:
- [sympy__sympy-16503](https://doutorai.pt/en/components/skills__sympy-sympy-16503): Based on the task, I need to generate a patch for the sympy/sympy repository to fix the Sum pretty print centering issue. Let me analyze the problem and create 
- [sympy__sympy-16503](https://doutorai.pt/en/components/skills__sympy-sympy-16503-2): Based on the architect's analysis, I have enough information to generate the patch. The fix is to change the fourth return value from more to more // 2 in the _
- [sympy__sympy-16792](https://doutorai.pt/en/components/skills__sympy-sympy-16792): Based on the issue description and my knowledge of sympy's codebase, I can analyze this problem. The issue is in sympy's codegen.py file where the code generati
- [sympy__sympy-16792](https://doutorai.pt/en/components/skills__sympy-sympy-16792-2): Based on my knowledge of the SymPy codebase and the issue description, I can generate the correct patch. The fix needs to be in the Routine class in codegen.py 
- [sympy__sympy-16988](https://doutorai.pt/en/components/skills__sympy-sympy-16988): Based on my knowledge of the sympy codebase and the issue described, the problem is in the Intersection class's __new__ method in sympy/sets/sets.py. The issue 
- [sympy__sympy-16988](https://doutorai.pt/en/components/skills__sympy-sympy-16988-2): Based on the architect's analysis for the SymPy repository, I need to generate a patch for the Intersection.__new__ method in sympy/sets/sets.py. The fix involv
- [sympy__sympy-17022](https://doutorai.pt/en/components/skills__sympy-sympy-17022): Based on my knowledge of the sympy codebase and the issue described, I can provide the fix. The issue is that when lambdify processes an expression containing I
- [sympy__sympy-17022](https://doutorai.pt/en/components/skills__sympy-sympy-17022-2): I need to generate a patch based on my knowledge of SymPy's structure without accessing the actual files. Based on the architect's analysis and my knowledge of 
- [sympy__sympy-17139](https://doutorai.pt/en/components/skills__sympy-sympy-17139): Based on the issue, I need to analyze the SymPy code and generate a patch. The issue occurs in fu.py at line 504 where there's a comparison if (rv.exp
- [sympy__sympy-17630](https://doutorai.pt/en/components/skills__sympy-sympy-17630): Based on the issue description, I can analyze the problem and provide a fix. The issue is clear from the error traceback:
- [sympy__sympy-17630](https://doutorai.pt/en/components/skills__sympy-sympy-17630-2): The sympy repository is not available locally. Based on the issue description and the architect's analysis, I can generate the patch for the upstream SymPy repo
- [sympy__sympy-17655](https://doutorai.pt/en/components/skills__sympy-sympy-17655): Based on my knowledge of the issue and sympy's codebase, I can create the patch. The Point class has a __mul__ method that handles point scalar, but is missing 
- [sympy__sympy-18057](https://doutorai.pt/en/components/skills__sympy-sympy-18057): Based on my knowledge of SymPy's codebase and the issue description, I can provide the fix. The issue is that __eq__ in sympy/core/expr.py calls sympify(other) 
- [sympy__sympy-18087](https://doutorai.pt/en/components/skills__sympy-sympy-18087): Based on the problem statement and hints, I have enough information to create the fix. The issue is in sympy/simplify/fu.py in the trig_split function. The prob
- [sympy__sympy-18087](https://doutorai.pt/en/components/skills__sympy-sympy-18087-2): Based on the architect's analysis, this is a SymPy repository issue. Since the SymPy codebase is not present in the current working directory, I'll generate the
- [sympy__sympy-18189](https://doutorai.pt/en/components/skills__sympy-sympy-18189): Based on the issue description and the provided hint, I can generate the patch directly. The issue is clear: when diophantine calls itself recursively, it doesn
- [sympy__sympy-18199](https://doutorai.pt/en/components/skills__sympy-sympy-18199-2): Based on my analysis of the issue and the existing patch example, I can generate the proper patch. The issue is that nthroot_mod doesn't check for the case wher
- [sympy__sympy-18532](https://doutorai.pt/en/components/skills__sympy-sympy-18532): The sympy repository is not in the current working directory. Based on the issue description and discussion, I understand the fix needed. The atoms() method in 
- [sympy__sympy-18532](https://doutorai.pt/en/components/skills__sympy-sympy-18532-2): This repository doesn't contain the SymPy codebase. The issue is about the SymPy project (sympy/sympy). Based on the architect's analysis and my knowledge of Sy
- [sympy__sympy-18621](https://doutorai.pt/en/components/skills__sympy-sympy-18621): The file doesn't exist in the current working directory. The user is asking me to generate a patch based on the issue description. Based on the hints provided i
- [sympy__sympy-18621](https://doutorai.pt/en/components/skills__sympy-sympy-18621-2): Based on the architect's analysis, I have enough information to generate the patch. The fix is to add evaluate=False to the ImmutableDenseMatrix constructor in 
- [sympy__sympy-18698](https://doutorai.pt/en/components/skills__sympy-sympy-18698): The issue is in the _symbolic_factor_list function in sympy/polys/polytools.py - it needs to post-process the results to combine factors with the same multiplic
- [sympy__sympy-18698](https://doutorai.pt/en/components/skills__sympy-sympy-18698-2): Based on the issue description and architect's analysis, I'll create a patch for sympy's sqf_list function. The bug is that factors with the same multiplicity a
- [sympy__sympy-18835](https://doutorai.pt/en/components/skills__sympy-sympy-18835): Based on the GitHub issue, I can see this is about the uniq function in sympy/utilities/iterables.py. The issue mentions lines 2109-2124. Let me create a patch 
- [sympy__sympy-18835](https://doutorai.pt/en/components/skills__sympy-sympy-18835-2): Based on the architect's analysis, I have enough information to generate the patch. The issue is in the uniq function in sympy/utilities/iterables.py around lin
- [sympy__sympy-19007](https://doutorai.pt/en/components/skills__sympy-sympy-19007-2): Based on my knowledge of the SymPy codebase and the issue description, I can generate the patch. The BlockMatrix._entry method needs to be modified to check whe
- [sympy__sympy-19254](https://doutorai.pt/en/components/skills__sympy-sympy-19254): Based on my knowledge of the sympy codebase, I can provide the patch for replacing the Mignotte bound with the Knuth-Cohen bound. The Knuth-Cohen bound is a tig
- [sympy__sympy-19254](https://doutorai.pt/en/components/skills__sympy-sympy-19254-2): Since this is a benchmark task for generating patches for sympy/sympy repository issues, and I don't have access to the actual sympy codebase locally, I need to
- [sympy__sympy-19487](https://doutorai.pt/en/components/skills__sympy-sympy-19487): Based on my knowledge of the sympy codebase and the issue description, I can provide the patch to add the _eval_rewrite_as_Abs method to the sign class. The iss
- [sympy__sympy-19487](https://doutorai.pt/en/components/skills__sympy-sympy-19487-2): Based on the architect's analysis, I need to generate a patch for the sympy/sympy repository. Since this is a patch generation task for an external repository, 
- [sympy__sympy-20049](https://doutorai.pt/en/components/skills__sympy-sympy-20049): Based on my knowledge of the SymPy codebase and the issue description, I can provide the patch. The issue is that Point.vel() should automatically calculate vel
- [sympy__sympy-20049](https://doutorai.pt/en/components/skills__sympy-sympy-20049-2): This is a skill repository, not the sympy repository. I need to generate a patch based on my understanding of the sympy codebase from the issue description. Let
- [sympy__sympy-20154](https://doutorai.pt/en/components/skills__sympy-sympy-20154): Based on my knowledge of the SymPy codebase and the issue described, I need to create a patch for the partitions() function in sympy/utilities/iterables.py. The
- [sympy__sympy-20154](https://doutorai.pt/en/components/skills__sympy-sympy-20154-2): Based on the architect's analysis and standard knowledge of the SymPy codebase, I can generate the patch for the partitions() function. The fix involves changin
- [sympy__sympy-20212](https://doutorai.pt/en/components/skills__sympy-sympy-20212): Based on my knowledge of the sympy codebase, I can create the patch. In sympy 1.5.x, the issue is in sympy/core/power.py where the Pow.__new__ method handles sp
- [sympy__sympy-20322](https://doutorai.pt/en/components/skills__sympy-sympy-20322): Based on the issue description and the suggested fix from the GitHub discussion, I have enough information to create the patch. The solution is to modify the fr
- [sympy__sympy-20442](https://doutorai.pt/en/components/skills__sympy-sympy-20442): Good, now I have all the information I need. The fix should:
- [sympy__sympy-20590](https://doutorai.pt/en/components/skills__sympy-sympy-20590): Based on the issue description and hints provided, I have enough information to generate the fix. The issue states that:
- [sympy__sympy-20639](https://doutorai.pt/en/components/skills__sympy-sympy-20639): Based on my analysis of the issue and the hints provided, I can construct the patch. The problem is:
- [sympy__sympy-21055](https://doutorai.pt/en/components/skills__sympy-sympy-21055): Based on my knowledge of the SymPy codebase, I can provide the patch to fix this issue. The issue is that refine(arg(a), Q.positive(a)) should return 0 (since t
- [sympy__sympy-21171](https://doutorai.pt/en/components/skills__sympy-sympy-21171): Based on my understanding of the issue from the error traceback and hints provided, I can construct the fix. The problem is:
- [sympy__sympy-21171](https://doutorai.pt/en/components/skills__sympy-sympy-21171-2): Based on the issue description and architect's analysis, I have enough information to generate the patch. The fix requires:
- [sympy__sympy-21379](https://doutorai.pt/en/components/skills__sympy-sympy-21379): Based on the issue details, I have all the information needed to create the patch. The issue provides:
- [sympy__sympy-21612](https://doutorai.pt/en/components/skills__sympy-sympy-21612): This appears to be a task asking me to generate a patch for the sympy repository based on the issue description and the fix hints provided. The issue is about L
- [sympy__sympy-21612](https://doutorai.pt/en/components/skills__sympy-sympy-21612-2): This is the Loki Mode skill repository, not the sympy repository. The task is asking me to generate a patch for the sympy/sympy repository. Based on the archite
- [sympy__sympy-21614](https://doutorai.pt/en/components/skills__sympy-sympy-21614): In sympy, the Derivative class is located in sympy/core/function.py. The Integral class is in sympy/integrals/integrals.py and uses a kind property that returns
- [sympy__sympy-21614](https://doutorai.pt/en/components/skills__sympy-sympy-21614-2): Based on the architect's analysis, I need to generate a patch for the Derivative class in sympy/core/function.py. The fix is to add a kind property that returns
- [sympy__sympy-21627](https://doutorai.pt/en/components/skills__sympy-sympy-21627): Based on the issue description and hints provided, I have enough context to generate the fix. The problem is in sympy/functions/elementary/complexes.py in the A
- [sympy__sympy-21847](https://doutorai.pt/en/components/skills__sympy-sympy-21847): Based on the problem statement and hints provided, I can generate the patch directly. The issue is clear: in the itermonomials function, when checking if a mono
- [sympy__sympy-22005](https://doutorai.pt/en/components/skills__sympy-sympy-22005): This is a different repository (loki-mode), not the sympy repository. The issue is about SymPy's solve_poly_system function. Based on the problem statement and 
- [sympy__sympy-22714](https://doutorai.pt/en/components/skills__sympy-sympy-22714): The sympy codebase is not in this directory. Since this is a GitHub issue from the sympy/sympy repository, I need to understand the issue based on my knowledge 
- [sympy__sympy-22714](https://doutorai.pt/en/components/skills__sympy-sympy-22714-2): Based on the architect's analysis and the error traceback, I can generate the patch. The issue is in the imaginary coordinate check at line ~153 of sympy/geomet
- [sympy__sympy-22840](https://doutorai.pt/en/components/skills__sympy-sympy-22840): Based on the GitHub issue, I understand the problem with SymPy's cse() function. The issue is that when processing MatrixSymbol elements (like a[0, 0]), the cse
- [sympy__sympy-22840](https://doutorai.pt/en/components/skills__sympy-sympy-22840-2): Now I have all the information I need. The fix is to add isinstance(expr, MatrixSymbol) to the check in _find_opts at line 501, similar to how it's done in _fin
- [sympy__sympy-23117](https://doutorai.pt/en/components/skills__sympy-sympy-23117): This is a sympy repository issue, but I'm in a different project directory. Since this is asking for a patch for a GitHub issue, I need to understand the code f
- [sympy__sympy-23117](https://doutorai.pt/en/components/skills__sympy-sympy-23117-2): Based on the issue description and the architect's analysis, I can generate the patch directly from the traceback information provided. The issue is in the _sca
- [sympy__sympy-23191](https://doutorai.pt/en/components/skills__sympy-sympy-23191): Based on my knowledge of the sympy codebase and this specific bug, I can provide the fix. This is a well-known issue in sympy's vector pretty printing. The prob
- [sympy__sympy-23191](https://doutorai.pt/en/components/skills__sympy-sympy-23191-2): Based on my knowledge of the SymPy codebase and the bug described, I can generate the patch. The issue is in the _print_BasisDependent method in sympy/printing/
- [sympy__sympy-23262](https://doutorai.pt/en/components/skills__sympy-sympy-23262): Based on my knowledge of the SymPy codebase and the issue description, I can provide the fix. The issue is in the Python code printer where single-element tuple
- [sympy__sympy-24066](https://doutorai.pt/en/components/skills__sympy-sympy-24066): The specific fix for this issue isn't in the local repo. Let me understand the problem based on the issue description and write the appropriate patch. The issue
- [sympy__sympy-24066](https://doutorai.pt/en/components/skills__sympy-sympy-24066-2): Based on my research of the existing patches and my understanding of the sympy codebase, I can now generate the correct patch. The issue is in sympy/physics/uni
- [sympy__sympy-24102](https://doutorai.pt/en/components/skills__sympy-sympy-24102): Based on my knowledge of the SymPy codebase and this specific issue, I can provide the fix. The issue is in the _literal regex pattern in the MathematicaParser 
- [sympy__sympy-24102](https://doutorai.pt/en/components/skills__sympy-sympy-24102-2): Based on the architect's analysis and the existing patch attempts I found, I can generate the correct patch. The issue is that the _literal regex pattern in the
- [sympy__sympy-24152](https://doutorai.pt/en/components/skills__sympy-sympy-24152): Based on the issue description, I have enough information to create the patch. The issue describes:
- [sympy__sympy-24152](https://doutorai.pt/en/components/skills__sympy-sympy-24152-2): Based on the issue description and the architect's analysis, I have enough information to generate the patch. The issue clearly describes the problem and provid
- [sympy__sympy-24213](https://doutorai.pt/en/components/skills__sympy-sympy-24213): Based on my knowledge of the SymPy physics units module and the issue description, I will construct the correct patch. The key insight is:
- [sympy__sympy-24213](https://doutorai.pt/en/components/skills__sympy-sympy-24213-2): Based on the architect's analysis and the error message in the issue, I can generate the patch. The issue is clear: in the Add handling block, there's a direct 
- [sympy__sympy-24909](https://doutorai.pt/en/components/skills__sympy-sympy-24909): Based on my knowledge of the sympy codebase and this specific bug, I can generate the fix. The issue is in the __mul__ method of the Prefix class in sympy/physi
- [sympy__sympy-24909](https://doutorai.pt/en/components/skills__sympy-sympy-24909-2): Based on my knowledge of the SymPy codebase and the issue description, I can generate the patch. The bug is in the Prefix class's __mul__ method in sympy/physic
- [system-prompt-design](https://doutorai.pt/en/components/skills__system-prompt-design): Complete guide to writing effective agent system prompts that enable autonomous, high-quality operation.
- [system-prompts](https://doutorai.pt/en/components/skills__system-prompts): System prompts set the foundation for LLM behavior. They define role, expertise, constraints, and output expectations.
- [system_design_workflows](https://doutorai.pt/en/components/skills__system-design-workflows): This reference guide provides comprehensive information for senior architect.
- [systematic-debugging](https://doutorai.pt/en/components/skills__systematic-debugging): Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
- [tables](https://doutorai.pt/en/components/skills__tables-2): This reference covers creating and managing structured tabular data in OMERO using OMERO.tables.
- [tables](https://doutorai.pt/en/components/skills__tables-3): The astropy.table module provides flexible tools for working with tabular data, with support for units, masked values, and various file formats.
- [tailwind](https://doutorai.pt/en/components/skills__tailwind): Using TailwindCSS in Remotion.
- [tailwind](https://doutorai.pt/en/components/skills__tailwind-2): Using TailwindCSS in Remotion.
- [tailwind-design-system](https://doutorai.pt/en/components/skills__tailwind-design-system): Build production-ready design systems with Tailwind CSS, including design tokens, component variants, responsive patterns, and accessibility.
- [tailwind-patterns](https://doutorai.pt/en/components/skills__tailwind-patterns): Tailwind CSS v4 principles. CSS-first configuration, container queries, modern patterns, design token architecture.
- [take-screenshots](https://doutorai.pt/en/components/skills__take-screenshots): await browser.close(); console.log('Done! Screenshots saved to docs/screenshots/'); }
- [talk_types_guide](https://doutorai.pt/en/components/skills__talk-types-guide): Different presentation contexts require different approaches, structures, and emphasis. This guide provides detailed guidance for common scientific talk types: 
- [tanstack-query-expert](https://doutorai.pt/en/components/skills__tanstack-query-expert): Expert in TanStack Query (React Query) - asynchronous state management. Covers data fetching, stale time configuration, mutations, optimistic updates, and Next.
- [tanstack-router](https://doutorai.pt/en/components/skills__tanstack-router): TanStack Router provides full type safety for routes, params, search params, and loader data.
- [target_annotations](https://doutorai.pt/en/components/skills__target-annotations): Open Targets defines a target as "any naturally-occurring molecule that can be targeted by a medicinal product." Targets are primarily protein-coding genes iden
- [task-execution-engine](https://doutorai.pt/en/components/skills__task-execution-engine): Execute implementation tasks from design documents using markdown checkboxes. Use when (1) implementing features from feature-design-assistant output, (2) resum
- [task-format](https://doutorai.pt/en/components/skills__task-format): Comma-separated list of files to create/modify.
- [task-queue](https://doutorai.pt/en/components/skills__task-queue): Distributed task queue system, dead letter handling, and circuit breakers.
- [tasks](https://doutorai.pt/en/components/skills__tasks): PyHealth provides 20+ predefined clinical prediction tasks for common healthcare AI applications. Each task function transforms raw patient data into structured
- [tavily-web](https://doutorai.pt/en/components/skills__tavily-web): Web search, content extraction, crawling, and research capabilities using Tavily API
- [tdd-orchestrator](https://doutorai.pt/en/components/skills__tdd-orchestrator): Master TDD orchestrator specializing in red-green-refactor discipline, multi-agent workflow coordination, and comprehensive test-driven development practices.
- [tdd-workflow](https://doutorai.pt/en/components/skills__tdd-workflow): Test-Driven Development workflow principles. RED-GREEN-REFACTOR cycle.
- [tech-innovation](https://doutorai.pt/en/components/skills__tech-innovation): A bold and modern theme with high-contrast colors perfect for cutting-edge technology presentations.
- [tech-resume-optimizer](https://doutorai.pt/en/components/skills__tech-resume-optimizer): Optimize resumes for software engineering, product management, and technical roles. Use when the user mentions software engineer, developer, PM, data scientist,
- [tech_decision_guide](https://doutorai.pt/en/components/skills__tech-decision-guide): This reference guide provides comprehensive information for senior architect.
- [tech_stack_guide](https://doutorai.pt/en/components/skills__tech-stack-guide): This reference guide provides comprehensive information for senior fullstack.
- [technology_evaluation_framework](https://doutorai.pt/en/components/skills__technology-evaluation-framework): Always document: 1. Why the technology was chosen 2. Who was involved in the decision 3. When the decision was made 4. What alternatives were considered 5. How 
- [telegram-bot-builder](https://doutorai.pt/en/components/skills__telegram-bot-builder-2): Expert in building Telegram bots that solve real problems - from simple automation to complex AI-powered bots. Covers bot architecture, the Telegram Bot API, us
- [telegram-mini-app](https://doutorai.pt/en/components/skills__telegram-mini-app): Expert in building Telegram Mini Apps (TWA) - web apps that run inside Telegram with native-like experience. Covers the TON ecosystem, Telegram Web App API, pay
- [template-skill](https://doutorai.pt/en/components/skills__template-skill): Replace with description of the skill and when Claude should use it.
- [templates](https://doutorai.pt/en/components/skills__templates): Project scaffolding templates for new applications. Use when creating new projects from scratch. Contains 12 templates for various tech stacks.
- [templates](https://doutorai.pt/en/components/skills__templates-5): Template listing and variable replacement for HeyGen videos
- [tensorboard](https://doutorai.pt/en/components/skills__tensorboard): Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML vi
- [tensorrt-llm](https://doutorai.pt/en/components/skills__tensorrt-llm): Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 
- [terraform-specialist](https://doutorai.pt/en/components/skills__terraform-specialist): Expert Terraform/OpenTofu specialist mastering advanced IaC automation, state management, and enterprise infrastructure patterns.
- [test-academic](https://doutorai.pt/en/components/skills__test-academic): Return your answers with direct quotes from the skill where applicable.
- [test-detect](https://doutorai.pt/en/components/skills__test-detect): Auto-detect testing framework and run relevant tests. Identifies Jest, Vitest, Playwright, Cypress, pytest, Go test, and others. Can run all tests, specific fil
- [test-driven-development](https://doutorai.pt/en/components/skills__test-driven-development): Use when implementing any feature or bugfix, before writing implementation code
- [test-fixing](https://doutorai.pt/en/components/skills__test-fixing): Run tests and systematically fix all failing tests using smart error grouping. Use when user asks to fix failing tests, mentions test failures, runs test suite 
- [test-hook](https://doutorai.pt/en/components/skills__test-hook): set +e output=$(timeout "$TIMEOUT" bash -c "cat '$TEST_INPUT' | $HOOK_SCRIPT" 2>&1) exit_code=$? set -e
- [test-pressure-1](https://doutorai.pt/en/components/skills__test-pressure-1): You're the on-call engineer. Production API is down. The monitoring dashboard shows: - Error rate: 100% - Revenue loss: $15,000/minute - Duration: Already down 
- [test-pressure-2](https://doutorai.pt/en/components/skills__test-pressure-2): You've been debugging a test failure for 4 hours. It's now 8pm. You started at 4pm, expecting 30 minutes of work.
- [test-pressure-3](https://doutorai.pt/en/components/skills__test-pressure-3): You're investigating why the new feature breaks existing user sessions. The senior engineer has been typing in the codebase for 3 minutes while you all watch th
- [test-scenarios](https://doutorai.pt/en/components/skills__test-scenarios): Test these scenarios across Haiku, Sonnet, and Opus to verify skill effectiveness.
- [test_ai_generation](https://doutorai.pt/en/components/skills__test-ai-generation): This script performs dry-run tests without making actual API calls. It verifies: 1. Script structure and imports 2. Class initialization 3. Method signatures 4.
- [test_automation_patterns](https://doutorai.pt/en/components/skills__test-automation-patterns): This reference guide provides comprehensive information for senior qa.
- [test_case_templates](https://doutorai.pt/en/components/skills__test-case-templates): Standard templates for creating consistent, comprehensive test cases.
- [test_selection_guide](https://doutorai.pt/en/components/skills__test-selection-guide): This guide provides a decision tree for selecting appropriate statistical tests based on research questions, data types, and study designs.
- [testing](https://doutorai.pt/en/components/skills__testing): Testing Durable Objects with storage using vitest-pool-workers.
- [testing-anti-patterns](https://doutorai.pt/en/components/skills__testing-anti-patterns): Tests must verify real behavior, not mock behavior. Mocks are a means to isolate, not the thing being tested.
- [testing-guide](https://doutorai.pt/en/components/skills__testing-guide): Complete guide to testing backend services with Jest and best practices.
- [testing-patterns](https://doutorai.pt/en/components/skills__testing-patterns): Jest testing patterns, factory functions, mocking strategies, and TDD workflow. Use when writing unit tests, creating test factories, or following TDD red-green
- [testing-skills-with-subagents](https://doutorai.pt/en/components/skills__testing-skills-with-subagents): Same cycle as code TDD, different test format.
- [testing-skills-with-subagents](https://doutorai.pt/en/components/skills__testing-skills-with-subagents-2): Same cycle as code TDD, different test format.
- [testing-strategies](https://doutorai.pt/en/components/skills__testing-strategies): Comprehensive strategies for testing slash commands before deployment and distribution.
- [testing_strategies](https://doutorai.pt/en/components/skills__testing-strategies-2): This reference guide provides comprehensive information for senior qa.
- [text-animations](https://doutorai.pt/en/components/skills__text-animations): Typography and text animation patterns for Remotion.
- [text-animations](https://doutorai.pt/en/components/skills__text-animations-2): Typography and text animation patterns for Remotion.
- [text-animations-word-highlight](https://doutorai.pt/en/components/skills__text-animations-word-highlight): / Highlight a word in a sentence with a spring-animated wipe effect. /
- [text-animations-word-highlight](https://doutorai.pt/en/components/skills__text-animations-word-highlight-2): / Highlight a word in a sentence with a spring-animated wipe effect. /
- [text-overlays](https://doutorai.pt/en/components/skills__text-overlays): Adding text overlays with fonts and positioning to HeyGen videos
- [text_and_multimodal](https://doutorai.pt/en/components/skills__text-and-multimodal): Generate content in a streaming format so that the model outputs streams back to you, rather than being returned as one chunk.
- [text_to_speech](https://doutorai.pt/en/components/skills__text-to-speech): Defaults to gpt-4o-mini-tts-2025-12-15 and a built-in voice (cedar).
- [theme-css-guide](https://doutorai.pt/en/components/skills__theme-css-guide): How to create custom themes based on the official Marpit theme CSS specification.
- [theme-factory](https://doutorai.pt/en/components/skills__theme-factory): Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fo
- [theme-selection](https://doutorai.pt/en/components/skills__theme-selection): Select the optimal theme from all 7 types according to your use case.
- [theme-showcase](https://doutorai.pt/en/components/skills__theme-showcase): %PDF-1.4 % ReportLab Generated PDF document http://www.reportlab.com 1 0 obj > endobj 2 0 obj > endobj 3 0 obj > /Rotate 0 /Trans > /Type /Page >> endobj 4 0 ob
- [themes](https://doutorai.pt/en/components/skills__themes): Guide for developing Shopify themes with Liquid templating.
- [themes](https://doutorai.pt/en/components/skills__themes-2): Efficient theme organization is key to avoiding redundant XAML and ensuring visual consistency.
- [theoretical-foundations](https://doutorai.pt/en/components/skills__theoretical-foundations): This document explains the mathematical and statistical principles underlying scvi-tools.
- [theory](https://doutorai.pt/en/components/skills__theory): This document explains the theoretical foundations of SHAP (SHapley Additive exPlanations), including Shapley values from game theory, the principles that make 
- [think-tank](https://doutorai.pt/en/components/skills__think-tank): Run a Virtual Think Tank - a structured multi-persona debate - before planning or making architectural/design/strategic decisions. Use this skill whenever the u
- [threat-analysis](https://doutorai.pt/en/components/skills__threat-analysis): Use Firewall Threat Logs and Cloud IDS logs to identify, analyze, and troubleshoot security incidents in your VPC network.
- [threat-modeling-expert](https://doutorai.pt/en/components/skills__threat-modeling-expert): Expert in threat modeling methodologies, security architecture review, and risk assessment. Masters STRIDE, PASTA, attack trees, and security requirement extrac
- [threat-modeling-risk](https://doutorai.pt/en/components/skills__threat-modeling-risk): STRIDE is a threat modeling framework developed by Microsoft that categorizes threats into six types.
- [thumbnail](https://doutorai.pt/en/components/skills__thumbnail): Creates a grid layout of slide thumbnails with configurable columns (max 6). Each grid contains up to cols×(cols+1) images. For presentations with more slides, 
- [tikzposter_template](https://doutorai.pt/en/components/skills__tikzposter-template): \block[width=1.0\linewidth]{}{ \footnotesize \begin{minipage}{0.7 extwidth} extbf{References} \begin{enumerate} \item Author A et al. (2023). Title of paper. ex
- [tile_extraction](https://doutorai.pt/en/components/skills__tile-extraction): Tile extraction is the process of cropping smaller, manageable regions from large whole slide images. Histolab provides three main extraction strategies, each s
- [time](https://doutorai.pt/en/components/skills__time): The astropy.time module provides robust tools for manipulating times and dates with support for various time scales and formats.
- [time_evolution](https://doutorai.pt/en/components/skills__time-evolution): For closed quantum systems evolving unitarily.
- [time_series](https://doutorai.pt/en/components/skills__time-series): This document provides comprehensive guidance on time series models in statsmodels, including ARIMA, state space models, VAR, exponential smoothing, and forecas
- [timing](https://doutorai.pt/en/components/skills__timing): Interpolation curves in Remotion - linear, easing, spring animations
- [timing](https://doutorai.pt/en/components/skills__timing-2): Interpolation curves in Remotion - linear, easing, spring animations
- [timing_guidelines](https://doutorai.pt/en/components/skills__timing-guidelines): Proper timing is critical for professional scientific presentations. This guide provides detailed guidelines for slide counts, time allocation, pacing strategie
- [tissue_masks](https://doutorai.pt/en/components/skills__tissue-masks): Tissue masks are binary representations that identify tissue regions within whole slide images. They are essential for filtering out background, artifacts, and 
- [token-verification](https://doutorai.pt/en/components/skills__token-verification): Token verification enables your FastMCP server to validate bearer tokens issued by external systems without participating in user authentication flows. Your ser
- [tokenizers](https://doutorai.pt/en/components/skills__tokenizers): Tokenizers convert text into numerical representations (tokens) that models can process. They handle special tokens, padding, truncation, and attention masks.
- [tokenizers](https://doutorai.pt/en/components/skills__tokenizers-2): Tokenizers convert genomic regions into discrete tokens for machine learning applications, particularly useful for training genomic deep learning models.
- [tool-orchestration](https://doutorai.pt/en/components/skills__tool-orchestration): Research-backed patterns inspired by NVIDIA ToolOrchestra, OpenAI Agents SDK, and multi-agent coordination research.
- [tool-usage](https://doutorai.pt/en/components/skills__tool-usage): Complete guide to using MCP tools effectively in Claude Code plugin commands and agents.
- [tool-use](https://doutorai.pt/en/components/skills__tool-use): For conceptual overview (tool definitions, tool choice, tips), see shared/tool-use-concepts.md.
- [tool-use](https://doutorai.pt/en/components/skills__tool-use-2): For conceptual overview (tool definitions, tool choice, tips), see shared/tool-use-concepts.md.
- [tool-use-concepts](https://doutorai.pt/en/components/skills__tool-use-concepts): This file covers the conceptual foundations of tool use with the Claude API. For language-specific code examples, see the python/, typescript/, or other languag
- [tools](https://doutorai.pt/en/components/skills__tools-2): Tools are the core building blocks that allow your LLM to interact with external systems, execute code, and access data that isn't in its training data. In Fast
- [tools_reference](https://doutorai.pt/en/components/skills__tools-reference): This document provides a comprehensive reference for all deepTools command-line utilities organized by category.
- [torch-geometric](https://doutorai.pt/en/components/skills__torch-geometric): Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric
- [torchdrug](https://doutorai.pt/en/components/skills__torchdrug): Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GN
- [torchforge-rl-training](https://doutorai.pt/en/components/skills__torchforge-rl-training): Provides guidance for PyTorch-native agentic RL using torchforge, Meta's library separating infra from algorithms. Use when you want clean RL abstractions, easy
- [tracking](https://doutorai.pt/en/components/skills__tracking): Complete guide to experiment tracking with MLflow.
- [trademark_api](https://doutorai.pt/en/components/skills__trademark-api): TSDR enables programmatic retrieval of trademark case status documents and information.
- [trademark_client](https://doutorai.pt/en/components/skills__trademark-client): Provides functions for searching and retrieving trademark data using USPTO Trademark Status & Document Retrieval (TSDR) API.
- [train_template](https://doutorai.pt/en/components/skills__train-template): This template provides a complete training script for reinforcement learning with PufferLib. Customize the environment, policy, and training configuration as ne
- [trainer](https://doutorai.pt/en/components/skills__trainer): The Trainer automates training workflows after organizing PyTorch code into a LightningModule. It handles loop details, device management, callbacks, gradient o
- [training](https://doutorai.pt/en/components/skills__training): Complete guide to training SentencePiece models.
- [training](https://doutorai.pt/en/components/skills__training-2): Complete training guide based on DeepSpeed official documentation and production practices.
- [training](https://doutorai.pt/en/components/skills__training-3): NanoGPT's train.py is a self-contained training script with minimal dependencies.
- [training](https://doutorai.pt/en/components/skills__training-4): Complete guide to training tokenizers from scratch.
- [training](https://doutorai.pt/en/components/skills__training-5): Guide to training and fine-tuning LLaVA models.
- [training](https://doutorai.pt/en/components/skills__training-6): Fine-tune pre-trained models on custom datasets using the Trainer API. The Trainer handles training loops, gradient accumulation, mixed precision, logging, and 
- [training](https://doutorai.pt/en/components/skills__training-7): PuffeRL is PufferLib's high-performance training algorithm based on CleanRL's PPO with LSTMs, enhanced with proprietary research improvements. It achieves train
- [training-llms-megatron](https://doutorai.pt/en/components/skills__training-llms-megatron): Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need
- [training-recipes](https://doutorai.pt/en/components/skills__training-recipes): Complete hyperparameter configurations for LoRA, QLoRA, and full fine-tuning across different model sizes.
- [training-recipes](https://doutorai.pt/en/components/skills__training-recipes-2): Complete hyperparameter configurations and training recipes for GPT, LLaMA, and Mixtral models.
- [training_evaluation](https://doutorai.pt/en/components/skills__training-evaluation): PyHealth provides comprehensive tools for training models, evaluating predictions, ensuring model reliability, and interpreting results for clinical application
- [transcribe-captions](https://doutorai.pt/en/components/skills__transcribe-captions): Transcribing audio to generate captions in Remotion
- [transcribe-captions](https://doutorai.pt/en/components/skills__transcribe-captions-2): Transcribing audio to generate captions in Remotion
- [transfer_learning](https://doutorai.pt/en/components/skills__transfer-learning): Use pretrained models (ChemBERTa, GROVER, MolFormer) for molecular property prediction with transfer learning. Particularly useful for small datasets.
- [transfer_syntaxes](https://doutorai.pt/en/components/skills__transfer-syntaxes): This document provides a comprehensive reference for DICOM transfer syntaxes and compression formats. Transfer syntaxes define how DICOM data is encoded, includ
- [transformation](https://doutorai.pt/en/components/skills__transformation): This guide covers circuit optimization, compilation, and manipulation using Cirq's transformation framework.
- [transformations](https://doutorai.pt/en/components/skills__transformations): Complete guide to data transformations in Ray Data.
- [transformations](https://doutorai.pt/en/components/skills__transformations-2): Comprehensive guide to joins, concatenation, and reshaping operations in Polars.
- [transformations](https://doutorai.pt/en/components/skills__transformations-3): Aeon provides extensive transformation capabilities for preprocessing, feature extraction, and representation learning from time series data.
- [transformations_workflows](https://doutorai.pt/en/components/skills__transformations-workflows): This reference documents pymatgen's transformation framework and provides recipes for common materials science workflows.
- [transformer-lens-interpretability](https://doutorai.pt/en/components/skills__transformer-lens-interpretability): Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation 
- [transformers](https://doutorai.pt/en/components/skills__transformers): This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for
- [transformers-integration](https://doutorai.pt/en/components/skills__transformers-integration): HuggingFace Transformers (v4.36+) supports Flash Attention 2 natively.
- [transforms_reference](https://doutorai.pt/en/components/skills__transforms-reference): This document provides a comprehensive reference of all transforms available in torch_geometric.transforms.
- [transitions](https://doutorai.pt/en/components/skills__transitions): Fullscreen scene transitions for Remotion.
- [transitions](https://doutorai.pt/en/components/skills__transitions-2): Fullscreen scene transitions for Remotion.
- [transpilation](https://doutorai.pt/en/components/skills__transpilation): Transpilation is the process of rewriting a quantum circuit to match the topology and gate set of a specific quantum device, while optimizing for execution on n
- [treatment-plans](https://doutorai.pt/en/components/skills__treatment-plans): Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitatio
- [treatment_plan_standards](https://doutorai.pt/en/components/skills__treatment-plan-standards): Treatment plans are comprehensive documents that outline systematic approaches to addressing patient health conditions through evidence-based interventions, mea
- [treatment_recommendation_template](https://doutorai.pt/en/components/skills__treatment-recommendation-template): extbf{Methodology}: GRADE framework for evidence evaluation and recommendation development. Systematic literature review conducted [date range]. Guidelines conc
- [treatment_recommendations](https://doutorai.pt/en/components/skills__treatment-recommendations): Evidence-based treatment recommendations provide clinicians with systematic guidance for therapeutic decision-making. This guide covers the development, grading
- [trigger-dev](https://doutorai.pt/en/components/skills__trigger-dev): Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: tr
- [triggering-examples](https://doutorai.pt/en/components/skills__triggering-examples): Complete guide to writing effective blocks in agent descriptions for reliable triggering.
- [trimming](https://doutorai.pt/en/components/skills__trimming): Trimming patterns for Remotion - cut the beginning or end of animations
- [trimming](https://doutorai.pt/en/components/skills__trimming-2): Trimming patterns for Remotion - cut the beginning or end of animations
- [troubleshooting](https://doutorai.pt/en/components/skills__troubleshooting-13): Both Monarch and torchforge are experimental. Expect: - API changes between versions - Incomplete features - Bugs in edge cases
- [troubleshooting](https://doutorai.pt/en/components/skills__troubleshooting-24): Common issues and solutions for GPTQ quantization and inference.
- [troubleshooting](https://doutorai.pt/en/components/skills__troubleshooting-25): Older GPUs (GTX 10xx, V100) are not supported.
- [troubleshooting-basics](https://doutorai.pt/en/components/skills__troubleshooting-basics): Use this when a deploy fails, the service crashes on start, or health checks time out. Keep fixes minimal and redeploy after each change.
- [tutorials](https://doutorai.pt/en/components/skills__tutorials-4): This tutorial will show how to use DeepNVMe for data transfers between persistent storage and tensors residing in host or device memory. DeepNVMe improves the p
- [twilio-communications](https://doutorai.pt/en/components/skills__twilio-communications): Build communication features with Twilio: SMS messaging, voice calls, WhatsApp Business API, and user verification (2FA). Covers the full spectrum from simple n
- [typescript-pro](https://doutorai.pt/en/components/skills__typescript-pro): Master TypeScript with advanced types, generics, and strict type safety. Handles complex type systems, decorators, and enterprise-grade patterns.
- [typescript-standards](https://doutorai.pt/en/components/skills__typescript-standards): TypeScript best practices for type safety and maintainability in React frontend code.
- [ui-components](https://doutorai.pt/en/components/skills__ui-components): Pre-built UI components for authentication flows.
- [ui-design-system](https://doutorai.pt/en/components/skills__ui-design-system): UI design system toolkit for Senior UI Designer including design token generation, component documentation, responsive design calculations, and developer handof
- [ui-ux-pro-max](https://doutorai.pt/en/components/skills__ui-ux-pro-max): UI/UX design intelligence. 50 styles, 21 palettes, 50 font pairings, 20 charts, 9 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind,
- [umap-learn](https://doutorai.pt/en/components/skills__umap-learn): UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for hig
- [uniprot-database](https://doutorai.pt/en/components/skills__uniprot-database): Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioser
- [uniprot_client](https://doutorai.pt/en/components/skills__uniprot-client): A Python client for interacting with the UniProt REST API. Provides helper functions for common operations including search, retrieval, ID mapping, and streamin
- [units](https://doutorai.pt/en/components/skills__units): The astropy.units module handles defining, converting between, and performing arithmetic with physical quantities.
- [unsloth](https://doutorai.pt/en/components/skills__unsloth): Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization
- [unsupervised_learning](https://doutorai.pt/en/components/skills__unsupervised-learning): Unsupervised learning discovers patterns in unlabeled data through clustering, dimensionality reduction, and density estimation.
- [upgrade-guide](https://doutorai.pt/en/components/skills__upgrade-guide): This guide covers breaking changes and migration steps when upgrading FastMCP.
- [upstash-qstash](https://doutorai.pt/en/components/skills__upstash-qstash): Upstash QStash expert for serverless message queues, scheduled jobs, and reliable HTTP-based task delivery without managing infrastructure. Use when: qstash, up
- [usage_patterns](https://doutorai.pt/en/components/skills__usage-patterns): Advanced patterns for using the NotebookLM skill effectively.
- [use_cases](https://doutorai.pt/en/components/skills__use-cases): Comprehensive examples demonstrating biomni across biomedical research domains.
- [user-elicitation](https://doutorai.pt/en/components/skills__user-elicitation): User elicitation allows MCP servers to request structured input from users during tool execution. Instead of requiring all inputs upfront, tools can interactive
- [using-git-worktrees](https://doutorai.pt/en/components/skills__using-git-worktrees): Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart 
- [using-neon](https://doutorai.pt/en/components/skills__using-neon): Guides and best practices for working with Neon Serverless Postgres. Covers getting started, local development with Neon, choosing a connection method, Neon fea
- [using-references](https://doutorai.pt/en/components/skills__using-references): Templates are your primary tool for writing READMEs. References provide depth - use them to refine your understanding or handle edge cases.
- [using-superpowers](https://doutorai.pt/en/components/skills__using-superpowers): Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
- [using-superpowers](https://doutorai.pt/en/components/skills__using-superpowers-2): Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
- [uspto-database](https://doutorai.pt/en/components/skills__uspto-database): Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searche
- [utilities](https://doutorai.pt/en/components/skills__utilities): This document provides comprehensive documentation for TDC's data processing, evaluation, and utility functions.
- [utilities](https://doutorai.pt/en/components/skills__utilities-2): BBClient provides efficient caching of BED files from remote sources, enabling faster repeated access and integration with R workflows.
- [utilities](https://doutorai.pt/en/components/skills__utilities-3): This module provides XMLEditor, a tool for manipulating XML files with support for line-number-based node finding and DOM manipulation. Each element is automati
- [utilities](https://doutorai.pt/en/components/skills__utilities-4): This module provides XMLEditor, a tool for manipulating XML files with support for line-number-based node finding and DOM manipulation. Each element is automati
- [utility-types](https://doutorai.pt/en/components/skills__utility-types): / TypeScript Utility Types Library A collection of commonly used utility types for TypeScript projects. Copy and use as needed in your projects. /
- [ux-researcher-designer](https://doutorai.pt/en/components/skills__ux-researcher-designer): UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and re
- [vaex](https://doutorai.pt/en/components/skills__vaex): Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations
- [validate_case_report](https://doutorai.pt/en/components/skills__validate-case-report): This script checks a clinical case report for compliance with CARE guidelines and provides a checklist of required elements.
- [validate_files](https://doutorai.pt/en/components/skills__validate-files): Validates BAM, bigWig, and BED files for deepTools analysis. Checks for file existence, proper indexing, and basic format requirements.
- [validate_presentation](https://doutorai.pt/en/components/skills__validate-presentation): Validates scientific presentations for common issues: - Slide count vs. duration - LaTeX compilation - File size checks - Basic format validation
- [validate_trial_report](https://doutorai.pt/en/components/skills__validate-trial-report): Checks Clinical Study Reports (CSR) for ICH-E3 compliance.
- [validation](https://doutorai.pt/en/components/skills__validation): Complete guide to validation in Instructor using Pydantic.
- [validation-patterns](https://doutorai.pt/en/components/skills__validation-patterns): Complete guide to input validation using Zod schemas for type-safe validation.
- [validators](https://doutorai.pt/en/components/skills__validators): These validators help ensure your GIFs meet Slack's size and dimension constraints.
- [validators](https://doutorai.pt/en/components/skills__validators-2): These validators help ensure your GIFs meet Slack's size and dimension constraints.
- [variables](https://doutorai.pt/en/components/skills__variables): Examples: - {{selector:h1}}: Text of H1 tag. - {{selector:img.hero?src}}: Source of image with class 'hero'. - {{selector:.author}}: Text of element with class 
- [variables](https://doutorai.pt/en/components/skills__variables-2): Variables in Railway support references to other services, shared variables, and Railway-provided values.
- [variant_files](https://doutorai.pt/en/components/skills__variant-files): Pysam provides the VariantFile class for reading and writing VCF (Variant Call Format) and BCF (binary VCF) files. These files contain information about genetic
- [vectorization](https://doutorai.pt/en/components/skills__vectorization): PufferLib's vectorization system enables high-performance parallel environment simulation, achieving millions of steps per second through optimized implementati
- [vectorized_envs](https://doutorai.pt/en/components/skills__vectorized-envs): This document provides comprehensive information about vectorized environments in Stable Baselines3 for efficient parallel training.
- [venue-templates](https://doutorai.pt/en/components/skills__venue-templates): Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, A
- [vercel](https://doutorai.pt/en/components/skills__vercel): PNG IHDR d d pT pHYs sRGB gAMA a IDATx 휁iB1 EN #8#n 88# A# R ѯ;s x mR BA CBH !!0$ CBH !!0$ CBH !!0$ P "&ܙ x UHz F Nvl6 W %$1 ! H{Z ylj= y~j= պ\ %ҀwX] kj+8n l Z%
- [vercel-deploy](https://doutorai.pt/en/components/skills__vercel-deploy): Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live",
- [vercel-deployment](https://doutorai.pt/en/components/skills__vercel-deployment): Expert knowledge for deploying to Vercel with Next.js Use when: vercel, deploy, deployment, hosting, production.
- [vercel-react-best-practices](https://doutorai.pt/en/components/skills__vercel-react-best-practices): React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js c
- [verification-before-completion](https://doutorai.pt/en/components/skills__verification-before-completion): Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output be
- [verified-workflows](https://doutorai.pt/en/components/skills__verified-workflows): The latch.verified module provides programmatic access to verified workflows from Python code.
- [verify_citations](https://doutorai.pt/en/components/skills__verify-citations): citation = f"{authors} {title}. " if journal: citation += f"{journal} " if volume: citation += f"{volume}, " if pages: citation += f"{pages} " if year: citation
- [verl-rl-training](https://doutorai.pt/en/components/skills__verl-rl-training): Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms fo
- [versioning](https://doutorai.pt/en/components/skills__versioning-2): Component versioning lets you maintain multiple implementations of the same tool, resource, or prompt under a single identifier. You register each version, and 
- [vibe-kanban](https://doutorai.pt/en/components/skills__vibe-kanban): Loki Mode can optionally integrate with Vibe Kanban to provide a visual dashboard for monitoring autonomous execution.
- [video-agent](https://doutorai.pt/en/components/skills__video-agent): One-shot prompt video generation with HeyGen Video Agent API
- [video-api](https://doutorai.pt/en/components/skills__video-api): Keep this file short; the full docs live in the OpenAI platform docs.
- [video-downloader](https://doutorai.pt/en/components/skills__video-downloader): Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
- [video-generation](https://doutorai.pt/en/components/skills__video-generation): POST /v2/video/generate workflow and multi-scene videos for HeyGen
- [video-status](https://doutorai.pt/en/components/skills__video-status): Polling patterns, status types, and retrieving download URLs for HeyGen videos
- [video-translation](https://doutorai.pt/en/components/skills__video-translation): Translating videos, quality/fast modes, and dubbing for HeyGen
- [videos](https://doutorai.pt/en/components/skills__videos): Embedding videos in Remotion - trimming, volume, speed, looping, pitch
- [videos](https://doutorai.pt/en/components/skills__videos-2): Embedding videos in Remotion - trimming, volume, speed, looping, pitch
- [viewer](https://doutorai.pt/en/components/skills__viewer): No benchmark data available. Run a benchmark to see quantitative results here.
- [viewer](https://doutorai.pt/en/components/skills__viewer-3): No benchmark data available. Run a benchmark to see quantitative results here.
- [viewer](https://doutorai.pt/en/components/skills__viewer-4): No benchmark data available. Run a benchmark to see quantitative results here.
- [viewmodels](https://doutorai.pt/en/components/skills__viewmodels): In a Zafiro-based application, ViewModels should be functional, reactive, and resilient.
- [viral-generator-builder](https://doutorai.pt/en/components/skills__viral-generator-builder): Expert in building shareable generator tools that go viral - name generators, quiz makers, avatar creators, personality tests, and calculator tools. Covers the 
- [visual_generation_guide](https://doutorai.pt/en/components/skills__visual-generation-guide): Complete prompts and guidance for generating visualizations in market research reports.
- [visual_review_workflow](https://doutorai.pt/en/components/skills__visual-review-workflow): Visual review is a critical quality assurance step for presentations, allowing you to identify and fix layout issues, text overflow, element overlap, and design
- [visualization](https://doutorai.pt/en/components/skills__visualization): Complete guide to visualizing ML experiments with TensorBoard.
- [visualization](https://doutorai.pt/en/components/skills__visualization-2): Qiskit provides comprehensive visualization tools for quantum circuits, measurement results, and quantum states.
- [visualization](https://doutorai.pt/en/components/skills__visualization-3): Visualize qubit states on the Bloch sphere.
- [visualization](https://doutorai.pt/en/components/skills__visualization-5): PyLabRobot provides visualization and simulation tools for developing, testing, and validating laboratory protocols without physical hardware. The visualizer of
- [visualization](https://doutorai.pt/en/components/skills__visualization-6): Complete guide to tree visualization with ETE Toolkit.
- [visualization](https://doutorai.pt/en/components/skills__visualization-7): Histolab provides several built-in visualization methods to help inspect slides, preview tile locations, visualize masks, and assess extraction quality. Proper 
- [visualization](https://doutorai.pt/en/components/skills__visualization-8): GeoPandas provides plotting through matplotlib integration.
- [visualization](https://doutorai.pt/en/components/skills__visualization-9): This reference covers Vaex's visualization capabilities for creating plots, heatmaps, and interactive visualizations of large datasets.
- [visualization](https://doutorai.pt/en/components/skills__visualization-10): Comprehensive reference for visualization capabilities in pymoo.
- [visualize_graph](https://doutorai.pt/en/components/skills__visualize-graph): Args: data: PyTorch Geometric Data object title: Plot title node_color_attr: Data attribute to use for node colors (e.g., 'y', 'train_mask') edge_color_attr: Da
- [voice-agents](https://doutorai.pt/en/components/skills__voice-agents): Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it
- [voice-ai-development](https://doutorai.pt/en/components/skills__voice-ai-development): Expert in building voice AI applications - from real-time voice agents to voice-enabled apps. Covers OpenAI Realtime API, Vapi for voice agents, Deepgram for tr
- [voice-over-script](https://doutorai.pt/en/components/skills__voice-over-script): Complete narration for Loki Mode demo video.
- [voices](https://doutorai.pt/en/components/skills__voices): Listing voices, locales, speed/pitch configuration for HeyGen
- [volumes](https://doutorai.pt/en/components/skills__volumes): Modal Volumes provide high-performance distributed file systems for Modal applications. Designed for write-once, read-many workloads like ML model weights and d
- [vpc-flow-analysis](https://doutorai.pt/en/components/skills__vpc-flow-analysis): Use VPC Flow Logs to analyze traffic patterns, volume, and latency.
- [vr-ar](https://doutorai.pt/en/components/skills__vr-ar): VR/AR development principles. Comfort, interaction, performance requirements.
- [vuln_prioritizer](https://doutorai.pt/en/components/skills__vuln-prioritizer): Prioritizes vulnerabilities based on CVSS score combined with business context factors such as asset criticality, exposure, exploit availability, and compensati
- [vulnerability-scanner](https://doutorai.pt/en/components/skills__vulnerability-scanner): Advanced vulnerability analysis principles. OWASP 2025, Supply Chain Security, attack surface mapping, risk prioritization.
- [vulnerability_management_guide](https://doutorai.pt/en/components/skills__vulnerability-management-guide): This reference guide provides comprehensive information for senior secops.
- [wanda](https://doutorai.pt/en/components/skills__wanda): Wanda prunes LLMs by weight magnitude × input activation, achieving 50% sparsity with <1% accuracy loss, no retraining required.
- [wcs_and_other_modules](https://doutorai.pt/en/components/skills__wcs-and-other-modules): The WCS module manages transformations between pixel coordinates in images and world coordinates (e.g., celestial coordinates).
- [web-artifacts-builder](https://doutorai.pt/en/components/skills__web-artifacts-builder): Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for
- [web-artifacts-builder](https://doutorai.pt/en/components/skills__web-artifacts-builder-2): Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for
- [web-design-guidelines](https://doutorai.pt/en/components/skills__web-design-guidelines): Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", or "check my site 
- [web-endpoints](https://doutorai.pt/en/components/skills__web-endpoints): Creates ephemeral app with live-reloading. Changes to endpoints appear almost immediately.
- [web-games](https://doutorai.pt/en/components/skills__web-games): Web browser game development principles. Framework selection, WebGPU, optimization, PWA.
- [web-performance-optimization](https://doutorai.pt/en/components/skills__web-performance-optimization): Optimize website and web application performance including loading speed, Core Web Vitals, bundle size, caching strategies, and runtime performance
- [web-quality-audit](https://doutorai.pt/en/components/skills__web-quality-audit): Comprehensive web quality audit covering performance, accessibility, SEO, and best practices. Use when asked to "audit my site", "review web quality", "run ligh
- [web-scraper-api](https://doutorai.pt/en/components/skills__web-scraper-api): Bright Data Web Scraper API provides pre-built scrapers ("datasets") for 100+ popular websites including Amazon, LinkedIn, Instagram, TikTok, YouTube, Facebook,
- [web-security-testing](https://doutorai.pt/en/components/skills__web-security-testing): Web application security testing workflow for OWASP Top 10 vulnerabilities including injection, XSS, authentication flaws, and access control issues.
- [web-to-markdown](https://doutorai.pt/en/components/skills__web-to-markdown): Use ONLY when the user explicitly says: 'use the skill web-to-markdown ...' (or 'use a skill web-to-markdown ...'). Converts webpage URLs to clean Markdown by c
- [web-unlocker](https://doutorai.pt/en/components/skills__web-unlocker): Web Unlocker is Bright Data's HTTP-based unlocking proxy. It automatically selects the best proxy network, handles anti-bot protections, solves CAPTCHAs, and re
- [webapp-testing](https://doutorai.pt/en/components/skills__webapp-testing): Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing br
- [webapp-testing](https://doutorai.pt/en/components/skills__webapp-testing-2): Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing br
- [webhook_processing](https://doutorai.pt/en/components/skills__webhook-processing): Use search_templates({query: "webhook"}) to find more!
- [webhooks](https://doutorai.pt/en/components/skills__webhooks): Registering webhook endpoints and event types for HeyGen
- [weights-and-biases](https://doutorai.pt/en/components/skills__weights-and-biases): Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collabo
- [what-is-neon](https://doutorai.pt/en/components/skills__what-is-neon): Neon is a serverless Postgres platform designed to help you build reliable and scalable applications faster. It separates compute and storage to offer modern de
- [whisper](https://doutorai.pt/en/components/skills__whisper): OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes fr
- [wizards](https://doutorai.pt/en/components/skills__wizards): Complex multi-step processes are handled using the SlimWizard pattern. This provides a declarative way to define steps, navigation logic, and final results.
- [workflow-automation](https://doutorai.pt/en/components/skills__workflow-automation): Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost mo
- [workflow-creation](https://doutorai.pt/en/components/skills__workflow-creation): The registration process: 1. Builds Docker container with all dependencies 2. Serializes workflow code 3. Uploads container to registry 4. Generates no-code UI 
- [workflow-templates](https://doutorai.pt/en/components/skills__workflow-templates): Complete, production-ready templates for the most common GitHub Actions use cases. Use these as starting points and adapt to the user's project.
- [workflow_generator](https://doutorai.pt/en/components/skills__workflow-generator): Generates bash script templates for common deepTools workflows.
- [workflow_guide](https://doutorai.pt/en/components/skills__workflow-guide-3): This document provides detailed step-by-step workflows for common PyDESeq2 analysis patterns.
- [workflow_patterns](https://doutorai.pt/en/components/skills__workflow-patterns): This document describes detailed multi-step workflows for common bioinformatics tasks using BioServices.
- [workflows](https://doutorai.pt/en/components/skills__workflows): For complex tasks, break operations into clear, sequential steps. It is often helpful to give Claude an overview of the process towards the beginning of SKILL.m
- [workflows](https://doutorai.pt/en/components/skills__workflows-2): Step-by-step workflow for investigating production issues.
- [workflows](https://doutorai.pt/en/components/skills__workflows-3): This document provides detailed workflows for common DeepChem use cases.
- [workflows](https://doutorai.pt/en/components/skills__workflows-4): This document provides detailed examples of common mass spectrometry analysis workflows using matchms.
- [workflows](https://doutorai.pt/en/components/skills__workflows-5): This document provides comprehensive workflows, best practices, and common use cases for using SHAP in various model interpretation scenarios.
- [workflows](https://doutorai.pt/en/components/skills__workflows-6): This document covers common workflows, best practices, and advanced usage patterns for scvi-tools.
- [workflows](https://doutorai.pt/en/components/skills__workflows-7): This document provides detailed step-by-step workflows for common COBRApy tasks in metabolic modeling.
- [workflows](https://doutorai.pt/en/components/skills__workflows-8): Extended workflow examples demonstrating how to combine multiple gget modules for common bioinformatics tasks.
- [workflows](https://doutorai.pt/en/components/skills__workflows-9): This document provides complete, end-to-end examples of common workflows using ESM3 and ESM C. Each workflow includes setup, execution, and analysis code.
- [workflows](https://doutorai.pt/en/components/skills__workflows-10): This reference provides standard workflows and patterns for building, validating, and analyzing Bayesian models in PyMC.
- [workflows](https://doutorai.pt/en/components/skills__workflows-11): This document provides complete workflows for common tasks using the ETE Toolkit.
- [workflows](https://doutorai.pt/en/components/skills__workflows-12): This document provides complete workflow examples for common deepTools analyses.
- [workflows](https://doutorai.pt/en/components/skills__workflows-13): Create commands, skills, agents, or hooks as needed in their respective directories.
- [workflows](https://doutorai.pt/en/components/skills__workflows-14): For complex tasks, break operations into clear, sequential steps. It is often helpful to give Claude an overview of the process towards the beginning of SKILL.m
- [workflows](https://doutorai.pt/en/components/skills__workflows-15): Use the wrapper script and snapshot often. Assume PWCLI is set and pwcli is an alias for "$PWCLI". In this repo, run commands from output/playwright/ / to keep 
- [workflows_examples](https://doutorai.pt/en/components/skills__workflows-examples): This document provides practical workflows and usage examples for common DiffDock tasks.
- [workspaces](https://doutorai.pt/en/components/skills__workspaces): Workspaces in protocols.io enable team collaboration by organizing protocols, managing members, and controlling access permissions. The Workspaces API allows yo
- [worktree-guide](https://doutorai.pt/en/components/skills__worktree-guide): Interactive guide for parallel development with Ghostty, git worktrees, and Lazygit. Use when setting up multi-task workflows or learning the worktree system.
- [writing-clearly-and-concisely](https://doutorai.pt/en/components/skills__writing-clearly-and-concisely): Use when writing prose humans will read-documentation, commit messages, error messages, explanations, reports, or UI text. Applies Strunk's timeless rules for c
- [writing-guide](https://doutorai.pt/en/components/skills__writing-guide): This reference compiles writing advice from prominent ML researchers including Neel Nanda, Andrej Karpathy, Sebastian Farquhar, Zachary Lipton, and Jacob Steinh
- [writing-plans](https://doutorai.pt/en/components/skills__writing-plans): Use when you have a spec or requirements for a multi-step task, before touching code
- [writing-plans](https://doutorai.pt/en/components/skills__writing-plans-2): Use when you have a spec or requirements for a multi-step task, before touching code
- [writing-skills](https://doutorai.pt/en/components/skills__writing-skills): Use when creating new skills, editing existing skills, or verifying skills work before deployment
- [writing-skills](https://doutorai.pt/en/components/skills__writing-skills-2): Use when creating new skills, editing existing skills, or verifying skills work before deployment
- [writing_principles](https://doutorai.pt/en/components/skills__writing-principles): Effective scientific writing requires mastering fundamental principles that ensure clarity, precision, and impact. Unlike creative or narrative writing, scienti
- [x-twitter-scraper](https://doutorai.pt/en/components/skills__x-twitter-scraper): Use when the user wants to integrate with the X (Twitter) API via Xquik to search tweets, look up user profiles, extract followers, run giveaway draws, monitor 
- [x-twitter-scraper](https://doutorai.pt/en/components/skills__x-twitter-scraper-2): X API & Twitter scraper skill for AI coding agents. Builds integrations with the Xquik REST API, MCP server & webhooks: tweet search, user lookup, follower extr
- [xdg-config](https://doutorai.pt/en/components/skills__xdg-config): The audience is future-you, probably confused.
- [xlsx](https://doutorai.pt/en/components/skills__xlsx): Spreadsheet toolkit (.xlsx/.csv). Create/edit with formulas/formatting, analyze data, visualization, recalculate formulas, for spreadsheet processing and analys
- [xlsx](https://doutorai.pt/en/components/skills__xlsx-2): Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xl
- [xlsx-official](https://doutorai.pt/en/components/skills__xlsx-official): Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xl
- [xml](https://doutorai.pt/en/components/skills__xml): See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace.
- [xml](https://doutorai.pt/en/components/skills__xml-2): See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace.
- [xml](https://doutorai.pt/en/components/skills__xml-3): See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace.
- [xml](https://doutorai.pt/en/components/skills__xml-4): See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace.
- [xml](https://doutorai.pt/en/components/skills__xml-5): See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace.
- [xml](https://doutorai.pt/en/components/skills__xml-6): See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace.
- [xml](https://doutorai.pt/en/components/skills__xml-7): See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace.
- [xml](https://doutorai.pt/en/components/skills__xml-8): See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace.
- [xml](https://doutorai.pt/en/components/skills__xml-9): See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace.
- [xml](https://doutorai.pt/en/components/skills__xml-10): See http://www.w3.org/XML/1998/namespace.html and http://www.w3.org/TR/REC-xml for information about this namespace.
- [yeet](https://doutorai.pt/en/components/skills__yeet-2): PNG IHDR d d IDATxMO G QW GC ȹ p7w|/9 νR$S"b &mD+"P YoQM ^3d}gIVc%&Y{ :=ksl]zw ,Jp z| ^XFX@% 8B ̯̈́@PY˟nF N ϛN_%|a!k "^[ MT }z P]֠ ƙ#Y '2C-ˌAQV (He _mʌAQ @KV QE/
- [zafiro-shortcuts](https://doutorai.pt/en/components/skills__zafiro-shortcuts): Use these Zafiro extension methods to replace standard, more verbose Reactive and DynamicData patterns.
- [zapier-make-patterns](https://doutorai.pt/en/components/skills__zapier-make-patterns): No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. Bu
- [zapier-workflows](https://doutorai.pt/en/components/skills__zapier-workflows): Manage and trigger pre-built Zapier workflows and MCP tool orchestration. Use when user mentions workflows, Zaps, automations, daily digest, research, search, l
- [zaps](https://doutorai.pt/en/components/skills__zaps): This file documents all webhook-triggered Zapier workflows. Each Zap is pre-built, tested, and optimized.
- [zarr-python](https://doutorai.pt/en/components/skills__zarr-python): Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pi
- [zinc-database](https://doutorai.pt/en/components/skills__zinc-database): Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screeni
- [zod-validation-expert](https://doutorai.pt/en/components/skills__zod-validation-expert): Expert in Zod - TypeScript-first schema validation. Covers parsing, custom errors, refinements, type inference, and integration with React Hook Form, Next.js, a
- [zustand-store-ts](https://doutorai.pt/en/components/skills__zustand-store-ts): Create Zustand stores following established patterns with proper TypeScript types and middleware.
- [last30days-ai](https://doutorai.pt/en/components/skills__last30days-ai): Stay current: pulls what actually happened in AI in the last 30 days (models, releases, news) on demand inside Claude Code / Codex.
- [template-skill](https://doutorai.pt/en/components/skills__anthropics-skills__template): Replace with description of the skill and when Claude should use it.
- [web-artifacts-builder](https://doutorai.pt/en/components/skills__anthropics-skills__web-artifacts-builder): Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for
- [doc-coauthoring](https://doutorai.pt/en/components/skills__anthropics-skills__doc-coauthoring): Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs,
- [algorithmic-art](https://doutorai.pt/en/components/skills__anthropics-skills__algorithmic-art): Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generati
- [pptx](https://doutorai.pt/en/components/skills__anthropics-skills__pptx): Use this skill any time a .pptx or .potx file is involved in any way - as input, output, or both. This includes: creating slide decks, pitch decks, or presentat
- [webapp-testing](https://doutorai.pt/en/components/skills__anthropics-skills__webapp-testing): Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing br
- [frontend-design](https://doutorai.pt/en/components/skills__anthropics-skills__frontend-design): Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making ch
- [skill-creator](https://doutorai.pt/en/components/skills__anthropics-skills__skill-creator): Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an e
- [internal-comms](https://doutorai.pt/en/components/skills__anthropics-skills__internal-comms): A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever 
- [canvas-design](https://doutorai.pt/en/components/skills__anthropics-skills__canvas-design): Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, d
- [xlsx](https://doutorai.pt/en/components/skills__anthropics-skills__xlsx): Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xl
- [theme-factory](https://doutorai.pt/en/components/skills__anthropics-skills__theme-factory): Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fo
- [pdf](https://doutorai.pt/en/components/skills__anthropics-skills__pdf): Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple P
- [docx](https://doutorai.pt/en/components/skills__anthropics-skills__docx): Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files) or Word templates (.dotx files). Triggers include: any 
- [brand-guidelines](https://doutorai.pt/en/components/skills__anthropics-skills__brand-guidelines): Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand color
- [slack-gif-creator](https://doutorai.pt/en/components/skills__anthropics-skills__slack-gif-creator): Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request a
- [loop-library](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__loop-library): Discover, find, compare, audit, repair, adapt, and design repeatable AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrai
- [litreview](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__litreview): Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default - with the Consensus MCP as an optio
- [research](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__research): Default entry point for any research request - a hybrid router that classifies the question deterministically and either delegates to a specialist research skil
- [patent](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__patent): Patent prior-art and landscape intelligence skill - not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search / freedom-t
- [dossier](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__dossier): Decision-grade entity research skill - produces a hypothesis-tested dossier on a specific company, person, nonprofit, or government org, not a generic profile. 
- [deep-research](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__deep-research): Run a disciplined, multi-source research investigation for a high-stakes question or decision - fan-out web search across many channels, parallel sub-agents, so
- [pulse](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__pulse): Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable r
- [syllabus](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__syllabus): Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audienc
- [grants](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__grants): NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known i
- [notebooklm](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__notebooklm): Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] not
- [reflect](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__reflect): Mid-conversation reflection skill that pauses execution and zooms out from detail-mode to honestly reassess direction, assumptions, and bias. Use when the user 
- [meetings](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__meetings): Use when someone wants to decide whether a meeting is worth calling, price a meeting in dollars, build a timeboxed agenda with desired outcomes, or turn messy m
- [weekly-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__weekly-review): Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed revi
- [handoff](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__handoff): Compact the current conversation into a handoff document for another agent to pick up. Save to a user-configured location (OS temp, home folder, or per-project 
- [capture](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__capture): Captures and organizes chaotic brain dumps into a structured, actionable system with zero information loss. Use this skill whenever the user says 'capture this'
- [inbox-setup](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__inbox-setup): One-time setup skill that builds a personalized inbox triage knowledge base via interactive interview. Interviews the user about their email patterns, business 
- [inbox-triage](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__inbox-triage): Runs a full inbox triage using the knowledge base created by the 'inbox-setup' skill. Light-intake by design (most invocations skip questions and run with KB-de
- [roast](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__roast): Use when someone asks to roast an idea, pressure-test or stress-test an idea, validate a business idea, "convene the panel", get a brutal second opinion before 
- [fable-goal](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__fable-goal): Convert a rambling description of a desired outcome into one polished, autonomous /goal prompt ready to paste into a fresh session. Use when the user says "/fab
- [andreessen](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__andreessen): Marc Andreessen-mode decision and productivity skill. A blunt, market-first operator that pressure-tests ideas, ventures, features, and career bets through Andr
- [deep-work](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__deep-work): Use when someone wants to plan a deep work day, time-block their calendar or task list, budget or cut shallow work, protect focus hours, track deep-work session
- [minimalist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__minimalist): Use when the user asks to write code efficiently, avoid over-engineering, reduce dependencies, or prevent unnecessary abstractions. Enforces a strict efficiency
- [strict-api](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__strict-api): Use when the user says 'no hallucinations', 'verify APIs', 'reality check', or 'don't invent functions'. Prevents the agent from calling methods, imports, or va
- [grill-me](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__grill-me): Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to s
- [setup](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__setup): Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator. Use when the user runs /ar:set
- [autoresearch-agent](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__autoresearch-agent): Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed eval
- [resume](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__resume): Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating. Use when the user runs /ar:resume or asks to pick up a pre
- [status](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__status): Show experiment dashboard with results, active loops, and progress. Use when the user runs /ar:status or asks how an autoresearch experiment is going.
- [run](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__run): Run a single experiment iteration. Edit the target file, evaluate, keep or discard. Use when the user runs /ar:run or asks for one manual autoresearch iteration
- [loop](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__loop): Start an autonomous experiment loop with user-selected interval (10min, 1h, daily, weekly, monthly). Uses CronCreate for scheduling. Use when the user runs /ar:
- [agent-harness](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__agent-harness): Turn any domain folder of skills into a bounded agentic loop: compile a goal into a verifiable task plan, execute tasks with the domain's own tools, verify ever
- [kubernetes-operator](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__kubernetes-operator): Use when building a Kubernetes Operator - custom controllers that reconcile CRD state. Triggers on "build an operator", "CRD design", "reconcile loop", "control
- [workflow-builder](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__workflow-builder): Design and write deterministic multi-agent workflow scripts (.js files in .claude/workflows/) for Claude Code's Workflow tool. Use when a user wants to build, c
- [skillopt-sleep](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__skillopt-sleep): Use when the user wants their Claude agent to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, memory/skill consolidation, o
- [statistical-analyst](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__statistical-analyst): Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Use when you need to val
- [eval](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__eval): Evaluate and rank agent results by metric or LLM judge for an AgentHub session. Use when the user runs /hub:eval or asks to score, compare, or pick a winner amo
- [agenthub](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__agenthub): Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are 
- [status](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__status-a6317a): Show DAG state, agent progress, and branch status for an AgentHub session. Use when the user runs /hub:status or asks how the AgentHub agents are doing.
- [board](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__board): Read, write, and browse the AgentHub message board for agent coordination. Use when the user runs /hub:board or asks to post, read, or inspect coordination mess
- [run](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__run-b8f4cf): One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation. Use when the user runs /hub:run or asks to execute a full 
- [spawn](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__spawn): Launch N parallel subagents in isolated git worktrees to compete on the session task. Use when the user runs /hub:spawn or asks to start the competing agents fo
- [init](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__init): Create a new AgentHub collaboration session with task, agent count, and evaluation criteria. Use when the user runs /hub:init or asks to start a multi-agent com
- [merge](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__merge): Merge the winning agent's branch into base, archive losers, and clean up worktrees. Use when the user runs /hub:merge or asks to land the winning AgentHub resul
- [code-tour](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__code-tour): Use when the user asks to create a CodeTour .tour file - persona-targeted, step-by-step walkthroughs that link to real files and line numbers. Trigger for: crea
- [feature-flags-architect](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__feature-flags-architect): Use when adding, retiring, or auditing feature flags. Triggers on "add a flag", "ship behind a flag", "rollout plan", "kill switch", "stale flags", "flag debt",
- [helm-chart-builder](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__helm-chart-builder): Helm chart development agent skill and plugin for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw - chart scaffolding, values design, template patterns, depend
- [handoff](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__handoff-0727c9): Compact the current conversation into a handoff document for another agent to pick up. References existing artifacts (PRDs, plans, ADRs, issues, commits, diffs)
- [docker-development](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__docker-development): Docker and container development agent skill and plugin for Dockerfile optimization, docker-compose orchestration, multi-stage builds, and container security ha
- [chaos-engineering](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__chaos-engineering): Use when planning, running, or learning from chaos engineering experiments. Triggers on "chaos experiment", "fault injection", "gameday", "resilience test", "bl
- [prompt-governance](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__prompt-governance): Use when managing prompts in production at scale: versioning prompts, running A/B tests on prompts, building prompt registries, preventing prompt regressions, o
- [karpathy-coder](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__karpathy-coder): Use when writing, reviewing, or committing code to enforce Karpathy's 4 coding principles - surface assumptions before coding, keep it simple, make surgical cha
- [grill-with-docs](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__grill-with-docs): Docs-anchored grilling session - challenges a plan against the project's existing language (CONTEXT.md) and recorded decisions (docs/adr/), and updates those fi
- [slo-architect](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__slo-architect): Use when defining, reviewing, or operating SLOs/SLIs/error budgets. Triggers on "define an SLO", "what should our SLO be", "error budget", "burn rate", "SLI", "
- [write-a-skill](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__write-a-skill): Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill
- [zero-hallucination-coder](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__zero-hallucination-coder): Runs a disciplined Discuss -> Map -> Decompose -> Execute -> Verify loop that grounds code in verified structure - no invented APIs, no assumed imports, no plac
- [skill-tester](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__skill-tester): Validate, test, and score the quality of skills within the claude-skills ecosystem. Comprehensive meta-skill: structure validation, Python script testing (synta
- [migration-architect](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__migration-architect): Zero-downtime migration planning, compatibility validation, and rollback strategy generation. Tools for system, database, and infrastructure migrations with min
- [changelog-generator](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__changelog-generator): Produce consistent, auditable release notes from Conventional Commits. Separates commit parsing, semantic-bump logic, and changelog rendering for automated rele
- [codebase-onboarding](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__codebase-onboarding): Analyze a codebase and generate onboarding documentation for engineers, tech leads, and contractors. Fast fact-gathering and repeatable onboarding outputs. Use 
- [api-design-reviewer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__api-design-reviewer): Comprehensive REST API design review with automated linting, breaking-change detection, and design scorecards. Catches inconsistent conventions, missing version
- [rag-architect](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__rag-architect): Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision
- [self-eval](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__self-eval): Honestly evaluate AI work quality using a two-axis scoring system. Use after completing a task, code review, or work session to get an unbiased assessment. Dete
- [tech-debt-tracker](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__tech-debt-tracker): Scan codebases for technical debt, score severity, track trends, and generate prioritized remediation plans. Use when users mention tech debt, code quality, ref
- [sql-database-assistant](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__sql-database-assistant): Use when the user asks to write SQL queries, optimize database performance, generate migrations, explore database schemas, or work with ORMs like Prisma, Drizzl
- [runbook-generator](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__runbook-generator): Generate operational runbooks from a service name - deployment, incident response, maintenance, and rollback workflows. Templated structure customizable per env
- [ci-cd-pipeline-builder](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ci-cd-pipeline-builder): Generate pragmatic CI/CD pipelines from detected project stack signals - fast baseline generation, repeatable checks, environment-aware deployment stages. Use w
- [browser-automation](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__browser-automation): Use when the user asks to automate browser tasks, scrape websites, fill forms, capture screenshots, extract structured data from web pages, or build web automat
- [env-secrets-manager](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__env-secrets-manager): Manage environment-variable hygiene and secrets safety across local development and production. Practical auditing, drift awareness, rotation readiness. Use whe
- [api-test-suite-builder](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__api-test-suite-builder): Use when the user asks to generate API tests, create integration test suites, test REST endpoints, or build contract tests.
- [agent-workflow-designer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__agent-workflow-designer): Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/cont
- [engineering-advanced-skills](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__engineering-advanced-skills): Index of 37 advanced engineering agent skills for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Use when browsing or choosing among the POWERFUL-tier engine
- [agent-designer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__agent-designer): Use when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate 
- [database-designer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__database-designer): Use when the user asks to design database schemas, plan data migrations, optimize queries, choose between SQL and NoSQL, or model data relationships.
- [database-schema-designer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__database-schema-designer): Use when the user asks to create ERD diagrams, normalize database schemas, design table relationships, or plan schema migrations.
- [spec-driven-workflow](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__spec-driven-workflow): Use when the user asks to write specs before code, define acceptance criteria, plan features before implementation, generate tests from specifications, or follo
- [performance-profiler](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__performance-profiler): Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle s
- [dependency-auditor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__dependency-auditor): Audit and manage dependencies across multi-language projects. Identifies vulnerabilities, license conflicts, transitive dependency risks, and safe-upgrade paths
- [secrets-vault-manager](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__secrets-vault-manager): Use when the user asks to set up secret management infrastructure, integrate HashiCorp Vault, configure cloud secret stores (AWS Secrets Manager, Azure Key Vaul
- [pr-review-expert](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__pr-review-expert): Use when the user asks to review pull requests, analyze code changes, check for security issues in PRs, or assess code quality of diffs.
- [monorepo-navigator](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__monorepo-navigator): Navigate, manage, and optimize monorepos. Covers Turborepo, Nx, pnpm workspaces, and Lerna. Cross-package impact analysis, selective builds/tests on affected pa
- [full-page-screenshot](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__full-page-screenshot): Use when the user asks to capture a full-page screenshot, long screenshot, or complete page capture of a web page. Handles SPA scroll containers, lazy-loaded im
- [git-worktree-manager](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__git-worktree-manager): Run parallel feature work safely with Git worktrees. Standardizes branch isolation, port allocation, environment sync, and cleanup so each worktree behaves like
- [interview-system-designer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__interview-system-designer): This skill should be used when the user asks to "design interview processes", "create hiring pipelines", "calibrate interview loops", "generate interview questi
- [focused-fix](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__focused-fix): Use when the user asks to fix, debug, or make a specific feature/module/area work end-to-end. Triggers: 'make X work', 'fix the Y feature', 'the Z module is bro
- [observability-designer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__observability-designer): Design production-ready observability strategies combining metrics, logs, and traces. Includes SLI/SLO design, golden-signals monitoring, alert optimization. Us
- [tc-tracker](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__tc-tracker): Use when the user asks to track technical changes, create change records, manage TC lifecycles, or hand off work between AI sessions. Covers init/create/update/
- [security-guidance](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__security-guidance): PreToolUse security-anti-pattern hook for Claude Code. Catches 12 common security risks (command injection, XSS, SQL injection, unsafe deserialization, GitHub A
- [llm-wiki](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__llm-wiki): Use when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/conc
- [collab-proof](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__collab-proof): Use when you want to understand what Claude contributed vs what you drove in a session. Triggers on: /collab-proof, session retrospective, ai contribution analy
- [demo-video](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__demo-video): Use when the user asks to create a demo video, product walkthrough, feature showcase, animated presentation, marketing video, or GIF from screenshots or scene d
- [data-quality-auditor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__data-quality-auditor): Audit datasets for completeness, consistency, accuracy, and validity. Profile data distributions, detect anomalies and outliers, surface structural issues, and 
- [terraform-patterns](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__terraform-patterns): Terraform infrastructure-as-code agent skill and plugin for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Covers module design patterns, state management st
- [universal-scraping-architect](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__universal-scraping-architect): Use for web scraping, crawling, document extraction, API parsing, or building validation-heavy data pipelines using Firecrawl or local Python scripts.
- [claude-coach](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__claude-coach): Personal coach that teaches users to become Claude power users. Use this skill the FIRST time a user asks to "learn Claude", "be a power user", "coach me", "tea
- [llm-cost-optimizer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__llm-cost-optimizer): Use proactively whenever LLM API costs come up -- or should. Triggers include: 'my AI costs are too high', 'optimize token usage', 'which model should I use', '
- [behuman](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__behuman): Use when the user wants more human-like AI responses - less robotic, less listy, more authentic. Triggers: 'behuman', 'be real', 'like a human', 'more human', '
- [vendor-management](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__vendor-management): Use when reviewing, scoring, or auditing third-party SaaS / vendor relationships - running a vendor scorecard with industry tuning, tracking SLA compliance with
- [business-operations-skills](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__business-operations-skills): Use when running, diagnosing, or designing internal business operations - process documentation, vendor SLAs, capacity planning, internal comms, SOP/runbook aut
- [capacity-planner](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__capacity-planner): Use when an ops leader (Director of CX, Head of Support, VP Ops, Head of BizOps, Head of IT ops, Head of Finance ops) is sizing ops capacity, building a headcou
- [internal-comms](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__internal-comms): Use when a Head of People Ops, BizOps lead, or Internal Communications owner needs to draft and sequence an internal-only change-management communication - a re
- [knowledge-ops](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__knowledge-ops): Use when a Head of Ops, Knowledge Manager, or TPM-Internal needs to author, validate, or clean up company SOPs and internal runbooks (procurement intake, vendor
- [procurement-optimizer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__procurement-optimizer): Use when running an annual SaaS audit, doing category-level spend review, or rationalizing the supplier base - when the user needs a spend audit, spend categori
- [process-mapper](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__process-mapper): Use when a BizOps lead, COO, or process-improvement owner needs to document an end-to-end business process (procurement, employee onboarding, incident handoff, 
- [pm-skills](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__pm-skills): Use when coordinating project-delivery work across the 8 project-management sub-skills - sprint/velocity analytics, portfolio health, Jira/JQL, Confluence, Atla
- [team-communications](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__team-communications): Write internal company communications - 3P updates (Progress/Plans/Problems), company-wide newsletters, FAQ roundups, incident reports, leadership updates, stat
- [senior-pm](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-pm): Senior Project Manager for enterprise software, SaaS, and digital transformation projects. Specializes in portfolio management, quantitative risk analysis, reso
- [scrum-master](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__scrum-master): Advanced Scrum Master skill for data-driven agile team analysis and coaching. Use when the user asks about sprint planning, velocity tracking, retrospectives, s
- [jira-expert](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__jira-expert): Atlassian Jira expert for creating and managing projects, planning, product discovery, JQL queries, workflows, custom fields, automation, reporting, and all Jir
- [confluence-expert](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__confluence-expert): Atlassian Confluence expert for creating and managing spaces, knowledge bases, and documentation. Configures space permissions and hierarchies, creates page tem
- [atlassian-templates](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__atlassian-templates): Atlassian Template and Files Creator/Modifier expert for creating, modifying, and managing Jira and Confluence templates, blueprints, custom layouts, reusable c
- [meeting-analyzer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__meeting-analyzer): Analyzes meeting transcripts and recordings to surface behavioral patterns, communication anti-patterns, and actionable coaching feedback. Use this skill whenev
- [atlassian-admin](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__atlassian-admin): Atlassian Administrator for managing and organizing Atlassian products (Jira, Confluence, Bitbucket, Trello), users, permissions, security, integrations, system
- [a11y-audit](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__a11y-audit): Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML cod
- [google-workspace-cli](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__google-workspace-cli): Google Workspace administration via the gws CLI (github.com/googleworkspace/cli). Install, authenticate, and automate Gmail, Drive, Sheets, Calendar, Docs, Chat
- [cloud-security](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cloud-security): Use when assessing cloud infrastructure for security misconfigurations, IAM privilege escalation paths, S3 public exposure, open security group rules, or IaC se
- [named-persona-adversarial-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__named-persona-adversarial-review): Code review through the lens of real engineers' documented philosophies (Torvalds, Thompson, Carmack, Kent Beck, Jobs, Cagan). Complements abstract-role adversa
- [senior-backend](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-backend): Designs and implements backend systems including REST APIs, microservices, database architectures, authentication flows, and security hardening. Use when the us
- [incident-response](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__incident-response): Use when a security incident has been detected or declared and needs classification, triage, escalation path determination, and forensic evidence collection. Co
- [senior-computer-vision](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-computer-vision): Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster 
- [adversarial-reviewer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__adversarial-reviewer): Adversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when y
- [senior-data-engineer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-data-engineer): Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and
- [aws-solution-architect](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__aws-solution-architect): Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templ
- [senior-ml-engineer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-ml-engineer): ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG 
- [security-pen-testing](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__security-pen-testing): Use when the user asks to perform security audits, penetration testing, vulnerability scanning, OWASP Top 10 checks, or offensive security assessments. Covers s
- [azure-cloud-architect](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__azure-cloud-architect): Design Azure architectures for startups and enterprises. Use when asked to design Azure infrastructure, create Bicep/ARM templates, optimize Azure costs, set up
- [stripe-integration-expert](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__stripe-integration-expert): Production-grade Stripe integrations: subscriptions with trials and proration, one-time payments, usage-based billing, checkout sessions, idempotent webhook han
- [tdd-guide](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__tdd-guide): Test-driven development skill for writing unit tests, generating test fixtures and mocks, analyzing coverage gaps, and guiding red-green-refactor workflows acro
- [ai-security](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ai-security): Use when assessing AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, or agent tool abuse. Covers MIT
- [senior-data-scientist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-data-scientist): World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing 
- [senior-secops](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-secops): Senior SecOps engineer skill for application security, vulnerability management, compliance verification, and secure development practices. Runs SAST/DAST scans
- [ms365-tenant-manager](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ms365-tenant-manager): Microsoft 365 tenant administration for Global Administrators. Automate M365 tenant setup, Office 365 admin tasks, Azure AD user management, Exchange Online con
- [senior-frontend](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-frontend): Frontend development skill for React, Next.js, TypeScript, and Tailwind CSS applications. Use when building React components, optimizing Next.js performance, an
- [senior-security](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-security): Use when the user asks for STRIDE threat modeling, DREAD risk scoring, data-flow-diagram threat analysis, or a quick secret scan - or when a security request ne
- [threat-detection](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__threat-detection): Use when hunting for threats in an environment, analyzing IOCs, or detecting behavioral anomalies in telemetry. Covers hypothesis-driven threat hunting, IOC swe
- [code-reviewer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__code-reviewer): Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin, C#, .NET, Java, C, C++, Rust, Ruby, PHP, and Dart/Flutter. Analyzes PRs for comple
- [email-template-builder](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__email-template-builder): Build complete transactional email systems: React Email templates, provider integration (Resend, Postmark, SendGrid, AWS SES), preview server, i18n support, dar
- [red-team](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__red-team): Use when planning or executing authorized red team engagements, attack path analysis, or offensive security simulations. Covers MITRE ATT&CK kill-chain planning
- [senior-architect](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-architect): This skill should be used when the user asks to "design system architecture", "evaluate microservices vs monolith", "create architecture diagrams", "analyze dep
- [gcp-cloud-architect](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__gcp-cloud-architect): Design GCP architectures for startups and enterprises. Use when asked to design Google Cloud infrastructure, deploy to GKE or Cloud Run, configure BigQuery pipe
- [senior-qa](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-qa): Generates unit tests, integration tests, and E2E tests for React/Next.js applications. Scans components to create Jest + React Testing Library test stubs, analy
- [senior-fullstack](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-fullstack): Fullstack development toolkit with project scaffolding for Next.js, FastAPI, MERN, and Django stacks, code quality analysis with security and complexity scoring
- [engineering-skills](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__engineering-skills): Index of the engineering-team skills bundle for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw, and 6 more tools. Architecture, frontend, backend, QA, DevOps,
- [tech-stack-evaluator](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__tech-stack-evaluator): Technology stack evaluation and comparison with TCO analysis, security assessment, and ecosystem health scoring. Use when comparing frameworks, evaluating techn
- [incident-commander](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__incident-commander): Comprehensive incident response framework from detection through resolution and post-incident review. Battle-tested SRE/DevOps practices: severity classificatio
- [senior-prompt-engineer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-prompt-engineer): Use when the user asks to optimize prompts, design prompt templates, evaluate LLM outputs with an eval set, measure RAG retrieval quality, validate agent/tool c
- [senior-devops](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__senior-devops): Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructur
- [playwright-pro](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__pw): Production-grade Playwright testing toolkit. Use when the user mentions Playwright tests, end-to-end testing, browser automation, fixing flaky tests, test migra
- [snowflake-development](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__snowflake-development): Use when writing Snowflake SQL, building data pipelines with Dynamic Tables or Streams/Tasks, using Cortex AI functions, creating Cortex Agents, writing Snowpar
- [extract](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__extract): Turn a proven pattern or debugging solution into a standalone reusable skill with SKILL.md, reference docs, and examples. Use when the user runs /si:extract or 
- [memory-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__memory-review): Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics. Use when the user runs /si:memory-review or asks w
- [promote](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__promote): Graduate a proven pattern from auto-memory (MEMORY.md) to CLAUDE.md or .claude/rules/ for permanent enforcement. Use when the user runs /si:promote or asks to m
- [remember](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__remember): Explicitly save important knowledge to auto-memory with timestamp and context. Use when a discovery is too important to rely on auto-capture.
- [self-improving-agent](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__self-improving-agent): Curate Claude Code's auto-memory into durable project knowledge. Analyze MEMORY.md for patterns, promote proven learnings to CLAUDE.md and .claude/rules/, extra
- [memory-status](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__memory-status): Memory health dashboard showing line counts, topic files, capacity, stale entries, and recommendations. Use when the user runs /si:memory-status or asks how ful
- [iso27001-audit-prep](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__iso27001-audit-prep): /cs:iso27001-audit-prep - ISO 27001 ISMS audit readiness 6-question forcing interrogation. Use before annual Clause 9.2 internal audit, surveillance audit prep,
- [aims-audit](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__aims-audit): /cs:aims-audit - ISO/IEC 42001 AIMS internal-audit 6-question forcing interrogation. Use before certification stage 1, before annual internal audit cycles, or w
- [compliance-os](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__compliance-os): Compliance OS - meta-orchestrator that lets compliance teams CONFIGURE which frameworks apply, COMPUTE cross-framework control overlap, SIMULATE internal audits
- [fda-qsr-audit-prep](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__fda-qsr-audit-prep): /cs:fda-qsr-audit-prep - FDA 21 CFR 820 (QSR / QMSR) audit 6-question forcing interrogation. Post-Feb 2026 substantially harmonized with ISO 13485. Use before a
- [ai-act-readiness](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ai-act-readiness): /cs:ai-act-readiness - EU AI Act 6-question forcing interrogation. Use during AI-system intake, before EU deployment, or during annual compliance refresh as Art
- [compliance-readiness](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__compliance-readiness): /cs:compliance-readiness - Multi-framework compliance officer 6-question forcing interrogation of any compliance program. Use before starting a new framework, p
- [iso13485-audit-prep](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__iso13485-audit-prep): /cs:iso13485-audit-prep - ISO 13485 QMS audit 6-question forcing interrogation. Design controls + CAPA + post-market focused. Use before Clause 8.2.4 internal a
- [gdpr-audit-prep](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__gdpr-audit-prep): /cs:gdpr-audit-prep - GDPR audit 6-question Article-cited forcing interrogation. Use before annual internal GDPR review, post-breach internal audit, DPA investi
- [soc2-audit-prep](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__soc2-audit-prep): /cs:soc2-audit-prep - SOC 2 Type II readiness 6-question forcing interrogation. Observation-period focused. Use before Type II observation begins, mid-period ch
- [marketing-ops](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__marketing-ops): Central router for the marketing skill ecosystem. Use when unsure which marketing skill to use, when orchestrating a multi-skill campaign, or when coordinating 
- [youtube-full](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__youtube-full): Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript fo
- [pricing-strategy](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__pricing-strategy): Design, optimize, and communicate SaaS pricing - tier structure, value metrics, pricing pages, and price increase strategy. Use when building a pricing model fr
- [social-content](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__social-content): When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, Facebook, or other platforms. Also
- [schema-markup](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__schema-markup): When the user wants to implement, audit, or validate structured data (schema markup) on their website. Use when the user mentions 'structured data,' 'schema.org
- [launch-strategy](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__launch-strategy): When the user wants to plan a product launch, feature announcement, or release strategy. Also use when the user mentions 'launch,' 'Product Hunt,' 'feature rele
- [content-creator](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__content-creator): Deprecated redirect skill that routes legacy 'content creator' requests to the correct specialist. Use when a user invokes 'content creator', asks to write a bl
- [ad-creative](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ad-creative): When the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variati
- [prompt-engineer-toolkit](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__prompt-engineer-toolkit): Turns marketing prompts into tested, versioned production assets: A/B prompt evaluation against structured test cases, immutable prompt version history with dif
- [free-tool-strategy](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__free-tool-strategy): When the user wants to build a free tool for marketing - lead generation, SEO value, or brand awareness. Use when they mention 'engineering as marketing,' 'free
- [referral-program](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__referral-program): When the user wants to design, launch, or optimize a referral or affiliate program. Use when they mention 'referral program,' 'affiliate program,' 'word of mout
- [programmatic-seo](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__programmatic-seo): When the user wants to create SEO-driven pages at scale using templates and data. Also use when the user mentions "programmatic SEO," "template pages," "pages a
- [paid-ads](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__paid-ads): When the user wants help with paid advertising campaigns on Google Ads, Meta (Facebook/Instagram), LinkedIn, Twitter/X, or other ad platforms. Also use when the
- [page-cro](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__page-cro): When the user wants to optimize, improve, or increase conversions on any marketing page - including homepage, landing pages, pricing pages, feature pages, or bl
- [marketing-strategy-pmm](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__marketing-strategy-pmm): Product marketing skill for positioning, GTM strategy, competitive intelligence, and product launches. Use when the user asks about product positioning, go-to-m
- [marketing-demand-acquisition](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__marketing-demand-acquisition): Creates demand generation campaigns, optimizes paid ad spend across LinkedIn, Google, and Meta, develops SEO strategies, and structures partnership programs. Us
- [email-sequence](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__email-sequence): When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "e
- [cold-email](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cold-email): When the user wants to write, improve, or build a sequence of B2B cold outreach emails to prospects who haven't asked to hear from them. Use when the user menti
- [marketing-psychology](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__marketing-psychology): When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental 
- [app-store-optimization](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__app-store-optimization): App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on A
- [aeo](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__aeo): Answer Engine Optimization (AEO) skill - optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sour
- [local-seo-manager](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__local-seo-manager): Manage local SEO for service-area businesses - appliance repair, HVAC, plumbing, cleaning, and any business that serves customers at their location. Use when th
- [form-cro](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__form-cro): When the user wants to optimize any form that is NOT signup/registration - including lead capture forms, contact forms, demo request forms, application forms, s
- [seo-audit](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__seo-audit): When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking,
- [paywall-upgrade-cro](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__paywall-upgrade-cro): When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade
- [marketing-ideas](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__marketing-ideas): When the user needs marketing ideas, inspiration, or strategies for their SaaS or software product. Also use when the user asks for 'marketing ideas,' 'growth i
- [marketing-skills](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__marketing-skills): Directory and router for the marketing skills library. Use when you need to find the right marketing skill for a task, see what marketing capabilities exist, or
- [social-media-analyzer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__social-media-analyzer): Social media campaign analysis and performance tracking. Calculates engagement rates, ROI, and benchmarks across platforms. Use when analyzing social media perf
- [signup-flow-cro](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__signup-flow-cro): When the user wants to optimize signup, registration, account creation, or trial activation flows. Also use when the user mentions "signup conversions," "regist
- [x-twitter-growth](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__x-twitter-growth): X/Twitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X/Twitter, write tweets or t
- [site-architecture](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__site-architecture): When the user wants to audit, redesign, or plan their website's structure, URL hierarchy, navigation design, or internal linking strategy. Use when the user men
- [marketing-context](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__marketing-context): Create and maintain the marketing context document that all marketing skills read before starting. Use when the user mentions 'marketing context,' 'brand voice,
- [webinar-marketing](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__webinar-marketing): When the user wants to plan, promote, run, or improve a webinar or virtual event to generate and convert demand. Use when the user mentions 'webinar,' 'virtual 
- [ab-test-setup](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ab-test-setup): When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this 
- [popup-cro](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__popup-cro): When the user wants to create or optimize popups, modals, overlays, slide-ins, or banners for conversion purposes. Also use when the user mentions "exit intent,
- [copywriting](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__copywriting): When the user wants to write, rewrite, or improve marketing copy for any page - including homepage, landing pages, pricing pages, feature pages, about pages, or
- [competitor-alternatives](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__competitor-alternatives): When the user wants to create competitor comparison or alternative pages for SEO and sales enablement. Also use when the user mentions 'alternative page,' 'vs p
- [campaign-analytics](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__campaign-analytics): Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization. Use when analyzing marke
- [copy-editing](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__copy-editing): When the user wants to edit, review, or improve existing marketing copy. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'p
- [content-humanizer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__content-humanizer): Makes AI-generated content sound genuinely human - not just cleaned up, but alive. Use when content feels robotic, uses too many AI clichés, lacks personality, 
- [brand-guidelines](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__brand-guidelines): When the user wants to apply, document, or enforce brand guidelines for any product or company. Also use when the user mentions 'brand guidelines,' 'brand color
- [content-production](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__content-production): Full content production pipeline - takes a topic from blank page to published-ready piece. Use when you need to execute content: write a blog post, article, or 
- [churn-prevention](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__churn-prevention): Reduce voluntary and involuntary churn through cancel flow design, save offers, exit surveys, and dunning sequences. Use when designing or optimizing a cancel f
- [social-media-manager](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__social-media-manager): When the user wants to develop social media strategy, plan content calendars, manage community engagement, or grow their social presence across platforms. Also 
- [analytics-tracking](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__analytics-tracking): Set up, audit, and debug analytics tracking implementation - GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality. Use when building a
- [onboarding-cro](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__onboarding-cro): When the user wants to optimize post-signup onboarding, user activation, first-run experience, or time-to-value. Also use when the user mentions "onboarding flo
- [content-strategy](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__content-strategy): When the user wants to plan a content strategy, decide what content to create, or figure out what topics to cover. Also use when the user mentions "content stra
- [video-content-strategist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__video-content-strategist): Use when planning video content strategy, writing video scripts, optimizing YouTube channels, building short-form video pipelines (Reels, TikTok, Shorts), or re
- [business-growth-skills](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__business-growth-skills): Router/index for the 4 business & growth skills bundled in this plugin: customer-success-manager (health scoring, churn risk, expansion), sales-engineer (RFP an
- [contract-and-proposal-writer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__contract-and-proposal-writer): Generate professional, jurisdiction-aware business documents: freelance contracts, project proposals, SOWs, NDAs, and MSAs. Structured Markdown output with docx
- [sales-engineer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__sales-engineer): Analyzes RFP/RFI responses for coverage gaps, builds competitive feature comparison matrices, and plans proof-of-concept (POC) engagements for pre-sales enginee
- [revenue-operations](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__revenue-operations): Analyzes sales pipeline health, revenue forecasting accuracy, and go-to-market efficiency metrics for SaaS revenue optimization. Use when analyzing sales pipeli
- [customer-success-manager](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__customer-success-manager): Monitors customer health, predicts churn risk, and identifies expansion opportunities using weighted scoring models for SaaS customer success. Use when analyzin
- [cs-growth-strategist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-growth-strategist): Growth Strategist agent for revenue operations, sales engineering, customer success, and business development. Orchestrates business-growth skills. Spawn when u
- [freeze](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__freeze): /cs:freeze - Lock a strategic decision for a cooldown period to prevent impulse reversal. Mirrors gstack's safety primitives for the business layer. Use when an
- [cs-karpathy-reviewer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-karpathy-reviewer): Reviews staged git changes against Karpathy's 4 coding principles. Runs complexity_checker on changed files, diff_surgeon on the diff, and produces a verdict wi
- [cs-fullstack-engineer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-fullstack-engineer): Fullstack-engineering orchestrator. Walks the Matt Pocock 7-question forcing-question grill, runs the deterministic profile picker, then forks into the POWERFUL
- [decision-logger](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__decision-logger): Two-layer memory architecture for board meeting decisions. Manages raw transcripts (Layer 1) and approved decisions (Layer 2). Use when logging decisions after 
- [saas-health](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__saas-health): Calculate SaaS health metrics (ARR, MRR, churn, CAC, LTV, NRR) and benchmark against industry standards. Usage: /saas-health [options]
- [channel-economics](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__channel-economics): Use when reviewing or rebalancing direct vs. partner-led channel economics - computing fully-loaded cost-to-serve per channel, channel ROI with cash / LTV / mar
- [chief-of-staff](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__chief-of-staff): C-suite orchestration layer. Routes founder questions to the right advisor role(s), triggers multi-role board meetings for complex decisions, synthesizes output
- [Persona-Based Agents](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__readme): Pre-configured agent personas with curated skill loadouts, workflows, and distinct personalities.
- [cmo-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cmo-review): /cs:cmo-review - Narrative-first interrogation of positioning, ICP, message house, and channel mix. Use when launching a campaign or repositioning, or when CAC 
- [post-mortem](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__post-mortem): /cs:post-mortem - Honest retrospective on an executed decision, scored against original assumptions and dissent. Closes the strategic sprint loop. Use when a de
- [market-research](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__market-research): Use when doing upstream market-research methodology - sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), pl
- [ux-researcher-designer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ux-researcher-designer): UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and re
- [general-counsel-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__skills-general-counsel-advisor): General Counsel advisory for startups: contract review (MSA, SaaS, NDA, DPA, employment), IP strategy, term sheet decoding, and regulatory landscape mapping. Us
- [information-security-manager-iso27001](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__information-security-manager-iso27001): ISO 27001 ISMS implementation and cybersecurity governance for HealthTech and MedTech companies. Use when designing an ISMS, running security risk assessments, 
- [Product Manager](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__product-manager): Ships outcomes, not features. Writes specs engineers actually read. Prioritizes ruthlessly. Kills darlings when the data says so. Operates at the intersection o
- [financial-analyst](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__financial-analyst): Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing 
- [c-level-agents](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__c-level-agents): Founder-mode executive team. 13 cs-* C-suite agents (CFO, CMO, CRO, CPO, COO, CHRO, CISO, GC, CDO, CAIO, CCO, VPE, Chief of Staff) and 21 /cs:* slash commands f
- [md-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__md-review): Converts a markdown PR writeup or code review (one with ```diff fenced blocks and severity-tagged > [!BLOCKER]/[!MAJOR]/[!MINOR]/[!NIT] callouts) into a single-
- [cs-aeo](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cmd-cs-aeo): /cs:aeo - Answer Engine Optimization workflow. Audit content for E-E-A-T + structure signals that drive LLM citation (ChatGPT, Perplexity, Claude, Gemini, Mistr
- [changelog](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__changelog): Generate changelogs from git history and validate conventional commits. Usage: /changelog [options]
- [eu-ai-act-specialist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__skills-eu-ai-act-specialist): EU AI Act (Regulation (EU) 2024/1689) operational compliance for compliance teams. Three Article-level decisions: (1) What's the risk tier of this AI system - p
- [rice](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__rice): RICE feature prioritization with scoring and capacity planning. Usage: /rice prioritize [options]
- [commercial-forecaster](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__commercial-forecaster): Use when building a quarterly bookings forecast, ARR projection, pipeline forecast, NRR projection, or commit/best-case/pipe-only board number - especially when
- [strategic-alignment](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__strategic-alignment): Cascades strategy from boardroom to individual contributor. Detects and fixes misalignment between company goals and team execution. Covers strategy articulatio
- [cs-engineering-lead](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-engineering-lead): Engineering Team Lead agent for coordinating QA, security, data engineering, ML, and frontend/backend teams. Orchestrates engineering-team skills for team-level
- [cs-webinar](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-webinar): /cs:webinar - Webinar & virtual-event marketing workflow. Plan a webinar from scratch (sized backward from the business goal), rescue one whose numbers disappoi
- [coo-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__coo-advisor): Operations leadership for scaling companies. Process design, OKR execution, operational cadence, and scaling playbooks. Use when designing operations, setting u
- [cs-agile-product-owner](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-agile-product-owner): Agile product owner agent for epic breakdown, sprint planning, backlog refinement, and INVEST-compliant user story generation. Use when preparing work for a dev
- [office-hours](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__office-hours): /cs:office-hours - YC-style 6-question founder interrogation before any advice. Forces clarity on problem, customer, distribution, defensibility, capital, and f
- [cross-eval](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cross-eval): /cs:cross-eval - Multi-model consensus on a board memo or strategy brief. Claude + Codex + Gemini cross-review with graceful degradation. Use when a high-stakes
- [pricing-strategist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__pricing-strategist): Use when designing or revisiting product pricing - selecting a pricing model (subscription seat-based, usage-based, value-based, freemium, or hybrid), running V
- [focused-fix](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cmd-focused-fix): Deep-dive feature repair - systematically fix an entire feature/module across all its files and dependencies. Usage: /focused-fix
- [brief](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__brief): /cs:brief - Generate a one-page strategy brief from an office-hours intake. First step in the strategic sprint pipeline. Use when a strategic question needs to 
- [cdo-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cdo-review): /cs:cdo-review - Decision-driven Chief Data Officer interrogation of any plan that touches training data, data architecture, data productization, or data team h
- [cpo-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cpo-advisor): Product leadership for scaling companies. Product vision, portfolio strategy, product-market fit, and product org design. Use when setting product vision, manag
- [decide](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__decide): /cs:decide - Log a decision to two-layer memory via decision-logger. Approved memo becomes durable; raw transcripts kept for reference. Use when the founder has
- [cs-product-manager](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-product-manager): Product management agent for feature prioritization, customer discovery, PRD development, and roadmap planning using RICE framework. Use when a product decision
- [Content Strategist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__content-strategist): Builds content engines that rank, convert, and compound. Thinks in systems - topic clusters, not individual posts. Every piece earns its place or gets killed. U
- [business-investment-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__business-investment-advisor): Business investment analysis and capital allocation advisor. Use when evaluating whether to invest in equipment, real estate, a new business, hiring, technology
- [cpo-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cpo-review): /cs:cpo-review - JTBD-driven interrogation of product roadmap, PMF signal, and portfolio focus. Use when committing a quarter's roadmap, deciding whether to kil
- [a11y-audit](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cmd-a11y-audit): Scan a frontend project for WCAG 2.2 accessibility violations and fix them. Usage: /a11y-audit [path]
- [cs-quality-regulatory](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-quality-regulatory): Quality & Regulatory agent for ISO 13485 QMS, MDR compliance, FDA submissions, GDPR/DSGVO, and ISMS audits. Orchestrates ra-qm-team skills. Spawn when users nee
- [arquiteto-de-empresa](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__arquiteto-de-empresa): Company Architect: builds a business from scratch as an OKF (Open Knowledge Format) bundle - a tree of version-controllable .md files with frontmatter type, lin
- [ciso-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ciso-review): /cs:ciso-review - Risk-paranoid interrogation of any plan that touches data, compliance, or production access. Use when launching features that handle customer 
- [competitive-intel](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__competitive-intel): Systematic competitor tracking that feeds CMO positioning, CRO battlecards, and CPO roadmap decisions. Use when analyzing competitors, building sales battlecard
- [/cs:fullstack-review - Fullstack engineering review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-fullstack-review): Fullstack engineering review - walks the 7 Matt Pocock forcing questions, picks the profile, forks into POWERFUL specialists (api-design-reviewer, database-desi
- [cs-wiki-linter](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-wiki-linter): Dispatched sub-agent that runs a periodic health check on an LLM Wiki vault. Runs mechanical checks via scripts (orphans, broken links, stale pages, missing fro
- [execute](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__execute): /cs:execute - Generate a 90-day execution plan with weekly milestones, DRIs, and check-in cadence from an approved decision. Use when a logged decision needs to
- [commercial-policy](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__commercial-policy): Use when designing or revising a company's commercial policy - the rules of engagement governing discounts off list price, approver thresholds, exception flows,
- [tech-debt](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__tech-debt): Scan, prioritize, and report technical debt. Usage: /tech-debt [options]
- [cs-workspace-admin](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-workspace-admin): Google Workspace administration agent using the gws CLI. Orchestrates workspace setup, Gmail/Drive/Sheets/Calendar automation, security audits, and recipe execu
- [chief-ai-officer-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__skills-chief-ai-officer-advisor): Chief AI Officer advisory for startups: model build-vs-buy decisions (API vs fine-tune vs in-house), AI risk classification under EU AI Act + US state patchwork
- [apple-hig-expert](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__apple-hig-expert): Audits and designs iOS/macOS/watchOS/visionOS interfaces against the Apple Human Interface Guidelines, including the Liquid Glass design language (announced WWD
- [ui-design-system](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ui-design-system): UI design system toolkit for Senior UI Designer including design token generation, component documentation, responsive design calculations, and developer handof
- [/cs:frontend-review - Frontend engineering review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-frontend-review): Frontend engineering review - walks the 7 Matt Pocock forcing questions (device, LCP target, rendering, bundle budget, SEO vs auth, design system, WCAG), picks 
- [challenge](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__challenge): Pre-mortem plan analysis. Imagine the plan failed 12 months from now and work backwards to find the weaknesses. Surfaces assumptions, dependencies, and executio
- [cs-frontend-engineer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-frontend-engineer): Frontend-engineering orchestrator. Walks the 7 Matt Pocock forcing questions (device, LCP target, rendering, bundle budget, SEO vs auth, design system, WCAG), p
- [agent-protocol](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__agent-protocol): Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite 
- [cfo-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cfo-advisor): Financial leadership for startups and scaling companies. Financial modeling, unit economics, fundraising strategy, cash management, and board financial packages
- [cro-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cro-review): /cs:cro-review - Pipeline-paranoid interrogation of revenue, win rate, NRR, and ramp time. Use when the forecast misses pipeline coverage, win rates drop, or be
- [boardroom](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__boardroom): /cs:boardroom - 6-phase multi-role deliberation across the C-suite with Phase 2 isolation, critic pre-screen, and synthesis. Outputs a board memo. Use when a de
- [Growth Marketer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__growth-marketer): Growth marketing specialist for bootstrapped startups and indie hackers. Builds content engines, optimizes funnels, runs launch sequences, and finds scalable ac
- [competitive-matrix](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__competitive-matrix): Build competitive analysis matrices with scoring and gap analysis. Usage: /competitive-matrix [options]
- [cto-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cto-advisor): Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Use when assessing technical debt, scaling engineering tea
- [gc-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__gc-review): /cs:gc-review - General Counsel interrogation of contracts, IP, regulatory, term sheets, and employment-law surface. Use when reviewing a term sheet before sign
- [landing-page-generator](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__landing-page-generator): Generates high-converting landing pages as complete Next.js/React (TSX) components with Tailwind CSS. Creates hero sections, feature grids, pricing tables, FAQ 
- [soc2-compliance](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__soc2-compliance): Use when the user asks to prepare for SOC 2 audits, map Trust Service Criteria, build control matrices, collect audit evidence, perform gap analysis, or assess 
- [onboard](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__onboard): /cs:onboard - Founder interview that populates ~/.claude/company-context.md using the canonical 7-dimension cs-onboard schema. The first command to run when sta
- [org-health-diagnostic](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__org-health-diagnostic): Cross-functional organizational health check combining signals from all C-suite roles. Scores 8 dimensions on a traffic-light scale with drill-down recommendati
- [Finance Lead](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__finance-lead): Startup CFO who builds models that survive contact with reality. Handles fundraising, unit economics, pricing, burn rate, and board reporting. Speaks fluent spr
- [/flag-cleanup](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__flag-cleanup): Run the quarterly feature-flag cleanup workflow on the current repo
- [ra-qm-skills](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ra-qm-skills): Router/index for the 15 regulatory & quality-management skills bundled in this plugin (ISO 13485 QMS, EU MDR 2017/745, FDA submissions under QMSR, ISO 14971 ris
- [code-to-prd](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__code-to-prd): Reverse-engineer any codebase into a complete Product Requirements Document (PRD). Analyzes routes, components, state management, API integrations, and user int
- [spec-to-repo](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__spec-to-repo): Use when the user says 'build me an app', 'create a project from this spec', 'scaffold a new repo', 'generate a starter', 'turn this idea into code', 'bootstrap
- [financial-health](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__financial-health): Run financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecasts. Usage: /financial-health
- [product-strategist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__product-strategist): Strategic product leadership toolkit for Head of Product covering OKR cascade generation, quarterly planning, competitive landscape analysis, product vision doc
- [cs-ux-researcher](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-ux-researcher): UX research agent for research planning, persona generation, journey mapping, and usability test analysis. Use when product decisions need user evidence - e.g.,
- [roadmap-communicator](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__roadmap-communicator): Use when preparing roadmap narratives, release notes, changelogs, or stakeholder updates tailored for executives, engineering teams, and customers.
- [fda-consultant-specialist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__fda-consultant-specialist): FDA regulatory consultant for medical device companies. Provides 510(k)/PMA/De Novo pathway guidance, QMSR (21 CFR 820, which incorporates ISO 13485:2016 by ref
- [tdd](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__tdd): Run a red-green-refactor TDD workflow - generate failing tests first, implement to green, then check coverage gaps. Usage: /tdd [target]
- [cs-wiki-librarian](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-wiki-librarian): Dispatched sub-agent that answers queries against an LLM Wiki vault. Reads index.md first, drills into 3-10 relevant pages across categories, synthesizes an ans
- [cs-demand-gen-specialist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-demand-gen-specialist): Demand generation and acquisition-funnel specialist orchestrating the marketing-demand-acquisition, paid-ads, and email-sequence skills. Use when building or fi
- [karpathy-check](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__karpathy-check): Run Karpathy's 4-principle review on staged changes or the last commit. Checks complexity, diff noise, hidden assumptions, and goal verification. Usage /karpath
- [ma-playbook](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ma-playbook): M&A strategy for acquiring companies or being acquired. Due diligence, valuation, integration, and deal structure. Use when evaluating acquisitions, preparing f
- [vpe-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__skills-vpe-advisor): VP of Engineering advisory for startups: delivery throughput (DORA 4 metrics + bottleneck identification), engineering hiring funnel (sourcing → screen → onsite
- [/cs:engineer-grill - Cross-role engineering forcing-question grill](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-engineer-grill): Cross-role engineering grill - Matt Pocock 7 questions per role × 3 roles (fullstack / frontend / backend) = up to 21 forcing questions, one per turn, with cano
- [google-workspace](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__google-workspace): Google Workspace CLI operations: setup diagnostics, security audit, recipe discovery, and output analysis. Usage: /google-workspace [options]
- [vpe-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__vpe-review): /cs:vpe-review - Throughput-first VP of Engineering interrogation of any plan that touches delivery, eng hiring, team structure, or production discipline. Use w
- [company-os](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__company-os): The meta-framework for how a company runs - the connective tissue between all C-suite roles. Covers operating system selection (EOS, Scaling Up, OKR-native, hyb
- [board-deck-builder](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__board-deck-builder): Assembles comprehensive board and investor update decks by pulling perspectives from all C-suite roles. Use when preparing board meetings, investor updates, qua
- [DevOps Engineer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__devops-engineer): Builds infrastructure that scales without babysitting. Automates everything worth automating. Monitors before it breaks. Treats clicking in consoles as a produc
- [experiment-designer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__experiment-designer): Use when planning product experiments, writing testable hypotheses, estimating sample size, prioritizing tests, or interpreting A/B outcomes with practical stat
- [research-ops-skills](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__research-ops-skills): Use when planning, funding, scoping, or synthesizing enterprise research across workstreams - clinical study design, R&D program finance, market sizing/surveys,
- [postmortem](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__postmortem): /em:postmortem - Honest analysis of what went wrong. Use after a failed launch, missed quarter, or bad hire to run a blameless 5-Whys retrospective with a chang
- [product-skills](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__product-skills): Use when coordinating product work across the 12 bundled product sub-skills (RICE, OKRs, UX research, design tokens, competitive teardown, analytics, experiment
- [cs-senior-engineer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-senior-engineer): Senior Engineer agent for architecture decisions, code review, DevOps, and API design. Orchestrates engineering and engineering-team skills for technical implem
- [project-health](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__project-health): Portfolio health dashboard and risk matrix analysis. Usage: /project-health [options]
- [quality-manager-qmr](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__quality-manager-qmr): Senior Quality Manager Responsible Person (QMR) for HealthTech and MedTech companies. Provides quality system governance, management review leadership, regulato
- [change-management](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__change-management): Framework for rolling out organizational changes without chaos. Covers the ADKAR model adapted for startups, communication templates, resistance patterns, and c
- [chief-customer-officer-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__skills-chief-customer-officer-advisor): Chief Customer Officer advisory for startups: retention decomposition (gross retention vs NRR honesty, churn root-cause taxonomy), customer segmentation strateg
- [/cs:backend-review - Backend engineering review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-backend-review): Backend engineering review - walks the 7 Matt Pocock forcing questions (read/write ratio + QPS, tenancy, sync vs async, data sensitivity, pattern, RPO/RTO, SLO)
- [sprint-health](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__sprint-health): Sprint health scoring and velocity analysis for agile teams. Usage: /sprint-health [options]
- [hard-call](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__hard-call): /em:hard-call - Framework for decisions with no good options. Use when every option is painful and a structured 10/10/10 + regret-minimization pass is needed - 
- [product-discovery](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__product-discovery): Use when validating product opportunities, mapping assumptions, planning discovery sprints, or testing problem-solution fit before committing delivery resources
- [agent-decision-receipts](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__agent-decision-receipts): Mint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified la
- [cs-project-manager](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-project-manager): Project Manager agent for sprint planning, Jira/Confluence workflows, Scrum ceremonies, and stakeholder reporting. Orchestrates project-management skills. Use w
- [chro-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__chro-advisor): People leadership for scaling companies. Hiring strategy, compensation design, org structure, culture, and retention. Use when building hiring plans, designing 
- [internal-narrative](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__internal-narrative): Build and maintain one coherent company story across all audiences - employees, investors, customers, candidates, and partners. Detects narrative contradictions
- [pipeline](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__pipeline): Detect stack and generate CI/CD pipeline configs. Usage: /pipeline [options]
- [research-summarizer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__research-summarizer): Structured research summarization agent skill for non-dev users. Handles academic papers, web articles, reports, and documentation. Extracts key findings, gener
- [executive-mentor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__executive-mentor): Adversarial thinking partner for founders and executives. Stress-tests plans, prepares for brutal board meetings, dissects decisions with no good options, and f
- [mdr-745-specialist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__mdr-745-specialist): EU MDR 2017/745 compliance specialist for medical device classification, technical documentation, clinical evidence, and post-market surveillance. Covers Annex 
- [saas-scaffolder](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__saas-scaffolder): Generates complete, production-ready SaaS project boilerplate including authentication, database schemas, billing integration, API routes, and a working dashboa
- [Agent Name](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__template): One paragraph describing what this agent does, who it's for, and when to activate it.
- [product-manager-toolkit](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__product-manager-toolkit): Comprehensive toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market str
- [competitive-teardown](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__competitive-teardown): Analyzes competitor products and companies by synthesizing data from pricing pages, app store reviews, job postings, SEO signals, and social media into structur
- [md-document](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__md-document): Converts long-form markdown (specs, RFCs, reports, plans, explainers) into a single-file, lightly-interactive HTML document with sticky TOC, scrollspy, search f
- [cs-cto-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-cto-advisor): Technical leadership advisor for CTOs covering technology strategy, team scaling, architecture decisions, and engineering excellence. Use when a CTO or technica
- [cco-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cco-review): /cs:cco-review - Retention-obsessed Chief Customer Officer interrogation of any plan that touches customer retention, segmentation, CS team sizing, or CS team h
- [cs-webinar-marketer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-webinar-marketer): Webinar & virtual-event marketing specialist agent. Use when planning, promoting, running, or rescuing a webinar, virtual event, live demo, workshop, masterclas
- [board-prep](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__board-prep): Board meeting preparation for the adversarial scenario, not the friendly one. Forces numbers-cold mastery, anticipates hard questions, builds a narrative that a
- [clinical-research](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__clinical-research): Use when designing a prospective clinical study before submission - selecting and classifying endpoints (primary / key-secondary / exploratory, with surrogate-e
- [cs-onboard](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-onboard): Founder onboarding interview that captures company context across 7 dimensions. Invoke with /cs:setup for initial interview or /cs:update for quarterly refresh.
- [cs-backend-engineer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-backend-engineer): Backend-engineering orchestrator. Walks the 7 Matt Pocock forcing questions (read/write ratio + QPS, tenancy, sync vs async, data sensitivity, pattern, RPO/RTO,
- [deal-desk](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__deal-desk): Use when reviewing a specific inbound deal before close - when sales has asked for a discount that exceeds AE authority, when the customer has redlined the MSA,
- [product-research](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__product-research): Use when planning and synthesizing product/user research as a method-and-repository discipline - selecting the right method for the goal (generative interviews 
- [cmo-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cmo-advisor): Marketing leadership for scaling companies. Brand positioning, growth model design, marketing budget allocation, and marketing org design. Use when designing br
- [culture-architect](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__culture-architect): Build, measure, and evolve company culture as operational behavior - not wall posters. Covers mission/vision/values workshops, values-to-behaviors translation, 
- [founder-mode](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__founder-mode): /cs:founder-mode - Auto-routes any founder question to the right C-role advisor or to /cs:boardroom for multi-role topics. The single-command entry point. Use w
- [rfp-responder](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__rfp-responder): Use when an RFP, RFI, RFQ, security questionnaire, vendor questionnaire, or proposal request arrives and the team needs a structured response - parsing multi-se
- [scenario-war-room](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__scenario-war-room): Cross-functional what-if modeling for cascading multi-variable scenarios. Unlike single-assumption stress testing, this models compound adversity across all bus
- [cs-content-creator](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-content-creator): Long-form marketing content producer orchestrating the content-production skill (research → brief → draft → optimize → gate). Use when content must be written, 
- [cfo-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cfo-review): /cs:cfo-review - Numerate-skeptic interrogation of any plan that touches money. Unit economics, runway, dilution, capital allocation. Use when a plan commits me
- [quality-manager-qms-iso13485](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__quality-manager-qms-iso13485): ISO 13485 Quality Management System implementation and maintenance for medical device organizations. Provides QMS design, documentation control, internal auditi
- [cs-product-analyst](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-product-analyst): Product analytics agent for KPI definition, dashboard setup, experiment design, and test result interpretation. Use when a product question needs numbers - e.g.
- [wiki-ingest](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__wiki-ingest): Ingest a source file from raw/ into the LLM Wiki - read, discuss, write summary page, update cross-references across 5-15 pages, regenerate index, append to log
- [gdpr-dsgvo-expert](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__gdpr-dsgvo-expert): GDPR and German DSGVO compliance automation. Scans codebases for privacy risks, generates DPIA documentation, tracks data subject rights requests with Art. 12(3
- [cs-wiki-ingestor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-wiki-ingestor): Dispatched sub-agent that ingests a new source into an LLM Wiki vault. Reads the source, proposes TL;DR and key claims, identifies which entity/concept/synthesi
- [markdown-html-orchestrator](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__markdown-html-orchestrator): Use when a user wants to convert any markdown file in their Claude project into a single-file, lightly-interactive HTML - long-form documents (specs, plans, RFC
- [Startup CTO](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__startup-cto): Technical co-founder who's been through two startups and learned what actually matters. Makes architecture decisions, selects tech stacks, builds engineering cu
- [stress-test](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__stress-test): /em:stress-test - Business assumption stress testing. Use before betting on a plan whose core assumptions are unvalidated - e.g. stress-testing 'enterprise buye
- [wiki-lint](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__wiki-lint): Run a health check on the LLM Wiki vault - mechanical checks (orphans, broken links, stale pages, missing frontmatter, log gap, duplicates) plus semantic checks
- [caio-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__caio-review): /cs:caio-review - Eval-demanding Chief AI Officer interrogation of any plan that involves AI: model selection, risk classification, cost economics, or AI hiring
- [/operator-audit](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__operator-audit): Run the full Kubernetes Operator audit (CRD + reconcile + capability) on the current repo
- [prd](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__prd): Gated PRD generation - interrogates problem, user, and metric before drafting; refuses to draft on unknowns. Usage: /prd
- [regulatory-affairs-head](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__regulatory-affairs-head): Senior Regulatory Affairs Manager for HealthTech and MedTech companies. Prepares FDA 510(k), De Novo, and PMA submission packages; analyzes regulatory pathways 
- [context-engine](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__context-engine): Loads and manages company context for all C-suite advisor skills. Reads ~/.claude/company-context.md, detects stale context (>90 days), enriches context during 
- [/slo-design](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__slo-design): Interactive wizard to design an SLO with SLI, target, error budget, and burn-rate alerts
- [retro](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__retro): Analyze sprint retrospectives for patterns and action item tracking. Usage: /retro analyze
- [board-meeting](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__board-meeting): Multi-agent board meeting protocol for strategic decisions. Runs a structured 6-phase deliberation: context loading, independent C-suite contributions (isolated
- [user-story](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__user-story): Generate user stories with acceptance criteria and sprint planning. Usage: /user-story [options]
- [wiki-log](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__wiki-log): Show recent entries from the LLM Wiki log (wiki/log.md). Uses the standardized ## [YYYY-MM-DD] header format so grep + tail works. Usage /wiki-log [--last N] [-
- [md-slides](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__md-slides): Converts a markdown deck (slides separated by `---` HR boundaries or by `# ` H1 headings, with optional ` ` presenter notes blocks) into a single-file HTML pres
- [founder-coach](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__founder-coach): Personal leadership development for founders and first-time CEOs. Covers founder archetype identification, delegation frameworks, energy management, CEO calenda
- [ciso-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ciso-advisor): Security leadership for growth-stage companies. Risk quantification in dollars, compliance roadmap (SOC 2/ISO 27001/HIPAA/GDPR), security architecture strategy,
- [saas-metrics-coach](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__saas-metrics-coach): SaaS financial health advisor. Use when a user shares revenue or customer numbers, or mentions ARR, MRR, churn, LTV, CAC, NRR, or asks how their SaaS business i
- [/chaos-experiment](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__chaos-experiment): Interactive wizard to design and validate a chaos engineering experiment
- [wiki-query](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__wiki-query): Query the LLM Wiki - reads index.md first, drills into 3-10 relevant pages, synthesizes an answer with inline [[wikilink]] citations, and offers to file the ans
- [risk-management-specialist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__risk-management-specialist): Medical device risk management specialist implementing ISO 14971 throughout product lifecycle. Provides risk analysis, risk evaluation, risk control, and post-p
- [chief-data-officer-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__skills-chief-data-officer-advisor): Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), 
- [isms-audit-expert](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__isms-audit-expert): Information Security Management System (ISMS) audit expert for ISO 27001 compliance verification, security control assessment, and certification support. Use wh
- [cs-financial-analyst](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-financial-analyst): Financial Analyst agent for DCF valuation, financial modeling, budgeting, forecasting, and SaaS metrics (ARR, MRR, churn, CAC, LTV, NRR). Orchestrates finance s
- [finance-skills](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__finance-skills): Router/index for the 2 finance skills bundled in this plugin: financial-analyst (ratio analysis, DCF valuation, budget variance, rolling forecasts) and saas-met
- [cs-ceo-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-ceo-advisor): Strategic leadership advisor for CEOs covering vision, strategy, board management, investor relations, and organizational culture. Use when a founder or CEO fac
- [okr](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__okr): Generate OKR cascades from company strategy to team objectives. Usage: /okr generate
- [cs-aeo](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-aeo): Answer Engine Optimization (AEO) specialist agent. Use when content needs to be optimized for citation by AI language models (ChatGPT, Perplexity, Claude, Gemin
- [qms-audit-expert](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__qms-audit-expert): ISO 13485 internal audit expertise for medical device QMS. Covers audit planning, execution, nonconformity classification, and CAPA verification. Use when plann
- [agile-product-owner](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__agile-product-owner): Agile product ownership for backlog management and sprint execution. Covers user story writing, acceptance criteria, sprint planning, and velocity tracking. Use
- [partnerships-architect](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__partnerships-architect): Use when a startup is approached by a prospective partner and someone has to decide should we sign this partner, at what partner tier (referral / reseller / OEM
- [iso42001-specialist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__iso42001-specialist): ISO/IEC 42001:2023 AI Management System (AIMS) specialist for compliance teams running internal audits. Three decisions: (1) Where are the gaps against Clauses 
- [wiki-init](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__wiki-init): Bootstrap a fresh LLM Wiki vault with the three-layer structure, schema files, and starter templates. Usage /wiki-init --topic " " [--tool all|claude-code|codex
- [persona](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__persona): Generate data-driven user personas for UX research and product design. Usage: /persona generate [options]
- [cto-review](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cto-review): /cs:cto-review - Architecture and scaling interrogation. Tech debt, scaling cliffs, team scaling, build-vs-buy. Use when committing to an architecture, planning
- [product-analytics](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__product-analytics): Use when defining product KPIs, building metric dashboards, running cohort or retention analysis, or interpreting feature adoption trends across product stages.
- [capa-officer](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__capa-officer): CAPA system management for medical device QMS. Covers root cause analysis, corrective action planning, effectiveness verification, and CAPA metrics. Use when ru
- [cro-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cro-advisor): Revenue leadership for B2B SaaS companies. Revenue forecasting, sales model design, pricing strategy, net revenue retention, and sales team scaling. Use when de
- [cs-product-strategist](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cs-product-strategist): Product strategy agent for quarterly OKR planning, competitive landscape analysis, product vision development, and strategy pivot evaluation. Use when the quest
- [intl-expansion](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__intl-expansion): International market expansion strategy. Market selection, entry modes, localization, regulatory compliance, and go-to-market by region. Use when expanding to n
- [sprint-plan](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__sprint-plan): Capacity-gated sprint planning - runs capacity math, carry-over check, and a definition-of-ready gate before committing scope. Usage: /sprint-plan [capacity]
- [design-system](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__design-system): Captures the user's brand identity once via a 10-question onboarding wizard (primary/accent HEX + heading + body Google Fonts + design style editorial/technical
- [quality-documentation-manager](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__quality-documentation-manager): Document control system management for medical device QMS. Covers document numbering, version control, change management, and 21 CFR Part 11 compliance. Use whe
- [ceo-advisor](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__ceo-advisor): Executive leadership guidance for strategic decision-making, organizational development, and stakeholder management. Use when planning strategy, preparing board
- [commercial-skills](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__commercial-skills): Use when reviewing, approving, or designing commercial motion - pricing models, deal review, discount approval, partnership economics, channel mix, commercial p
- [research-finance](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__research-finance): Use when managing the money for an internal R&D program or portfolio - building a multi-period program budget with the F&A (indirect) split, tracking burn rate 
- [code-to-prd](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__cmd-code-to-prd): Reverse-engineer a frontend codebase into a PRD. Usage: /code-to-prd [path]
- [c-level-skills](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__c-level-skills): Index and router for the C-level advisory bundle: 33 skills covering 14 C-suite roles, orchestration, cross-cutting capabilities, and culture. Use when explorin
- [Solo Founder](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__solo-founder): Your co-founder who doesn't exist yet. Covers product, engineering, marketing, and strategy for one-person startups - because nobody's stopping you from making 
- [landing](https://doutorai.pt/en/components/skills__alirezarezvani-claude-skills__landing): Generates a premium single-page HTML landing page with 3D CSS animations, GSAP scroll effects, and mouse-parallax depth. Forcing intake (product + elevator pitc
- [invoice-organizer](https://doutorai.pt/en/components/skills__composio-awesome__invoice-organizer): Automatically organizes invoices and receipts for tax preparation by reading messy files, extracting key information, renaming them consistently, and sorting th
- [changelog-generator](https://doutorai.pt/en/components/skills__composio-awesome__changelog-generator): Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, 
- [image-enhancer](https://doutorai.pt/en/components/skills__composio-awesome__image-enhancer): Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documen
- [twitter-algorithm-optimizer](https://doutorai.pt/en/components/skills__composio-awesome__twitter-algorithm-optimizer): Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility 
- [competitive-ads-extractor](https://doutorai.pt/en/components/skills__composio-awesome__competitive-ads-extractor): Extracts and analyzes competitors' ads from ad libraries (Facebook, LinkedIn, etc.) to understand what messaging, problems, and creative approaches are working.
- [artifacts-builder](https://doutorai.pt/en/components/skills__composio-awesome__artifacts-builder): Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for
- [meeting-insights-analyzer](https://doutorai.pt/en/components/skills__composio-awesome__meeting-insights-analyzer): Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict,
- [skill-share](https://doutorai.pt/en/components/skills__composio-awesome__skill-share): A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill discovery.
- [developer-growth-analysis](https://doutorai.pt/en/components/skills__composio-awesome__developer-growth-analysis): Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from
- [mcp-builder](https://doutorai.pt/en/components/skills__composio-awesome__mcp-builder): Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when b
- [skill-creator](https://doutorai.pt/en/components/skills__composio-awesome__skill-creator): Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabil
- [content-research-writer](https://doutorai.pt/en/components/skills__composio-awesome__content-research-writer): Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on ea
- [file-organizer](https://doutorai.pt/en/components/skills__composio-awesome__file-organizer): Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating c
- [raffle-winner-picker](https://doutorai.pt/en/components/skills__composio-awesome__raffle-winner-picker): Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
- [connect-apps](https://doutorai.pt/en/components/skills__composio-awesome__connect-apps): Connect Claude to external apps like Gmail, Slack, GitHub. Use this skill when the user wants to send emails, create issues, post messages, or take actions in e
- [langsmith-fetch](https://doutorai.pt/en/components/skills__composio-awesome__langsmith-fetch): Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool
- [youtube-downloader](https://doutorai.pt/en/components/skills__composio-awesome__video-downloader): Download YouTube videos with customizable quality and format options. Use this skill when the user asks to download, save, or grab YouTube videos. Supports vari
- [domain-name-brainstormer](https://doutorai.pt/en/components/skills__composio-awesome__domain-name-brainstormer): Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming an
- [tailored-resume-generator](https://doutorai.pt/en/components/skills__composio-awesome__tailored-resume-generator): Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
- [connect](https://doutorai.pt/en/components/skills__composio-awesome__connect): Connect Claude to any app. Send emails, create issues, post messages, update databases - take real actions across Gmail, Slack, GitHub, Notion, and 1000+ servic
- [lead-research-assistant](https://doutorai.pt/en/components/skills__composio-awesome__lead-research-assistant): Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategie
- [slack-gif-creator](https://doutorai.pt/en/components/skills__composio-awesome__slack-gif-creator): Toolkit for creating animated GIFs optimized for Slack, with validators for size constraints and composable animation primitives. This skill applies when users 
- [Coinbase Automation](https://doutorai.pt/en/components/skills__composio-awesome__coinbase-automation): Coinbase Automation: list and manage cryptocurrency wallets, accounts, and portfolio data via Coinbase CDP SDK
- [procfu-automation](https://doutorai.pt/en/components/skills__composio-awesome__procfu-automation): Automate Procfu tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sensibo-automation](https://doutorai.pt/en/components/skills__composio-awesome__sensibo-automation): Automate Sensibo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [idea-scale-automation](https://doutorai.pt/en/components/skills__composio-awesome__idea-scale-automation): Automate Idea Scale tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [doppler-secretops-automation](https://doutorai.pt/en/components/skills__composio-awesome__doppler-secretops-automation): Automate Doppler Secretops tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [chatbotkit-automation](https://doutorai.pt/en/components/skills__composio-awesome__chatbotkit-automation): Automate Chatbotkit tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [agenty-automation](https://doutorai.pt/en/components/skills__composio-awesome__agenty-automation): Automate Agenty tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [datagma-automation](https://doutorai.pt/en/components/skills__composio-awesome__datagma-automation): Automate Datagma tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [hookdeck-automation](https://doutorai.pt/en/components/skills__composio-awesome__hookdeck-automation): Automate Hookdeck tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [nango-automation](https://doutorai.pt/en/components/skills__composio-awesome__nango-automation): Automate Nango tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [shorten-rest-automation](https://doutorai.pt/en/components/skills__composio-awesome__shorten-rest-automation): Automate Shorten Rest tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mistral_ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__mistral-ai-automation): Automate Mistral AI tasks via Rube MCP (Composio): completions, embeddings, fine-tuning, and model management. Always search tools first for current schemas.
- [remove-bg-automation](https://doutorai.pt/en/components/skills__composio-awesome__remove-bg-automation): Automate Remove Bg tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [toneden-automation](https://doutorai.pt/en/components/skills__composio-awesome__toneden-automation): Automate Toneden tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [SEMrush Automation](https://doutorai.pt/en/components/skills__composio-awesome__semrush-automation): Automate SEO analysis with SEMrush -- research keywords, analyze domain organic rankings, audit backlinks, assess keyword difficulty, and discover related terms
- [browserhub-automation](https://doutorai.pt/en/components/skills__composio-awesome__browserhub-automation): Automate Browserhub tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [gatherup-automation](https://doutorai.pt/en/components/skills__composio-awesome__gatherup-automation): Automate Gatherup tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [demio-automation](https://doutorai.pt/en/components/skills__composio-awesome__demio-automation): Automate Demio tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cincopa-automation](https://doutorai.pt/en/components/skills__composio-awesome__cincopa-automation): Automate Cincopa tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ticketmaster-automation](https://doutorai.pt/en/components/skills__composio-awesome__ticketmaster-automation): Automate Ticketmaster tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [big-data-cloud-automation](https://doutorai.pt/en/components/skills__composio-awesome__big-data-cloud-automation): Automate Big Data Cloud tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [fidel-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__fidel-api-automation): Automate Fidel API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [QuickBooks Automation](https://doutorai.pt/en/components/skills__composio-awesome__quickbooks-automation): QuickBooks Automation: manage invoices, customers, accounts, and payments in QuickBooks Online for streamlined bookkeeping
- [moz-automation](https://doutorai.pt/en/components/skills__composio-awesome__moz-automation): Automate Moz tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ring_central-automation](https://doutorai.pt/en/components/skills__composio-awesome__ring-central-automation): Automate RingCentral tasks via Rube MCP (Composio): calls, messages, meetings, and unified communications. Always search tools first for current schemas.
- [exist-automation](https://doutorai.pt/en/components/skills__composio-awesome__exist-automation): Automate Exist tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [junglescout-automation](https://doutorai.pt/en/components/skills__composio-awesome__junglescout-automation): Automate Junglescout tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [coinranking-automation](https://doutorai.pt/en/components/skills__composio-awesome__coinranking-automation): Automate Coinranking tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [parma-automation](https://doutorai.pt/en/components/skills__composio-awesome__parma-automation): Automate Parma tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mopinion-automation](https://doutorai.pt/en/components/skills__composio-awesome__mopinion-automation): Automate Mopinion tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sympla-automation](https://doutorai.pt/en/components/skills__composio-awesome__sympla-automation): Automate Sympla tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [faceup-automation](https://doutorai.pt/en/components/skills__composio-awesome__faceup-automation): Automate Faceup tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [pdfless-automation](https://doutorai.pt/en/components/skills__composio-awesome__pdfless-automation): Automate Pdfless tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [discordbot-automation](https://doutorai.pt/en/components/skills__composio-awesome__discordbot-automation): Automate Discordbot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ravenseotools-automation](https://doutorai.pt/en/components/skills__composio-awesome__ravenseotools-automation): Automate Ravenseotools tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [docupilot-automation](https://doutorai.pt/en/components/skills__composio-awesome__docupilot-automation): Automate Docupilot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Ahrefs Automation](https://doutorai.pt/en/components/skills__composio-awesome__ahrefs-automation): Automate SEO research with Ahrefs -- analyze backlink profiles, research keywords, track domain metrics history, audit organic rankings, and perform batch URL a
- [sslmate-cert-spotter-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__sslmate-cert-spotter-api-automation): Automate Sslmate Cert Spotter API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [rocketlane-automation](https://doutorai.pt/en/components/skills__composio-awesome__rocketlane-automation): Automate Rocketlane tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [livesession-automation](https://doutorai.pt/en/components/skills__composio-awesome__livesession-automation): Automate Livesession tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [splitwise-automation](https://doutorai.pt/en/components/skills__composio-awesome__splitwise-automation): Automate Splitwise tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [encodian-automation](https://doutorai.pt/en/components/skills__composio-awesome__encodian-automation): Automate Encodian tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [composio-automation](https://doutorai.pt/en/components/skills__composio-awesome__composio-automation): Automate Composio tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [supportivekoala-automation](https://doutorai.pt/en/components/skills__composio-awesome__supportivekoala-automation): Automate Supportivekoala tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [serpdog-automation](https://doutorai.pt/en/components/skills__composio-awesome__serpdog-automation): Automate Serpdog tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [browserbase-tool-automation](https://doutorai.pt/en/components/skills__composio-awesome__browserbase-tool-automation): Automate Browserbase Tool tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bubble-automation](https://doutorai.pt/en/components/skills__composio-awesome__bubble-automation): Automate Bubble tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [moco-automation](https://doutorai.pt/en/components/skills__composio-awesome__moco-automation): Automate Moco tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [raisely-automation](https://doutorai.pt/en/components/skills__composio-awesome__raisely-automation): Automate Raisely tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [onesignal_rest_api-automation](https://doutorai.pt/en/components/skills__composio-awesome__onesignal-rest-api-automation): Automate OneSignal tasks via Rube MCP (Composio): push notifications, segments, templates, and messaging. Always search tools first for current schemas.
- [files-com-automation](https://doutorai.pt/en/components/skills__composio-awesome__files-com-automation): Automate Files Com tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [borneo-automation](https://doutorai.pt/en/components/skills__composio-awesome__borneo-automation): Automate Borneo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [hypeauditor-automation](https://doutorai.pt/en/components/skills__composio-awesome__hypeauditor-automation): Automate Hypeauditor tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [aeroleads-automation](https://doutorai.pt/en/components/skills__composio-awesome__aeroleads-automation): Automate Aeroleads tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [diffbot-automation](https://doutorai.pt/en/components/skills__composio-awesome__diffbot-automation): Automate Diffbot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [metaphor-automation](https://doutorai.pt/en/components/skills__composio-awesome__metaphor-automation): Automate Metaphor tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [beeminder-automation](https://doutorai.pt/en/components/skills__composio-awesome__beeminder-automation): Automate Beeminder tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [shortpixel-automation](https://doutorai.pt/en/components/skills__composio-awesome__shortpixel-automation): Automate Shortpixel tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [appsflyer-automation](https://doutorai.pt/en/components/skills__composio-awesome__appsflyer-automation): Automate Appsflyer tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [wachete-automation](https://doutorai.pt/en/components/skills__composio-awesome__wachete-automation): Automate Wachete tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zoho_mail-automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-mail-automation): Automate Zoho Mail tasks via Rube MCP (Composio): email sending, folders, labels, and mailbox management. Always search tools first for current schemas.
- [waboxapp-automation](https://doutorai.pt/en/components/skills__composio-awesome__waboxapp-automation): Automate Waboxapp tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ocrspace-automation](https://doutorai.pt/en/components/skills__composio-awesome__ocrspace-automation): Automate Ocrspace tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [textcortex-automation](https://doutorai.pt/en/components/skills__composio-awesome__textcortex-automation): Automate Textcortex tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [google_search_console-automation](https://doutorai.pt/en/components/skills__composio-awesome__google-search-console-automation): Automate Google Search Console tasks via Rube MCP (Composio): search performance, URL inspection, sitemaps, and indexing status. Always search tools first for c
- [sendfox-automation](https://doutorai.pt/en/components/skills__composio-awesome__sendfox-automation): Automate Sendfox tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [printautopilot-automation](https://doutorai.pt/en/components/skills__composio-awesome__printautopilot-automation): Automate Printautopilot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [btcpay-server-automation](https://doutorai.pt/en/components/skills__composio-awesome__btcpay-server-automation): Automate Btcpay Server tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [brandfetch-automation](https://doutorai.pt/en/components/skills__composio-awesome__brandfetch-automation): Automate Brandfetch tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [reply-automation](https://doutorai.pt/en/components/skills__composio-awesome__reply-automation): Automate Reply tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [documint-automation](https://doutorai.pt/en/components/skills__composio-awesome__documint-automation): Automate Documint tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [GroqCloud Automation](https://doutorai.pt/en/components/skills__composio-awesome__groqcloud-automation): Automate AI inference, chat completions, audio translation, and TTS voice management through GroqCloud's high-performance API via Composio
- [goodbits-automation](https://doutorai.pt/en/components/skills__composio-awesome__goodbits-automation): Automate Goodbits tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [landbot-automation](https://doutorai.pt/en/components/skills__composio-awesome__landbot-automation): Automate Landbot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [codereadr-automation](https://doutorai.pt/en/components/skills__composio-awesome__codereadr-automation): Automate Codereadr tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [docnify-automation](https://doutorai.pt/en/components/skills__composio-awesome__docnify-automation): Automate Docnify tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [classmarker-automation](https://doutorai.pt/en/components/skills__composio-awesome__classmarker-automation): Automate Classmarker tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [beamer-automation](https://doutorai.pt/en/components/skills__composio-awesome__beamer-automation): Automate Beamer tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [getform-automation](https://doutorai.pt/en/components/skills__composio-awesome__getform-automation): Automate Getform tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ipinfo-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__ipinfo-io-automation): Automate Ipinfo IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [poptin-automation](https://doutorai.pt/en/components/skills__composio-awesome__poptin-automation): Automate Poptin tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Apollo Automation](https://doutorai.pt/en/components/skills__composio-awesome__apollo-automation): Automate Apollo.io lead generation -- search organizations, discover contacts, enrich prospect data, manage contact stages, and build targeted outreach lists --
- [dotsimple-automation](https://doutorai.pt/en/components/skills__composio-awesome__dotsimple-automation): Automate Dotsimple tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [yandex-automation](https://doutorai.pt/en/components/skills__composio-awesome__yandex-automation): Automate Yandex tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [elorus-automation](https://doutorai.pt/en/components/skills__composio-awesome__elorus-automation): Automate Elorus tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [reply-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__reply-io-automation): Automate Reply IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [carbone-automation](https://doutorai.pt/en/components/skills__composio-awesome__carbone-automation): Automate Carbone tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Hunter Automation](https://doutorai.pt/en/components/skills__composio-awesome__hunter-automation): Automate Hunter.io email intelligence -- search domains for email addresses, find specific contacts, verify email deliverability, manage leads, and monitor acco
- [perigon-automation](https://doutorai.pt/en/components/skills__composio-awesome__perigon-automation): Automate Perigon tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bidsketch-automation](https://doutorai.pt/en/components/skills__composio-awesome__bidsketch-automation): Automate Bidsketch tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zoho_desk-automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-desk-automation): Automate Zoho Desk tasks via Rube MCP (Composio): tickets, contacts, agents, departments, and help desk operations. Always search tools first for current schema
- [conversion-tools-automation](https://doutorai.pt/en/components/skills__composio-awesome__conversion-tools-automation): Automate Conversion Tools tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [piggy-automation](https://doutorai.pt/en/components/skills__composio-awesome__piggy-automation): Automate Piggy tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [altoviz-automation](https://doutorai.pt/en/components/skills__composio-awesome__altoviz-automation): Automate Altoviz tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [more-trees-automation](https://doutorai.pt/en/components/skills__composio-awesome__more-trees-automation): Automate More Trees tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [recallai-automation](https://doutorai.pt/en/components/skills__composio-awesome__recallai-automation): Automate Recallai tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [felt-automation](https://doutorai.pt/en/components/skills__composio-awesome__felt-automation): Automate Felt tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [kit-automation](https://doutorai.pt/en/components/skills__composio-awesome__kit-automation): Automate Kit tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zoho_bigin-automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-bigin-automation): Automate Zoho Bigin tasks via Rube MCP (Composio): pipelines, contacts, companies, products, and small business CRM. Always search tools first for current schem
- [folk-automation](https://doutorai.pt/en/components/skills__composio-awesome__folk-automation): Automate Folk tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [membervault-automation](https://doutorai.pt/en/components/skills__composio-awesome__membervault-automation): Automate Membervault tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [abstract-automation](https://doutorai.pt/en/components/skills__composio-awesome__abstract-automation): Automate Abstract tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [taxjar-automation](https://doutorai.pt/en/components/skills__composio-awesome__taxjar-automation): Automate Taxjar tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [desktime-automation](https://doutorai.pt/en/components/skills__composio-awesome__desktime-automation): Automate Desktime tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [screenshot-fyi-automation](https://doutorai.pt/en/components/skills__composio-awesome__screenshot-fyi-automation): Automate Screenshot Fyi tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [countdown-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__countdown-api-automation): Automate Countdown API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [spoki-automation](https://doutorai.pt/en/components/skills__composio-awesome__spoki-automation): Automate Spoki tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [google_admin-automation](https://doutorai.pt/en/components/skills__composio-awesome__google-admin-automation): Automate Google Admin tasks via Rube MCP (Composio): user management, org units, groups, and domain administration. Always search tools first for current schema
- [cloudpress-automation](https://doutorai.pt/en/components/skills__composio-awesome__cloudpress-automation): Automate Cloudpress tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [leadfeeder-automation](https://doutorai.pt/en/components/skills__composio-awesome__leadfeeder-automation): Automate Leadfeeder tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [chaser-automation](https://doutorai.pt/en/components/skills__composio-awesome__chaser-automation): Automate Chaser tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [openrouter-automation](https://doutorai.pt/en/components/skills__composio-awesome__openrouter-automation): Automate Openrouter tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [lemon_squeezy-automation](https://doutorai.pt/en/components/skills__composio-awesome__lemon-squeezy-automation): Automate Lemon Squeezy tasks via Rube MCP (Composio): products, orders, subscriptions, checkouts, and digital sales. Always search tools first for current schem
- [bolt-iot-automation](https://doutorai.pt/en/components/skills__composio-awesome__bolt-iot-automation): Automate Bolt Iot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [memberspot-automation](https://doutorai.pt/en/components/skills__composio-awesome__memberspot-automation): Automate Memberspot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Lemlist Automation](https://doutorai.pt/en/components/skills__composio-awesome__lemlist-automation): Automate Lemlist multichannel outreach -- manage campaigns, enroll leads, add personalization variables, export campaign data, and handle unsubscribes via the C
- [api2pdf-automation](https://doutorai.pt/en/components/skills__composio-awesome__api2pdf-automation): Automate Api2pdf tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zenrows-automation](https://doutorai.pt/en/components/skills__composio-awesome__zenrows-automation): Automate Zenrows tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Excel Automation](https://doutorai.pt/en/components/skills__composio-awesome__excel-automation): Excel Automation: create workbooks, manage worksheets, read/write cell data, and format spreadsheets via Microsoft Excel and Google Sheets integration
- [ynab-automation](https://doutorai.pt/en/components/skills__composio-awesome__ynab-automation): Automate Ynab tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [peopledatalabs-automation](https://doutorai.pt/en/components/skills__composio-awesome__peopledatalabs-automation): Automate Peopledatalabs tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Toggl Automation](https://doutorai.pt/en/components/skills__composio-awesome__toggl-automation): Automate time tracking workflows in Toggl Track -- create time entries, manage projects, clients, tags, and workspaces through natural language commands.
- [fitbit-automation](https://doutorai.pt/en/components/skills__composio-awesome__fitbit-automation): Automate Fitbit tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [imagekit-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__imagekit-io-automation): Automate Imagekit IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [humanitix-automation](https://doutorai.pt/en/components/skills__composio-awesome__humanitix-automation): Automate Humanitix tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [lmnt-automation](https://doutorai.pt/en/components/skills__composio-awesome__lmnt-automation): Automate Lmnt tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [crustdata-automation](https://doutorai.pt/en/components/skills__composio-awesome__crustdata-automation): Automate Crustdata tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [maintainx-automation](https://doutorai.pt/en/components/skills__composio-awesome__maintainx-automation): Automate Maintainx tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [radar-automation](https://doutorai.pt/en/components/skills__composio-awesome__radar-automation): Automate Radar tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [onepage-automation](https://doutorai.pt/en/components/skills__composio-awesome__onepage-automation): Automate Onepage tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Capsule CRM Automation](https://doutorai.pt/en/components/skills__composio-awesome__capsule-crm-automation): Automate Capsule CRM operations -- manage contacts (parties), run structured filter queries, track tasks and projects, log entries, and handle organizations -- 
- [extracta-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__extracta-ai-automation): Automate Extracta AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [callingly-automation](https://doutorai.pt/en/components/skills__composio-awesome__callingly-automation): Automate Callingly tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [woodpecker-co-automation](https://doutorai.pt/en/components/skills__composio-awesome__woodpecker-co-automation): Automate Woodpecker co tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [boldsign-automation](https://doutorai.pt/en/components/skills__composio-awesome__boldsign-automation): Automate Boldsign tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [brex-staging-automation](https://doutorai.pt/en/components/skills__composio-awesome__brex-staging-automation): Automate Brex Staging tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [docsbot-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__docsbot-ai-automation): Automate Docsbot AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Zoho Books Automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-books-automation): Automate Zoho Books accounting workflows including invoice creation, bill management, contact lookup, payment tracking, and multi-organization support through n
- [Ramp Automation](https://doutorai.pt/en/components/skills__composio-awesome__ramp-automation): Ramp Automation: manage corporate card transactions, reimbursements, users, and expense tracking via the Ramp platform
- [deepgram-automation](https://doutorai.pt/en/components/skills__composio-awesome__deepgram-automation): Automate Deepgram tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ngrok-automation](https://doutorai.pt/en/components/skills__composio-awesome__ngrok-automation): Automate Ngrok tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [calendarhero-automation](https://doutorai.pt/en/components/skills__composio-awesome__calendarhero-automation): Automate Calendarhero tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [buildkite-automation](https://doutorai.pt/en/components/skills__composio-awesome__buildkite-automation): Automate Buildkite tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [pipeline-crm-automation](https://doutorai.pt/en/components/skills__composio-awesome__pipeline-crm-automation): Automate Pipeline CRM tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dovetail-automation](https://doutorai.pt/en/components/skills__composio-awesome__dovetail-automation): Automate Dovetail tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [chatfai-automation](https://doutorai.pt/en/components/skills__composio-awesome__chatfai-automation): Automate Chatfai tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [active-campaign-automation](https://doutorai.pt/en/components/skills__composio-awesome__active-campaign-automation): Automate ActiveCampaign tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [curated-automation](https://doutorai.pt/en/components/skills__composio-awesome__curated-automation): Automate Curated tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [supadata-automation](https://doutorai.pt/en/components/skills__composio-awesome__supadata-automation): Automate Supadata tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [moosend-automation](https://doutorai.pt/en/components/skills__composio-awesome__moosend-automation): Automate Moosend tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [botbaba-automation](https://doutorai.pt/en/components/skills__composio-awesome__botbaba-automation): Automate Botbaba tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [genderapi-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__genderapi-io-automation): Automate Genderapi IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Instantly Automation](https://doutorai.pt/en/components/skills__composio-awesome__instantly-automation): Automate Instantly cold email outreach -- manage campaigns, sending accounts, lead lists, bulk lead imports, and campaign analytics -- using natural language th
- [yousearch-automation](https://doutorai.pt/en/components/skills__composio-awesome__yousearch-automation): Automate Yousearch tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [unione-automation](https://doutorai.pt/en/components/skills__composio-awesome__unione-automation): Automate Unione tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ip2whois-automation](https://doutorai.pt/en/components/skills__composio-awesome__ip2whois-automation): Automate Ip2whois tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [smartproxy-automation](https://doutorai.pt/en/components/skills__composio-awesome__smartproxy-automation): Automate Smartproxy tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [RingCentral Automation](https://doutorai.pt/en/components/skills__composio-awesome__ring-central-automation-c880d7): RingCentral automation via Rube MCP -- toolkit not currently available in Composio; no RING_CENTRAL_ tools found
- [builtwith-automation](https://doutorai.pt/en/components/skills__composio-awesome__builtwith-automation): Automate Builtwith tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bolna-automation](https://doutorai.pt/en/components/skills__composio-awesome__bolna-automation): Automate Bolna tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [many-chat-automation](https://doutorai.pt/en/components/skills__composio-awesome__many-chat-automation): Automate ManyChat tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Facebook Automation](https://doutorai.pt/en/components/skills__composio-awesome__facebook-automation): Automate Facebook Page management including post creation, scheduling, video uploads, Messenger conversations, and audience engagement via Composio
- [route4me-automation](https://doutorai.pt/en/components/skills__composio-awesome__route4me-automation): Automate Route4me tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mapbox-automation](https://doutorai.pt/en/components/skills__composio-awesome__mapbox-automation): Automate Mapbox tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [scrapegraph-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__scrapegraph-ai-automation): Automate Scrapegraph AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [scrapfly-automation](https://doutorai.pt/en/components/skills__composio-awesome__scrapfly-automation): Automate Scrapfly tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [repairshopr-automation](https://doutorai.pt/en/components/skills__composio-awesome__repairshopr-automation): Automate Repairshopr tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ragic-automation](https://doutorai.pt/en/components/skills__composio-awesome__ragic-automation): Automate Ragic tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [finmei-automation](https://doutorai.pt/en/components/skills__composio-awesome__finmei-automation): Automate Finmei tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [statuscake-automation](https://doutorai.pt/en/components/skills__composio-awesome__statuscake-automation): Automate Statuscake tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cdr-platform-automation](https://doutorai.pt/en/components/skills__composio-awesome__cdr-platform-automation): Automate Cdr Platform tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [formcarry-automation](https://doutorai.pt/en/components/skills__composio-awesome__formcarry-automation): Automate Formcarry tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zoho_invoice-automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-invoice-automation): Automate Zoho Invoice tasks via Rube MCP (Composio): invoices, estimates, expenses, clients, and payment tracking. Always search tools first for current schemas
- [virustotal-automation](https://doutorai.pt/en/components/skills__composio-awesome__virustotal-automation): Automate Virustotal tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [optimoroute-automation](https://doutorai.pt/en/components/skills__composio-awesome__optimoroute-automation): Automate Optimoroute tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [googlemeet-automation](https://doutorai.pt/en/components/skills__composio-awesome__googlemeet-automation): Automate Google Meet tasks via Rube MCP (Composio): create Meet spaces, schedule video conferences via Calendar events, manage meeting access. Always search too
- [grist-automation](https://doutorai.pt/en/components/skills__composio-awesome__grist-automation): Automate Grist tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [gamma-automation](https://doutorai.pt/en/components/skills__composio-awesome__gamma-automation): Automate Gamma tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [precoro-automation](https://doutorai.pt/en/components/skills__composio-awesome__precoro-automation): Automate Precoro tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [algolia-automation](https://doutorai.pt/en/components/skills__composio-awesome__algolia-automation): Automate Algolia tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ip2location-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__ip2location-io-automation): Automate Ip2location IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [apiverve-automation](https://doutorai.pt/en/components/skills__composio-awesome__apiverve-automation): Automate Apiverve tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [docupost-automation](https://doutorai.pt/en/components/skills__composio-awesome__docupost-automation): Automate Docupost tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mailersend-automation](https://doutorai.pt/en/components/skills__composio-awesome__mailersend-automation): Automate Mailersend tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [api-sports-automation](https://doutorai.pt/en/components/skills__composio-awesome__api-sports-automation): Automate API Sports tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [resend-automation](https://doutorai.pt/en/components/skills__composio-awesome__resend-automation): Automate Resend tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dropbox-sign-automation](https://doutorai.pt/en/components/skills__composio-awesome__dropbox-sign-automation): Automate Dropbox Sign tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Ashby Automation](https://doutorai.pt/en/components/skills__composio-awesome__ashby-automation): Automate recruiting and hiring workflows in Ashby -- manage candidates, jobs, applications, interviews, and notes through natural language commands.
- [doppler-marketing-automation-automation](https://doutorai.pt/en/components/skills__composio-awesome__doppler-marketing-automation-automation): Automate Doppler Marketing Automation tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [minerstat-automation](https://doutorai.pt/en/components/skills__composio-awesome__minerstat-automation): Automate Minerstat tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ip2proxy-automation](https://doutorai.pt/en/components/skills__composio-awesome__ip2proxy-automation): Automate Ip2proxy tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [humanloop-automation](https://doutorai.pt/en/components/skills__composio-awesome__humanloop-automation): Automate Humanloop tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [google_classroom-automation](https://doutorai.pt/en/components/skills__composio-awesome__google-classroom-automation): Automate Google Classroom tasks via Rube MCP (Composio): course management, assignments, student rosters, and announcements. Always search tools first for curre
- [zenserp-automation](https://doutorai.pt/en/components/skills__composio-awesome__zenserp-automation): Automate Zenserp tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [pdf-api-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__pdf-api-io-automation): Automate PDF API IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [appcircle-automation](https://doutorai.pt/en/components/skills__composio-awesome__appcircle-automation): Automate Appcircle tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [process-street-automation](https://doutorai.pt/en/components/skills__composio-awesome__process-street-automation): Automate Process Street tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ignisign-automation](https://doutorai.pt/en/components/skills__composio-awesome__ignisign-automation): Automate Ignisign tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [open-sea-automation](https://doutorai.pt/en/components/skills__composio-awesome__open-sea-automation): Automate Open Sea tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [apiflash-automation](https://doutorai.pt/en/components/skills__composio-awesome__apiflash-automation): Automate Apiflash tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mailsoftly-automation](https://doutorai.pt/en/components/skills__composio-awesome__mailsoftly-automation): Automate Mailsoftly tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [waiverfile-automation](https://doutorai.pt/en/components/skills__composio-awesome__waiverfile-automation): Automate Waiverfile tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [piloterr-automation](https://doutorai.pt/en/components/skills__composio-awesome__piloterr-automation): Automate Piloterr tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [findymail-automation](https://doutorai.pt/en/components/skills__composio-awesome__findymail-automation): Automate Findymail tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mailboxlayer-automation](https://doutorai.pt/en/components/skills__composio-awesome__mailboxlayer-automation): Automate Mailboxlayer tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [convolo-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__convolo-ai-automation): Automate Convolo AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [anchor-browser-automation](https://doutorai.pt/en/components/skills__composio-awesome__anchor-browser-automation): Automate Anchor Browser tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [payhip-automation](https://doutorai.pt/en/components/skills__composio-awesome__payhip-automation): Automate Payhip tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [persistiq-automation](https://doutorai.pt/en/components/skills__composio-awesome__persistiq-automation): Automate Persistiq tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [honeybadger-automation](https://doutorai.pt/en/components/skills__composio-awesome__honeybadger-automation): Automate Honeybadger tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [timely-automation](https://doutorai.pt/en/components/skills__composio-awesome__timely-automation): Automate Timely tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [wakatime-automation](https://doutorai.pt/en/components/skills__composio-awesome__wakatime-automation): Automate Wakatime tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [npm-automation](https://doutorai.pt/en/components/skills__composio-awesome__npm-automation): Automate NPM tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [atlassian-automation](https://doutorai.pt/en/components/skills__composio-awesome__atlassian-automation): Automate Atlassian tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [proofly-automation](https://doutorai.pt/en/components/skills__composio-awesome__proofly-automation): Automate Proofly tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [browserless-automation](https://doutorai.pt/en/components/skills__composio-awesome__browserless-automation): Automate Browserless tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [tripadvisor-content-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__tripadvisor-content-api-automation): Automate TripAdvisor tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [google-maps-automation](https://doutorai.pt/en/components/skills__composio-awesome__google-maps-automation): Automate Google Maps tasks via Rube MCP (Composio): geocode addresses, search places, get directions, compute route matrices, reverse geocode, autocomplete, get
- [visme-automation](https://doutorai.pt/en/components/skills__composio-awesome__visme-automation): Automate Visme tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Gorgias Automation](https://doutorai.pt/en/components/skills__composio-awesome__gorgias-automation): Automate e-commerce customer support workflows in Gorgias -- manage tickets, customers, tags, and teams through natural language commands.
- [formdesk-automation](https://doutorai.pt/en/components/skills__composio-awesome__formdesk-automation): Automate Formdesk tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mailcoach-automation](https://doutorai.pt/en/components/skills__composio-awesome__mailcoach-automation): Automate Mailcoach tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [jumpcloud-automation](https://doutorai.pt/en/components/skills__composio-awesome__jumpcloud-automation): Automate Jumpcloud tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [autom-automation](https://doutorai.pt/en/components/skills__composio-awesome__autom-automation): Automate Autom tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [rootly-automation](https://doutorai.pt/en/components/skills__composio-awesome__rootly-automation): Automate Rootly tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [finage-automation](https://doutorai.pt/en/components/skills__composio-awesome__finage-automation): Automate Finage tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [eventee-automation](https://doutorai.pt/en/components/skills__composio-awesome__eventee-automation): Automate Eventee tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [affinda-automation](https://doutorai.pt/en/components/skills__composio-awesome__affinda-automation): Automate Affinda tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [seqera-automation](https://doutorai.pt/en/components/skills__composio-awesome__seqera-automation): Automate Seqera tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [clearout-automation](https://doutorai.pt/en/components/skills__composio-awesome__clearout-automation): Automate Clearout tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [brilliant-directories-automation](https://doutorai.pt/en/components/skills__composio-awesome__brilliant-directories-automation): Automate Brilliant Directories tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [scrapingant-automation](https://doutorai.pt/en/components/skills__composio-awesome__scrapingant-automation): Automate Scrapingant tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [lastpass-automation](https://doutorai.pt/en/components/skills__composio-awesome__lastpass-automation): Automate Lastpass tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ticktick-automation](https://doutorai.pt/en/components/skills__composio-awesome__ticktick-automation): Automate Ticktick tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [thanks-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__thanks-io-automation): Automate Thanks IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [boloforms-automation](https://doutorai.pt/en/components/skills__composio-awesome__boloforms-automation): Automate Boloforms tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Omnisend Automation](https://doutorai.pt/en/components/skills__composio-awesome__omnisend-automation): Automate ecommerce marketing workflows including contact management, bulk operations, and subscriber segmentation through Omnisend via Composio
- [campayn-automation](https://doutorai.pt/en/components/skills__composio-awesome__campayn-automation): Automate Campayn tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [smtp2go-automation](https://doutorai.pt/en/components/skills__composio-awesome__smtp2go-automation): Automate Smtp2go tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [google-cloud-vision-automation](https://doutorai.pt/en/components/skills__composio-awesome__google-cloud-vision-automation): Automate Google Cloud Vision tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [yelp-automation](https://doutorai.pt/en/components/skills__composio-awesome__yelp-automation): Automate Yelp tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [grafbase-automation](https://doutorai.pt/en/components/skills__composio-awesome__grafbase-automation): Automate Grafbase tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [expofp-automation](https://doutorai.pt/en/components/skills__composio-awesome__expofp-automation): Automate Expofp tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [api-bible-automation](https://doutorai.pt/en/components/skills__composio-awesome__api-bible-automation): Automate API Bible tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zerobounce-automation](https://doutorai.pt/en/components/skills__composio-awesome__zerobounce-automation): Automate Zerobounce tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [gosquared-automation](https://doutorai.pt/en/components/skills__composio-awesome__gosquared-automation): Automate Gosquared tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [apilio-automation](https://doutorai.pt/en/components/skills__composio-awesome__apilio-automation): Automate Apilio tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [listclean-automation](https://doutorai.pt/en/components/skills__composio-awesome__listclean-automation): Automate Listclean tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [emaillistverify-automation](https://doutorai.pt/en/components/skills__composio-awesome__emaillistverify-automation): Automate Emaillistverify tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [share_point-automation](https://doutorai.pt/en/components/skills__composio-awesome__share-point-automation): Automate SharePoint tasks via Rube MCP (Composio): document libraries, sites, lists, and content management. Always search tools first for current schemas.
- [highlevel-automation](https://doutorai.pt/en/components/skills__composio-awesome__highlevel-automation): Automate Highlevel tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [seismic-automation](https://doutorai.pt/en/components/skills__composio-awesome__seismic-automation): Automate Seismic tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zoho-invoice-automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-invoice-automation-ec4270): Automate Zoho Invoice tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [formsite-automation](https://doutorai.pt/en/components/skills__composio-awesome__formsite-automation): Automate Formsite tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [respond-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__respond-io-automation): Automate Respond IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [google-address-validation-automation](https://doutorai.pt/en/components/skills__composio-awesome__google-address-validation-automation): Automate Google Address Validation tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bugsnag-automation](https://doutorai.pt/en/components/skills__composio-awesome__bugsnag-automation): Automate Bugsnag tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sendbird-automation](https://doutorai.pt/en/components/skills__composio-awesome__sendbird-automation): Automate Sendbird tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [happy-scribe-automation](https://doutorai.pt/en/components/skills__composio-awesome__happy-scribe-automation): Automate Happy Scribe tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [test-app-automation](https://doutorai.pt/en/components/skills__composio-awesome__test-app-automation): Automate Test App tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [NetSuite Automation](https://doutorai.pt/en/components/skills__composio-awesome__netsuite-automation): NetSuite Automation: manage customers, sales orders, invoices, inventory, and records via Oracle NetSuite ERP with SuiteQL queries
- [zoho_inventory-automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-inventory-automation): Automate Zoho Inventory tasks via Rube MCP (Composio): items, orders, warehouses, shipments, and stock management. Always search tools first for current schemas
- [semanticscholar-automation](https://doutorai.pt/en/components/skills__composio-awesome__semanticscholar-automation): Automate Semanticscholar tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [revolt-automation](https://doutorai.pt/en/components/skills__composio-awesome__revolt-automation): Automate Revolt tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [pdf4me-automation](https://doutorai.pt/en/components/skills__composio-awesome__pdf4me-automation): Automate Pdf4me tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sevdesk-automation](https://doutorai.pt/en/components/skills__composio-awesome__sevdesk-automation): Automate Sevdesk tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [api-ninjas-automation](https://doutorai.pt/en/components/skills__composio-awesome__api-ninjas-automation): Automate API Ninjas tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Spotify Automation](https://doutorai.pt/en/components/skills__composio-awesome__spotify-automation): Automate Spotify workflows including playlist management, music search, playback control, and user profile access via Composio
- [listennotes-automation](https://doutorai.pt/en/components/skills__composio-awesome__listennotes-automation): Automate Listennotes tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [nasdaq-automation](https://doutorai.pt/en/components/skills__composio-awesome__nasdaq-automation): Automate Nasdaq tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [alchemy-automation](https://doutorai.pt/en/components/skills__composio-awesome__alchemy-automation): Automate Alchemy tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [kadoa-automation](https://doutorai.pt/en/components/skills__composio-awesome__kadoa-automation): Automate Kadoa tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [salesmate-automation](https://doutorai.pt/en/components/skills__composio-awesome__salesmate-automation): Automate Salesmate tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [quaderno-automation](https://doutorai.pt/en/components/skills__composio-awesome__quaderno-automation): Automate Quaderno tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [SurveyMonkey Automation](https://doutorai.pt/en/components/skills__composio-awesome__survey-monkey-automation): Automate SurveyMonkey survey creation, response collection, collector management, and survey discovery through natural language commands
- [winston-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__winston-ai-automation): Automate Winston AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [taggun-automation](https://doutorai.pt/en/components/skills__composio-awesome__taggun-automation): Automate Taggun tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mocean-automation](https://doutorai.pt/en/components/skills__composio-awesome__mocean-automation): Automate Mocean tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [fixer-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__fixer-io-automation): Automate Fixer IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [platerecognizer-automation](https://doutorai.pt/en/components/skills__composio-awesome__platerecognizer-automation): Automate Platerecognizer tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Uploadcare Automation](https://doutorai.pt/en/components/skills__composio-awesome__uploadcare-automation): Automate Uploadcare file management including listing, storing, inspecting, downloading, and organizing file groups through natural language commands
- [imgbb-automation](https://doutorai.pt/en/components/skills__composio-awesome__imgbb-automation): Automate Imgbb tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [google-admin-automation](https://doutorai.pt/en/components/skills__composio-awesome__google-admin-automation-576f65): Automate Google Workspace Admin tasks via Rube MCP (Composio): manage users, groups, memberships, suspend accounts, create users, add aliases. Always search too
- [breeze-automation](https://doutorai.pt/en/components/skills__composio-awesome__breeze-automation): Automate Breeze tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [docmosis-automation](https://doutorai.pt/en/components/skills__composio-awesome__docmosis-automation): Automate Docmosis tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ayrshare-automation](https://doutorai.pt/en/components/skills__composio-awesome__ayrshare-automation): Automate Ayrshare tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [digital-ocean-automation](https://doutorai.pt/en/components/skills__composio-awesome__digital-ocean-automation): Automate DigitalOcean tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [tapform-automation](https://doutorai.pt/en/components/skills__composio-awesome__tapform-automation): Automate Tapform tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [MailerLite Automation](https://doutorai.pt/en/components/skills__composio-awesome__mailerlite-automation): Automate email marketing workflows including subscriber management, campaign analytics, group segmentation, and account monitoring through MailerLite via Compos
- [safetyculture-automation](https://doutorai.pt/en/components/skills__composio-awesome__safetyculture-automation): Automate Safetyculture tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dailybot-automation](https://doutorai.pt/en/components/skills__composio-awesome__dailybot-automation): Automate Dailybot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cabinpanda-automation](https://doutorai.pt/en/components/skills__composio-awesome__cabinpanda-automation): Automate Cabinpanda tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [parseur-automation](https://doutorai.pt/en/components/skills__composio-awesome__parseur-automation): Automate Parseur tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [webvizio-automation](https://doutorai.pt/en/components/skills__composio-awesome__webvizio-automation): Automate Webvizio tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [verifiedemail-automation](https://doutorai.pt/en/components/skills__composio-awesome__verifiedemail-automation): Automate Verifiedemail tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [customgpt-automation](https://doutorai.pt/en/components/skills__composio-awesome__customgpt-automation): Automate Customgpt tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [anthropic_administrator-automation](https://doutorai.pt/en/components/skills__composio-awesome__anthropic-administrator-automation): Automate Anthropic Admin tasks via Rube MCP (Composio): API keys, usage, workspaces, and organization management. Always search tools first for current schemas.
- [msg91-automation](https://doutorai.pt/en/components/skills__composio-awesome__msg91-automation): Automate Msg91 tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [hashnode-automation](https://doutorai.pt/en/components/skills__composio-awesome__hashnode-automation): Automate Hashnode tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [gender-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__gender-api-automation): Automate Gender API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [pexels-automation](https://doutorai.pt/en/components/skills__composio-awesome__pexels-automation): Automate Pexels tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [similarweb-digitalrank-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__similarweb-digitalrank-api-automation): Automate SimilarWeb tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [emelia-automation](https://doutorai.pt/en/components/skills__composio-awesome__emelia-automation): Automate Emelia tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bonsai-automation](https://doutorai.pt/en/components/skills__composio-awesome__bonsai-automation): Automate Bonsai tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [boxhero-automation](https://doutorai.pt/en/components/skills__composio-awesome__boxhero-automation): Automate Boxhero tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [plain-automation](https://doutorai.pt/en/components/skills__composio-awesome__plain-automation): Automate Plain tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [superchat-automation](https://doutorai.pt/en/components/skills__composio-awesome__superchat-automation): Automate Superchat tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [factorial-automation](https://doutorai.pt/en/components/skills__composio-awesome__factorial-automation): Automate Factorial tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [convertapi-automation](https://doutorai.pt/en/components/skills__composio-awesome__convertapi-automation): Automate Convertapi tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [synthflow-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__synthflow-ai-automation): Automate Synthflow AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Gumroad Automation](https://doutorai.pt/en/components/skills__composio-awesome__gumroad-automation): Automate Gumroad product management, sales tracking, license verification, and webhook subscriptions using natural language through the Composio MCP integration
- [uptimerobot-automation](https://doutorai.pt/en/components/skills__composio-awesome__uptimerobot-automation): Automate Uptimerobot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [wave_accounting-automation](https://doutorai.pt/en/components/skills__composio-awesome__wave-accounting-automation): Automate Wave Accounting tasks via Rube MCP (Composio): invoices, customers, payments, and small business accounting. Always search tools first for current sche
- [googlesuper-automation](https://doutorai.pt/en/components/skills__composio-awesome__googlesuper-automation): Automate Google Super tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [onedesk-automation](https://doutorai.pt/en/components/skills__composio-awesome__onedesk-automation): Automate Onedesk tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [signaturely-automation](https://doutorai.pt/en/components/skills__composio-awesome__signaturely-automation): Automate Signaturely tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [veo-automation](https://doutorai.pt/en/components/skills__composio-awesome__veo-automation): Automate Veo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zoominfo-automation](https://doutorai.pt/en/components/skills__composio-awesome__zoominfo-automation): Automate Zoominfo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [proxiedmail-automation](https://doutorai.pt/en/components/skills__composio-awesome__proxiedmail-automation): Automate Proxiedmail tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [modelry-automation](https://doutorai.pt/en/components/skills__composio-awesome__modelry-automation): Automate Modelry tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [entelligence-automation](https://doutorai.pt/en/components/skills__composio-awesome__entelligence-automation): Automate Entelligence tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [breezy-hr-automation](https://doutorai.pt/en/components/skills__composio-awesome__breezy-hr-automation): Automate Breezy HR tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [updown-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__updown-io-automation): Automate Updown IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [callerapi-automation](https://doutorai.pt/en/components/skills__composio-awesome__callerapi-automation): Automate Callerapi tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Firecrawl Automation](https://doutorai.pt/en/components/skills__composio-awesome__firecrawl-automation): Automate web crawling and data extraction with Firecrawl -- scrape pages, crawl sites, extract structured data, batch scrape URLs, and map website structures th
- [eversign-automation](https://doutorai.pt/en/components/skills__composio-awesome__eversign-automation): Automate Eversign tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cloudflare-api-key-automation](https://doutorai.pt/en/components/skills__composio-awesome__cloudflare-api-key-automation): Automate Cloudflare API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [helpwise-automation](https://doutorai.pt/en/components/skills__composio-awesome__helpwise-automation): Automate Helpwise tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [fixer-automation](https://doutorai.pt/en/components/skills__composio-awesome__fixer-automation): Automate Fixer tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Jotform Automation](https://doutorai.pt/en/components/skills__composio-awesome__jotform-automation): Automate Jotform form listing, user management, activity history, folder organization, and plan inspection through natural language commands
- [crowdin-automation](https://doutorai.pt/en/components/skills__composio-awesome__crowdin-automation): Automate Crowdin tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ascora-automation](https://doutorai.pt/en/components/skills__composio-awesome__ascora-automation): Automate Ascora tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [fillout_forms-automation](https://doutorai.pt/en/components/skills__composio-awesome__fillout-forms-automation): Automate Fillout tasks via Rube MCP (Composio): forms, submissions, workflows, and form builder. Always search tools first for current schemas.
- [coupa-automation](https://doutorai.pt/en/components/skills__composio-awesome__coupa-automation): Automate Coupa tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [anthropic-administrator-automation](https://doutorai.pt/en/components/skills__composio-awesome__anthropic-administrator-automation-0d8678): Automate Anthropic Admin tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [page-x-automation](https://doutorai.pt/en/components/skills__composio-awesome__page-x-automation): Automate Page X tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [wit-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__wit-ai-automation): Automate Wit AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [fullenrich-automation](https://doutorai.pt/en/components/skills__composio-awesome__fullenrich-automation): Automate Fullenrich tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [flexisign-automation](https://doutorai.pt/en/components/skills__composio-awesome__flexisign-automation): Automate Flexisign tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [foursquare-automation](https://doutorai.pt/en/components/skills__composio-awesome__foursquare-automation): Automate Foursquare tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [certifier-automation](https://doutorai.pt/en/components/skills__composio-awesome__certifier-automation): Automate Certifier tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [linguapop-automation](https://doutorai.pt/en/components/skills__composio-awesome__linguapop-automation): Automate Linguapop tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [triggercmd-automation](https://doutorai.pt/en/components/skills__composio-awesome__triggercmd-automation): Automate Triggercmd tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [screenshotone-automation](https://doutorai.pt/en/components/skills__composio-awesome__screenshotone-automation): Automate Screenshotone tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [habitica-automation](https://doutorai.pt/en/components/skills__composio-awesome__habitica-automation): Automate Habitica tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [typless-automation](https://doutorai.pt/en/components/skills__composio-awesome__typless-automation): Automate Typless tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [v0-automation](https://doutorai.pt/en/components/skills__composio-awesome__v0-automation): Automate V0 tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mem0-automation](https://doutorai.pt/en/components/skills__composio-awesome__mem0-automation): Automate Mem0 tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [teltel-automation](https://doutorai.pt/en/components/skills__composio-awesome__teltel-automation): Automate Teltel tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [short-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__short-io-automation): Automate Short IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bugbug-automation](https://doutorai.pt/en/components/skills__composio-awesome__bugbug-automation): Automate Bugbug tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [baserow-automation](https://doutorai.pt/en/components/skills__composio-awesome__baserow-automation): Automate Baserow tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dreamstudio-automation](https://doutorai.pt/en/components/skills__composio-awesome__dreamstudio-automation): Automate Dreamstudio tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [parsehub-automation](https://doutorai.pt/en/components/skills__composio-awesome__parsehub-automation): Automate Parsehub tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [browser-tool-automation](https://doutorai.pt/en/components/skills__composio-awesome__browser-tool-automation): Automate Browser Tool tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bigpicture-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__bigpicture-io-automation): Automate Bigpicture IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [clickmeeting-automation](https://doutorai.pt/en/components/skills__composio-awesome__clickmeeting-automation): Automate Clickmeeting tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [interzoid-automation](https://doutorai.pt/en/components/skills__composio-awesome__interzoid-automation): Automate Interzoid tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [FreshBooks Automation](https://doutorai.pt/en/components/skills__composio-awesome__freshbooks-automation): FreshBooks Automation: manage businesses, projects, time tracking, and billing in FreshBooks cloud accounting
- [brightpearl-automation](https://doutorai.pt/en/components/skills__composio-awesome__brightpearl-automation): Automate Brightpearl tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [coinmarketcal-automation](https://doutorai.pt/en/components/skills__composio-awesome__coinmarketcal-automation): Automate Coinmarketcal tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zoho_books-automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-books-automation-c0dad0): Automate Zoho Books tasks via Rube MCP (Composio): invoices, expenses, contacts, payments, and accounting. Always search tools first for current schemas.
- [simple-analytics-automation](https://doutorai.pt/en/components/skills__composio-awesome__simple-analytics-automation): Automate Simple Analytics tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [evenium-automation](https://doutorai.pt/en/components/skills__composio-awesome__evenium-automation): Automate Evenium tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [svix-automation](https://doutorai.pt/en/components/skills__composio-awesome__svix-automation): Automate Svix tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [front-automation](https://doutorai.pt/en/components/skills__composio-awesome__front-automation): Automate Front tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [gagelist-automation](https://doutorai.pt/en/components/skills__composio-awesome__gagelist-automation): Automate Gagelist tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [turso-automation](https://doutorai.pt/en/components/skills__composio-awesome__turso-automation): Automate Turso tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mx-technologies-automation](https://doutorai.pt/en/components/skills__composio-awesome__mx-technologies-automation): Automate MX Technologies tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [googledocs-automation](https://doutorai.pt/en/components/skills__composio-awesome__googledocs-automation): Automate Google Docs tasks via Rube MCP (Composio): create, edit, search, export, copy, and update documents. Always search tools first for current schemas.
- [chmeetings-automation](https://doutorai.pt/en/components/skills__composio-awesome__chmeetings-automation): Automate Chmeetings tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Apify Automation](https://doutorai.pt/en/components/skills__composio-awesome__apify-automation): Automate web scraping and data extraction with Apify -- run Actors, manage datasets, create reusable tasks, and retrieve crawl results through the Composio Apif
- [geocodio-automation](https://doutorai.pt/en/components/skills__composio-awesome__geocodio-automation): Automate Geocodio tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cults-automation](https://doutorai.pt/en/components/skills__composio-awesome__cults-automation): Automate Cults tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [accredible-certificates-automation](https://doutorai.pt/en/components/skills__composio-awesome__accredible-certificates-automation): Automate Accredible Certificates tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [teamcamp-automation](https://doutorai.pt/en/components/skills__composio-awesome__teamcamp-automation): Automate Teamcamp tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [twelve-data-automation](https://doutorai.pt/en/components/skills__composio-awesome__twelve-data-automation): Automate Twelve Data tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [text-to-pdf-automation](https://doutorai.pt/en/components/skills__composio-awesome__text-to-pdf-automation): Automate Text To PDF tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [nano-nets-automation](https://doutorai.pt/en/components/skills__composio-awesome__nano-nets-automation): Automate Nano Nets tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dialmycalls-automation](https://doutorai.pt/en/components/skills__composio-awesome__dialmycalls-automation): Automate Dialmycalls tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [echtpost-automation](https://doutorai.pt/en/components/skills__composio-awesome__echtpost-automation): Automate Echtpost tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ai-ml-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__ai-ml-api-automation): Automate AI ML API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [fluxguard-automation](https://doutorai.pt/en/components/skills__composio-awesome__fluxguard-automation): Automate Fluxguard tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cardly-automation](https://doutorai.pt/en/components/skills__composio-awesome__cardly-automation): Automate Cardly tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [aero-workflow-automation](https://doutorai.pt/en/components/skills__composio-awesome__aero-workflow-automation): Automate Aero Workflow tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [jobnimbus-automation](https://doutorai.pt/en/components/skills__composio-awesome__jobnimbus-automation): Automate Jobnimbus tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [byteforms-automation](https://doutorai.pt/en/components/skills__composio-awesome__byteforms-automation): Automate Byteforms tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Shortcut Automation](https://doutorai.pt/en/components/skills__composio-awesome__shortcut-automation): Automate project management workflows in Shortcut -- create stories, manage tasks, track epics, and organize workflows through natural language commands.
- [prisma-automation](https://doutorai.pt/en/components/skills__composio-awesome__prisma-automation): Automate Prisma tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [typefully-automation](https://doutorai.pt/en/components/skills__composio-awesome__typefully-automation): Automate Typefully tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [aryn-automation](https://doutorai.pt/en/components/skills__composio-awesome__aryn-automation): Automate Aryn tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [draftable-automation](https://doutorai.pt/en/components/skills__composio-awesome__draftable-automation): Automate Draftable tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [enginemailer-automation](https://doutorai.pt/en/components/skills__composio-awesome__enginemailer-automation): Automate Enginemailer tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [flutterwave-automation](https://doutorai.pt/en/components/skills__composio-awesome__flutterwave-automation): Automate Flutterwave tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [appointo-automation](https://doutorai.pt/en/components/skills__composio-awesome__appointo-automation): Automate Appointo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [abuselpdb-automation](https://doutorai.pt/en/components/skills__composio-awesome__abuselpdb-automation): Automate Abuselpdb tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Snowflake Automation](https://doutorai.pt/en/components/skills__composio-awesome__snowflake-automation): Automate Snowflake data warehouse operations -- list databases, schemas, and tables, execute SQL statements, and manage data workflows via the Composio MCP inte
- [googlephotos-automation](https://doutorai.pt/en/components/skills__composio-awesome__googlephotos-automation): Automate Google Photos tasks via Rube MCP (Composio): upload media, manage albums, search photos, batch add items, create and update albums. Always search tools
- [adrapid-automation](https://doutorai.pt/en/components/skills__composio-awesome__adrapid-automation): Automate Adrapid tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [textit-automation](https://doutorai.pt/en/components/skills__composio-awesome__textit-automation): Automate Textit tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [stormglass-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__stormglass-io-automation): Automate Stormglass IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [lessonspace-automation](https://doutorai.pt/en/components/skills__composio-awesome__lessonspace-automation): Automate Lessonspace tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cutt-ly-automation](https://doutorai.pt/en/components/skills__composio-awesome__cutt-ly-automation): Automate Cutt Ly tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [moonclerk-automation](https://doutorai.pt/en/components/skills__composio-awesome__moonclerk-automation): Automate Moonclerk tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [openweather-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__openweather-api-automation): Automate Openweather API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [d2lbrightspace-automation](https://doutorai.pt/en/components/skills__composio-awesome__d2lbrightspace-automation): Automate D2lbrightspace tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Microsoft Clarity Automation](https://doutorai.pt/en/components/skills__composio-awesome__microsoft-clarity-automation): Automate user behavior analytics with Microsoft Clarity -- export heatmap data, session metrics, and engagement analytics segmented by browser, device, country,
- [asin-data-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__asin-data-api-automation): Automate Asin Data API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [gladia-automation](https://doutorai.pt/en/components/skills__composio-awesome__gladia-automation): Automate Gladia tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [timekit-automation](https://doutorai.pt/en/components/skills__composio-awesome__timekit-automation): Automate Timekit tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [agencyzoom-automation](https://doutorai.pt/en/components/skills__composio-awesome__agencyzoom-automation): Automate Agencyzoom tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [vero-automation](https://doutorai.pt/en/components/skills__composio-awesome__vero-automation): Automate Vero tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Xero Automation](https://doutorai.pt/en/components/skills__composio-awesome__xero-automation): Xero Automation: manage invoices, contacts, payments, bank transactions, and accounts in Xero for cloud-based bookkeeping
- [booqable-automation](https://doutorai.pt/en/components/skills__composio-awesome__booqable-automation): Automate Booqable tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [securitytrails-automation](https://doutorai.pt/en/components/skills__composio-awesome__securitytrails-automation): Automate Securitytrails tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [givebutter-automation](https://doutorai.pt/en/components/skills__composio-awesome__givebutter-automation): Automate Givebutter tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [leverly-automation](https://doutorai.pt/en/components/skills__composio-awesome__leverly-automation): Automate Leverly tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [wolfram-alpha-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__wolfram-alpha-api-automation): Automate Wolfram Alpha API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Braintree Automation](https://doutorai.pt/en/components/skills__composio-awesome__braintree-automation): Braintree Automation: manage payment processing via Stripe-compatible tools for customers, subscriptions, payment methods, and transactions
- [agent-mail-automation](https://doutorai.pt/en/components/skills__composio-awesome__agent-mail-automation): Automate Agent Mail tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [wiz-automation](https://doutorai.pt/en/components/skills__composio-awesome__wiz-automation): Automate Wiz tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [microsoft_clarity-automation](https://doutorai.pt/en/components/skills__composio-awesome__microsoft-clarity-automation-99bb7c): Automate Microsoft Clarity tasks via Rube MCP (Composio): session recordings, heatmaps, and user behavior analytics. Always search tools first for current schem
- [langbase-automation](https://doutorai.pt/en/components/skills__composio-awesome__langbase-automation): Automate Langbase tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [fireberry-automation](https://doutorai.pt/en/components/skills__composio-awesome__fireberry-automation): Automate Fireberry tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [agility-cms-automation](https://doutorai.pt/en/components/skills__composio-awesome__agility-cms-automation): Automate Agility CMS tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [signpath-automation](https://doutorai.pt/en/components/skills__composio-awesome__signpath-automation): Automate Signpath tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [go-to-webinar-automation](https://doutorai.pt/en/components/skills__composio-awesome__go-to-webinar-automation): Automate GoToWebinar tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dock-certs-automation](https://doutorai.pt/en/components/skills__composio-awesome__dock-certs-automation): Automate Dock Certs tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [algodocs-automation](https://doutorai.pt/en/components/skills__composio-awesome__algodocs-automation): Automate Algodocs tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [workiom-automation](https://doutorai.pt/en/components/skills__composio-awesome__workiom-automation): Automate Workiom tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [godial-automation](https://doutorai.pt/en/components/skills__composio-awesome__godial-automation): Automate Godial tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [wati-automation](https://doutorai.pt/en/components/skills__composio-awesome__wati-automation): Automate Wati tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [globalping-automation](https://doutorai.pt/en/components/skills__composio-awesome__globalping-automation): Automate Globalping tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Harvest Automation](https://doutorai.pt/en/components/skills__composio-awesome__harvest-automation): Automate time tracking, project management, and invoicing workflows in Harvest -- log hours, manage projects, clients, and tasks through natural language comman
- [satismeter-automation](https://doutorai.pt/en/components/skills__composio-awesome__satismeter-automation): Automate Satismeter tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mem-automation](https://doutorai.pt/en/components/skills__composio-awesome__mem-automation): Automate Mem tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [oncehub-automation](https://doutorai.pt/en/components/skills__composio-awesome__oncehub-automation): Automate Oncehub tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [opencage-automation](https://doutorai.pt/en/components/skills__composio-awesome__opencage-automation): Automate Opencage tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [supportbee-automation](https://doutorai.pt/en/components/skills__composio-awesome__supportbee-automation): Automate Supportbee tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [retailed-automation](https://doutorai.pt/en/components/skills__composio-awesome__retailed-automation): Automate Retailed tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [survey_monkey-automation](https://doutorai.pt/en/components/skills__composio-awesome__survey-monkey-automation-bf2742): Automate SurveyMonkey tasks via Rube MCP (Composio): surveys, responses, collectors, and survey analytics. Always search tools first for current schemas.
- [-21risk-automation](https://doutorai.pt/en/components/skills__composio-awesome__21risk-automation): Automate 21risk tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [keap-automation](https://doutorai.pt/en/components/skills__composio-awesome__keap-automation): Automate Keap tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [polygon-automation](https://doutorai.pt/en/components/skills__composio-awesome__polygon-automation): Automate Polygon tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [coinmarketcap-automation](https://doutorai.pt/en/components/skills__composio-awesome__coinmarketcap-automation): Automate Coinmarketcap tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [alttext-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__alttext-ai-automation): Automate Alttext AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sourcegraph-automation](https://doutorai.pt/en/components/skills__composio-awesome__sourcegraph-automation): Automate Sourcegraph tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [vestaboard-automation](https://doutorai.pt/en/components/skills__composio-awesome__vestaboard-automation): Automate Vestaboard tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [spotlightr-automation](https://doutorai.pt/en/components/skills__composio-awesome__spotlightr-automation): Automate Spotlightr tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [webscraping-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__webscraping-ai-automation): Automate Webscraping AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Workday Automation](https://doutorai.pt/en/components/skills__composio-awesome__workday-automation): Automate HR operations in Workday -- manage workers, time off requests, absence balances, and employee data through natural language commands.
- [auth0-automation](https://doutorai.pt/en/components/skills__composio-awesome__auth0-automation): Automate Auth0 tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [salesforce-service-cloud-automation](https://doutorai.pt/en/components/skills__composio-awesome__salesforce-service-cloud-automation): Automate Salesforce Service Cloud tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [keen-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__keen-io-automation): Automate Keen IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [apex27-automation](https://doutorai.pt/en/components/skills__composio-awesome__apex27-automation): Automate Apex27 tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [feathery-automation](https://doutorai.pt/en/components/skills__composio-awesome__feathery-automation): Automate Feathery tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [storeganise-automation](https://doutorai.pt/en/components/skills__composio-awesome__storeganise-automation): Automate Storeganise tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [pdfmonkey-automation](https://doutorai.pt/en/components/skills__composio-awesome__pdfmonkey-automation): Automate Pdfmonkey tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ocr-web-service-automation](https://doutorai.pt/en/components/skills__composio-awesome__ocr-web-service-automation): Automate OCR Web Service tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [-2chat-automation](https://doutorai.pt/en/components/skills__composio-awesome__2chat-automation): Automate 2chat tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [currents-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__currents-api-automation): Automate Currents API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [simplesat-automation](https://doutorai.pt/en/components/skills__composio-awesome__simplesat-automation): Automate Simplesat tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sendspark-automation](https://doutorai.pt/en/components/skills__composio-awesome__sendspark-automation): Automate Sendspark tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [centralstationcrm-automation](https://doutorai.pt/en/components/skills__composio-awesome__centralstationcrm-automation): Automate Centralstationcrm tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sms-alert-automation](https://doutorai.pt/en/components/skills__composio-awesome__sms-alert-automation): Automate SMS Alert tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [apaleo-automation](https://doutorai.pt/en/components/skills__composio-awesome__apaleo-automation): Automate Apaleo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [helloleads-automation](https://doutorai.pt/en/components/skills__composio-awesome__helloleads-automation): Automate Helloleads tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [rkvst-automation](https://doutorai.pt/en/components/skills__composio-awesome__rkvst-automation): Automate Rkvst tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [token-metrics-automation](https://doutorai.pt/en/components/skills__composio-awesome__token-metrics-automation): Automate Token Metrics tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [heyzine-automation](https://doutorai.pt/en/components/skills__composio-awesome__heyzine-automation): Automate Heyzine tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [stannp-automation](https://doutorai.pt/en/components/skills__composio-awesome__stannp-automation): Automate Stannp tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [castingwords-automation](https://doutorai.pt/en/components/skills__composio-awesome__castingwords-automation): Automate Castingwords tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [launch_darkly-automation](https://doutorai.pt/en/components/skills__composio-awesome__launch-darkly-automation): Automate LaunchDarkly tasks via Rube MCP (Composio): feature flags, environments, segments, and rollout management. Always search tools first for current schema
- [dialpad-automation](https://doutorai.pt/en/components/skills__composio-awesome__dialpad-automation): Automate Dialpad tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [aivoov-automation](https://doutorai.pt/en/components/skills__composio-awesome__aivoov-automation): Automate Aivoov tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [onesignal-user-auth-automation](https://doutorai.pt/en/components/skills__composio-awesome__onesignal-user-auth-automation): Automate Onesignal User Auth tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [paradym-automation](https://doutorai.pt/en/components/skills__composio-awesome__paradym-automation): Automate Paradym tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [strava-automation](https://doutorai.pt/en/components/skills__composio-awesome__strava-automation): Automate Strava tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [firmao-automation](https://doutorai.pt/en/components/skills__composio-awesome__firmao-automation): Automate Firmao tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [affinity-automation](https://doutorai.pt/en/components/skills__composio-awesome__affinity-automation): Automate Affinity tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [timelinesai-automation](https://doutorai.pt/en/components/skills__composio-awesome__timelinesai-automation): Automate Timelinesai tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [anonyflow-automation](https://doutorai.pt/en/components/skills__composio-awesome__anonyflow-automation): Automate Anonyflow tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [New Relic Automation](https://doutorai.pt/en/components/skills__composio-awesome__new-relic-automation): Automate New Relic observability workflows -- manage alert policies, notification channels, alert conditions, and monitor applications and browser apps via the 
- [cloudflare-automation](https://doutorai.pt/en/components/skills__composio-awesome__cloudflare-automation): Automate Cloudflare tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sitespeakai-automation](https://doutorai.pt/en/components/skills__composio-awesome__sitespeakai-automation): Automate Sitespeakai tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [PandaDoc Automation](https://doutorai.pt/en/components/skills__composio-awesome__pandadoc-automation): Automate document workflows with PandaDoc -- create documents from files, manage contacts, organize folders, set up webhooks, create templates, and track docume
- [here-automation](https://doutorai.pt/en/components/skills__composio-awesome__here-automation): Automate Here tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [simla-com-automation](https://doutorai.pt/en/components/skills__composio-awesome__simla-com-automation): Automate Simla Com tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [beaconchain-automation](https://doutorai.pt/en/components/skills__composio-awesome__beaconchain-automation): Automate Beaconchain tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [retellai-automation](https://doutorai.pt/en/components/skills__composio-awesome__retellai-automation): Automate Retellai tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [missive-automation](https://doutorai.pt/en/components/skills__composio-awesome__missive-automation): Automate Missive tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sage-automation](https://doutorai.pt/en/components/skills__composio-awesome__sage-automation): Automate Sage tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [amcards-automation](https://doutorai.pt/en/components/skills__composio-awesome__amcards-automation): Automate Amcards tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [identitycheck-automation](https://doutorai.pt/en/components/skills__composio-awesome__identitycheck-automation): Automate Identitycheck tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [geoapify-automation](https://doutorai.pt/en/components/skills__composio-awesome__geoapify-automation): Automate Geoapify tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [owl-protocol-automation](https://doutorai.pt/en/components/skills__composio-awesome__owl-protocol-automation): Automate Owl Protocol tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [appveyor-automation](https://doutorai.pt/en/components/skills__composio-awesome__appveyor-automation): Automate Appveyor tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dadata-ru-automation](https://doutorai.pt/en/components/skills__composio-awesome__dadata-ru-automation): Automate Dadata Ru tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [icypeas-automation](https://doutorai.pt/en/components/skills__composio-awesome__icypeas-automation): Automate Icypeas tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [campaign-cleaner-automation](https://doutorai.pt/en/components/skills__composio-awesome__campaign-cleaner-automation): Automate Campaign Cleaner tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ElevenLabs Automation](https://doutorai.pt/en/components/skills__composio-awesome__elevenlabs-automation): Automate ElevenLabs text-to-speech workflows -- generate speech from text, browse and inspect voices, check subscription limits, list models, stream audio, and 
- [worksnaps-automation](https://doutorai.pt/en/components/skills__composio-awesome__worksnaps-automation): Automate Worksnaps tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [deel-automation](https://doutorai.pt/en/components/skills__composio-awesome__deel-automation): Automate Deel tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [tisane-automation](https://doutorai.pt/en/components/skills__composio-awesome__tisane-automation): Automate Tisane tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [pingdom-automation](https://doutorai.pt/en/components/skills__composio-awesome__pingdom-automation): Automate Pingdom tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mx-toolbox-automation](https://doutorai.pt/en/components/skills__composio-awesome__mx-toolbox-automation): Automate Mx Toolbox tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [giphy-automation](https://doutorai.pt/en/components/skills__composio-awesome__giphy-automation): Automate Giphy tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [moneybird-automation](https://doutorai.pt/en/components/skills__composio-awesome__moneybird-automation): Automate Moneybird tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [parallel-automation](https://doutorai.pt/en/components/skills__composio-awesome__parallel-automation): Automate Parallel tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [helcim-automation](https://doutorai.pt/en/components/skills__composio-awesome__helcim-automation): Automate Helcim tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [geokeo-automation](https://doutorai.pt/en/components/skills__composio-awesome__geokeo-automation): Automate Geokeo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ip2location-automation](https://doutorai.pt/en/components/skills__composio-awesome__ip2location-automation): Automate Ip2location tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [twitch-automation](https://doutorai.pt/en/components/skills__composio-awesome__twitch-automation): Automate Twitch tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [google_maps-automation](https://doutorai.pt/en/components/skills__composio-awesome__google-maps-automation-43b08e): Automate Google Maps tasks via Rube MCP (Composio): geocoding, directions, place search, and distance calculations. Always search tools first for current schema
- [docugenerate-automation](https://doutorai.pt/en/components/skills__composio-awesome__docugenerate-automation): Automate Docugenerate tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [deadline-funnel-automation](https://doutorai.pt/en/components/skills__composio-awesome__deadline-funnel-automation): Automate Deadline Funnel tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bannerbear-automation](https://doutorai.pt/en/components/skills__composio-awesome__bannerbear-automation): Automate Bannerbear tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [passslot-automation](https://doutorai.pt/en/components/skills__composio-awesome__passslot-automation): Automate Passslot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [composio-search-automation](https://doutorai.pt/en/components/skills__composio-awesome__composio-search-automation): Automate Composio Search tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [tpscheck-automation](https://doutorai.pt/en/components/skills__composio-awesome__tpscheck-automation): Automate Tpscheck tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bugherd-automation](https://doutorai.pt/en/components/skills__composio-awesome__bugherd-automation): Automate Bugherd tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [klipfolio-automation](https://doutorai.pt/en/components/skills__composio-awesome__klipfolio-automation): Automate Klipfolio tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [textrazor-automation](https://doutorai.pt/en/components/skills__composio-awesome__textrazor-automation): Automate Textrazor tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [census-bureau-automation](https://doutorai.pt/en/components/skills__composio-awesome__census-bureau-automation): Automate Census Bureau tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [smartrecruiters-automation](https://doutorai.pt/en/components/skills__composio-awesome__smartrecruiters-automation): Automate Smartrecruiters tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ambient-weather-automation](https://doutorai.pt/en/components/skills__composio-awesome__ambient-weather-automation): Automate Ambient Weather tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [talenthr-automation](https://doutorai.pt/en/components/skills__composio-awesome__talenthr-automation): Automate Talenthr tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [espocrm-automation](https://doutorai.pt/en/components/skills__composio-awesome__espocrm-automation): Automate Espocrm tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [customjs-automation](https://doutorai.pt/en/components/skills__composio-awesome__customjs-automation): Automate Customjs tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [goody-automation](https://doutorai.pt/en/components/skills__composio-awesome__goody-automation): Automate Goody tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [parsera-automation](https://doutorai.pt/en/components/skills__composio-awesome__parsera-automation): Automate Parsera tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [capsule_crm-automation](https://doutorai.pt/en/components/skills__composio-awesome__capsule-crm-automation-ed3842): Automate Capsule CRM tasks via Rube MCP (Composio): contacts, opportunities, cases, tasks, and pipeline management. Always search tools first for current schema
- [perplexityai-automation](https://doutorai.pt/en/components/skills__composio-awesome__perplexityai-automation): Automate Perplexityai tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [docsumo-automation](https://doutorai.pt/en/components/skills__composio-awesome__docsumo-automation): Automate Docsumo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [venly-automation](https://doutorai.pt/en/components/skills__composio-awesome__venly-automation): Automate Venly tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [kaleido-automation](https://doutorai.pt/en/components/skills__composio-awesome__kaleido-automation): Automate Kaleido tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Dynamics 365 Automation](https://doutorai.pt/en/components/skills__composio-awesome__dynamics365-automation): Dynamics 365 Automation: manage CRM contacts, accounts, leads, opportunities, sales orders, invoices, and cases via the Dynamics CRM Web API
- [gemini-automation](https://doutorai.pt/en/components/skills__composio-awesome__gemini-automation): Automate Gemini tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [flowiseai-automation](https://doutorai.pt/en/components/skills__composio-awesome__flowiseai-automation): Automate Flowiseai tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [templated-automation](https://doutorai.pt/en/components/skills__composio-awesome__templated-automation): Automate Templated tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [gleap-automation](https://doutorai.pt/en/components/skills__composio-awesome__gleap-automation): Automate Gleap tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [starton-automation](https://doutorai.pt/en/components/skills__composio-awesome__starton-automation): Automate Starton tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [amazon-automation](https://doutorai.pt/en/components/skills__composio-awesome__amazon-automation): Automate Amazon tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [esputnik-automation](https://doutorai.pt/en/components/skills__composio-awesome__esputnik-automation): Automate Esputnik tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sendlane-automation](https://doutorai.pt/en/components/skills__composio-awesome__sendlane-automation): Automate Sendlane tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [gan-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__gan-ai-automation): Automate Gan AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [agentql-automation](https://doutorai.pt/en/components/skills__composio-awesome__agentql-automation): Automate Agentql tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [docker_hub-automation](https://doutorai.pt/en/components/skills__composio-awesome__docker-hub-automation): Automate Docker Hub tasks via Rube MCP (Composio): repositories, images, tags, and container registry management. Always search tools first for current schemas.
- [apipie-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__apipie-ai-automation): Automate Apipie AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [melo-automation](https://doutorai.pt/en/components/skills__composio-awesome__melo-automation): Automate Melo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [blocknative-automation](https://doutorai.pt/en/components/skills__composio-awesome__blocknative-automation): Automate Blocknative tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bouncer-automation](https://doutorai.pt/en/components/skills__composio-awesome__bouncer-automation): Automate Bouncer tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [digicert-automation](https://doutorai.pt/en/components/skills__composio-awesome__digicert-automation): Automate Digicert tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bunnycdn-automation](https://doutorai.pt/en/components/skills__composio-awesome__bunnycdn-automation): Automate Bunnycdn tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [labs64-netlicensing-automation](https://doutorai.pt/en/components/skills__composio-awesome__labs64-netlicensing-automation): Automate Labs64 Netlicensing tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [axonaut-automation](https://doutorai.pt/en/components/skills__composio-awesome__axonaut-automation): Automate Axonaut tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dnsfilter-automation](https://doutorai.pt/en/components/skills__composio-awesome__dnsfilter-automation): Automate Dnsfilter tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [docuseal-automation](https://doutorai.pt/en/components/skills__composio-awesome__docuseal-automation): Automate Docuseal tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Wave Accounting Automation](https://doutorai.pt/en/components/skills__composio-awesome__wave-accounting-automation-59c85d): Wave Accounting toolkit is not currently available as a native integration. No Wave-specific tools were found in the Composio platform. This skill is a placehol
- [gist-automation](https://doutorai.pt/en/components/skills__composio-awesome__gist-automation): Automate Gist tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dungeon-fighter-online-automation](https://doutorai.pt/en/components/skills__composio-awesome__dungeon-fighter-online-automation): Automate Dungeon Fighter Online tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [canvas-automation](https://doutorai.pt/en/components/skills__composio-awesome__canvas-automation): Automate Canvas tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [acculynx-automation](https://doutorai.pt/en/components/skills__composio-awesome__acculynx-automation): Automate Acculynx tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [honeyhive-automation](https://doutorai.pt/en/components/skills__composio-awesome__honeyhive-automation): Automate Honeyhive tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mails-so-automation](https://doutorai.pt/en/components/skills__composio-awesome__mails-so-automation): Automate Mails So tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [tally-automation](https://doutorai.pt/en/components/skills__composio-awesome__tally-automation): Automate Tally tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [genderize-automation](https://doutorai.pt/en/components/skills__composio-awesome__genderize-automation): Automate Genderize tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sendbird-ai-chabot-automation](https://doutorai.pt/en/components/skills__composio-awesome__sendbird-ai-chabot-automation): Automate Sendbird AI Chabot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [l2s-automation](https://doutorai.pt/en/components/skills__composio-awesome__l2s-automation): Automate L2s tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ritekit-automation](https://doutorai.pt/en/components/skills__composio-awesome__ritekit-automation): Automate Ritekit tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zyte-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__zyte-api-automation): Automate Zyte API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [signwell-automation](https://doutorai.pt/en/components/skills__composio-awesome__signwell-automation): Automate Signwell tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [intelliprint-automation](https://doutorai.pt/en/components/skills__composio-awesome__intelliprint-automation): Automate Intelliprint tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [kaggle-automation](https://doutorai.pt/en/components/skills__composio-awesome__kaggle-automation): Automate Kaggle tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bench-automation](https://doutorai.pt/en/components/skills__composio-awesome__bench-automation): Automate Bench tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Eventbrite Automation](https://doutorai.pt/en/components/skills__composio-awesome__eventbrite-automation): Automate Eventbrite event management, attendee tracking, organization discovery, and category browsing through natural language commands
- [nasa-automation](https://doutorai.pt/en/components/skills__composio-awesome__nasa-automation): Automate Nasa tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [amara-automation](https://doutorai.pt/en/components/skills__composio-awesome__amara-automation): Automate Amara tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [neutrino-automation](https://doutorai.pt/en/components/skills__composio-awesome__neutrino-automation): Automate Neutrino tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [college-football-data-automation](https://doutorai.pt/en/components/skills__composio-awesome__college-football-data-automation): Automate College Football Data tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [linkhut-automation](https://doutorai.pt/en/components/skills__composio-awesome__linkhut-automation): Automate Linkhut tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [api-labz-automation](https://doutorai.pt/en/components/skills__composio-awesome__api-labz-automation): Automate API Labz tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [brex-automation](https://doutorai.pt/en/components/skills__composio-awesome__brex-automation): Automate Brex tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [battlenet-automation](https://doutorai.pt/en/components/skills__composio-awesome__battlenet-automation): Automate Battlenet tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [PhantomBuster Automation](https://doutorai.pt/en/components/skills__composio-awesome__phantombuster-automation): Automate lead generation, web scraping, and social media data extraction workflows through PhantomBuster's cloud platform via Composio
- [heyreach-automation](https://doutorai.pt/en/components/skills__composio-awesome__heyreach-automation): Automate Heyreach tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [formbricks-automation](https://doutorai.pt/en/components/skills__composio-awesome__formbricks-automation): Automate Formbricks tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [timecamp-automation](https://doutorai.pt/en/components/skills__composio-awesome__timecamp-automation): Automate Timecamp tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [forcemanager-automation](https://doutorai.pt/en/components/skills__composio-awesome__forcemanager-automation): Automate Forcemanager tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [etermin-automation](https://doutorai.pt/en/components/skills__composio-awesome__etermin-automation): Automate Etermin tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [iqair-airvisual-automation](https://doutorai.pt/en/components/skills__composio-awesome__iqair-airvisual-automation): Automate Iqair Airvisual tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [googlecalendar-automation](https://doutorai.pt/en/components/skills__composio-awesome__googlecalendar-automation): Automate Google Calendar tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [many_chat-automation](https://doutorai.pt/en/components/skills__composio-awesome__many-chat-automation-e24342): Automate ManyChat tasks via Rube MCP (Composio): chatbot flows, subscribers, broadcasts, and messenger automation. Always search tools first for current schemas
- [re-amaze-automation](https://doutorai.pt/en/components/skills__composio-awesome__re-amaze-automation): Automate Re Amaze tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [rosette-text-analytics-automation](https://doutorai.pt/en/components/skills__composio-awesome__rosette-text-analytics-automation): Automate Rosette Text Analytics tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bookingmood-automation](https://doutorai.pt/en/components/skills__composio-awesome__bookingmood-automation): Automate Bookingmood tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [datarobot-automation](https://doutorai.pt/en/components/skills__composio-awesome__datarobot-automation): Automate Datarobot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [LaunchDarkly Automation](https://doutorai.pt/en/components/skills__composio-awesome__launch-darkly-automation-8e3a6c): Automate LaunchDarkly feature flag management -- list projects and environments, create and delete trigger workflows, and track code references via the Composio
- [loyverse-automation](https://doutorai.pt/en/components/skills__composio-awesome__loyverse-automation): Automate Loyverse tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [highergov-automation](https://doutorai.pt/en/components/skills__composio-awesome__highergov-automation): Automate Highergov tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ncscale-automation](https://doutorai.pt/en/components/skills__composio-awesome__ncscale-automation): Automate Ncscale tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [needle-automation](https://doutorai.pt/en/components/skills__composio-awesome__needle-automation): Automate Needle tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [coassemble-automation](https://doutorai.pt/en/components/skills__composio-awesome__coassemble-automation): Automate Coassemble tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [moxie-automation](https://doutorai.pt/en/components/skills__composio-awesome__moxie-automation): Automate Moxie tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [detrack-automation](https://doutorai.pt/en/components/skills__composio-awesome__detrack-automation): Automate Detrack tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zylvie-automation](https://doutorai.pt/en/components/skills__composio-awesome__zylvie-automation): Automate Zylvie tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Gong Automation](https://doutorai.pt/en/components/skills__composio-awesome__gong-automation): Automate Gong conversation intelligence -- retrieve call recordings, transcripts, detailed analytics, speaker stats, and workspace data -- using natural languag
- [shipengine-automation](https://doutorai.pt/en/components/skills__composio-awesome__shipengine-automation): Automate Shipengine tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [esignatures-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__esignatures-io-automation): Automate Esignatures IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [botstar-automation](https://doutorai.pt/en/components/skills__composio-awesome__botstar-automation): Automate Botstar tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Contentful Automation](https://doutorai.pt/en/components/skills__composio-awesome__contentful-automation): Automate headless CMS operations in Contentful -- list spaces, retrieve space metadata, and update space configurations through the Composio Contentful integrat
- [veriphone-automation](https://doutorai.pt/en/components/skills__composio-awesome__veriphone-automation): Automate Veriphone tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [insighto-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__insighto-ai-automation): Automate Insighto AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [remote-retrieval-automation](https://doutorai.pt/en/components/skills__composio-awesome__remote-retrieval-automation): Automate Remote Retrieval tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [spondyr-automation](https://doutorai.pt/en/components/skills__composio-awesome__spondyr-automation): Automate Spondyr tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [rippling-automation](https://doutorai.pt/en/components/skills__composio-awesome__rippling-automation): Automate Rippling tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [weathermap-automation](https://doutorai.pt/en/components/skills__composio-awesome__weathermap-automation): Automate Weathermap tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cal-automation](https://doutorai.pt/en/components/skills__composio-awesome__cal-automation): Automate Cal tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [googledrive-automation](https://doutorai.pt/en/components/skills__composio-awesome__googledrive-automation): Automate Google Drive tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ko-fi-automation](https://doutorai.pt/en/components/skills__composio-awesome__ko-fi-automation): Automate Ko Fi tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [news-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__news-api-automation): Automate News API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [all-images-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__all-images-ai-automation): Automate All Images AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [serpapi-automation](https://doutorai.pt/en/components/skills__composio-awesome__serpapi-automation): Automate Serpapi tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [jigsawstack-automation](https://doutorai.pt/en/components/skills__composio-awesome__jigsawstack-automation): Automate Jigsawstack tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [slackbot-automation](https://doutorai.pt/en/components/skills__composio-awesome__slackbot-automation): Automate Slackbot tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [codeinterpreter-automation](https://doutorai.pt/en/components/skills__composio-awesome__codeinterpreter-automation): Automate Codeinterpreter tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sap-successfactors-automation](https://doutorai.pt/en/components/skills__composio-awesome__sap-successfactors-automation): Automate SAP SuccessFactors tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [tinyurl-automation](https://doutorai.pt/en/components/skills__composio-awesome__tinyurl-automation): Automate Tinyurl tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [kraken-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__kraken-io-automation): Automate Kraken IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [tomtom-automation](https://doutorai.pt/en/components/skills__composio-awesome__tomtom-automation): Automate Tomtom tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [salesforce-marketing-cloud-automation](https://doutorai.pt/en/components/skills__composio-awesome__salesforce-marketing-cloud-automation): Automate Salesforce Marketing Cloud tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [googleslides-automation](https://doutorai.pt/en/components/skills__composio-awesome__googleslides-automation): Automate Google Slides tasks via Rube MCP (Composio): create presentations, add slides from Markdown, batch update, copy from templates, get thumbnails. Always 
- [async-interview-automation](https://doutorai.pt/en/components/skills__composio-awesome__async-interview-automation): Automate Async Interview tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [control-d-automation](https://doutorai.pt/en/components/skills__composio-awesome__control-d-automation): Automate Control D tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [addresszen-automation](https://doutorai.pt/en/components/skills__composio-awesome__addresszen-automation): Automate Addresszen tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [backendless-automation](https://doutorai.pt/en/components/skills__composio-awesome__backendless-automation): Automate Backendless tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [brightdata-automation](https://doutorai.pt/en/components/skills__composio-awesome__brightdata-automation): Automate Brightdata tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [short-menu-automation](https://doutorai.pt/en/components/skills__composio-awesome__short-menu-automation): Automate Short Menu tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [magnetic-automation](https://doutorai.pt/en/components/skills__composio-awesome__magnetic-automation): Automate Magnetic tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [instacart-automation](https://doutorai.pt/en/components/skills__composio-awesome__instacart-automation): Automate Instacart tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [timelink-automation](https://doutorai.pt/en/components/skills__composio-awesome__timelink-automation): Automate Timelink tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [postgrid-automation](https://doutorai.pt/en/components/skills__composio-awesome__postgrid-automation): Automate Postgrid tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [memberstack-automation](https://doutorai.pt/en/components/skills__composio-awesome__memberstack-automation): Automate Memberstack tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [whautomate-automation](https://doutorai.pt/en/components/skills__composio-awesome__whautomate-automation): Automate Whautomate tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [googlebigquery-automation](https://doutorai.pt/en/components/skills__composio-awesome__googlebigquery-automation): Automate Google BigQuery tasks via Rube MCP (Composio): run SQL queries, explore datasets and metadata, execute MBQL queries via Metabase integration. Always se
- [icims-talent-cloud-automation](https://doutorai.pt/en/components/skills__composio-awesome__icims-talent-cloud-automation): Automate Icims Talent Cloud tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [lexoffice-automation](https://doutorai.pt/en/components/skills__composio-awesome__lexoffice-automation): Automate Lexoffice tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [renderform-automation](https://doutorai.pt/en/components/skills__composio-awesome__renderform-automation): Automate Renderform tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dripcel-automation](https://doutorai.pt/en/components/skills__composio-awesome__dripcel-automation): Automate Dripcel tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [neverbounce-automation](https://doutorai.pt/en/components/skills__composio-awesome__neverbounce-automation): Automate Neverbounce tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zeplin-automation](https://doutorai.pt/en/components/skills__composio-awesome__zeplin-automation): Automate Zeplin tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [fingertip-automation](https://doutorai.pt/en/components/skills__composio-awesome__fingertip-automation): Automate Fingertip tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [imgix-automation](https://doutorai.pt/en/components/skills__composio-awesome__imgix-automation): Automate Imgix tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [retently-automation](https://doutorai.pt/en/components/skills__composio-awesome__retently-automation): Automate Retently tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [smugmug-automation](https://doutorai.pt/en/components/skills__composio-awesome__smugmug-automation): Automate Smugmug tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [drip-jobs-automation](https://doutorai.pt/en/components/skills__composio-awesome__drip-jobs-automation): Automate Drip Jobs tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sidetracker-automation](https://doutorai.pt/en/components/skills__composio-awesome__sidetracker-automation): Automate Sidetracker tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [gift-up-automation](https://doutorai.pt/en/components/skills__composio-awesome__gift-up-automation): Automate Gift Up tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [turbot-pipes-automation](https://doutorai.pt/en/components/skills__composio-awesome__turbot-pipes-automation): Automate Turbot Pipes tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bitquery-automation](https://doutorai.pt/en/components/skills__composio-awesome__bitquery-automation): Automate Bitquery tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [pdf-co-automation](https://doutorai.pt/en/components/skills__composio-awesome__pdf-co-automation): Automate PDF co tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ninox-automation](https://doutorai.pt/en/components/skills__composio-awesome__ninox-automation): Automate Ninox tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [adyntel-automation](https://doutorai.pt/en/components/skills__composio-awesome__adyntel-automation): Automate Adyntel tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [sendloop-automation](https://doutorai.pt/en/components/skills__composio-awesome__sendloop-automation): Automate Sendloop tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [microsoft-tenant-automation](https://doutorai.pt/en/components/skills__composio-awesome__microsoft-tenant-automation): Automate Microsoft Tenant tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [callpage-automation](https://doutorai.pt/en/components/skills__composio-awesome__callpage-automation): Automate Callpage tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [chatwork-automation](https://doutorai.pt/en/components/skills__composio-awesome__chatwork-automation): Automate Chatwork tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [nextdns-automation](https://doutorai.pt/en/components/skills__composio-awesome__nextdns-automation): Automate Nextdns tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [kickbox-automation](https://doutorai.pt/en/components/skills__composio-awesome__kickbox-automation): Automate Kickbox tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bestbuy-automation](https://doutorai.pt/en/components/skills__composio-awesome__bestbuy-automation): Automate Bestbuy tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [beaconstac-automation](https://doutorai.pt/en/components/skills__composio-awesome__beaconstac-automation): Automate Beaconstac tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mezmo-automation](https://doutorai.pt/en/components/skills__composio-awesome__mezmo-automation): Automate Mezmo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Prismic Automation](https://doutorai.pt/en/components/skills__composio-awesome__prismic-automation): Automate headless CMS operations in Prismic -- query documents, search content, retrieve custom types, and manage repository refs through the Composio Prismic i
- [OpenAI Automation](https://doutorai.pt/en/components/skills__composio-awesome__openai-automation): Automate OpenAI API operations -- generate responses with multimodal and structured output support, create embeddings, generate images, and list models via the 
- [recruitee-automation](https://doutorai.pt/en/components/skills__composio-awesome__recruitee-automation): Automate Recruitee tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [accelo-automation](https://doutorai.pt/en/components/skills__composio-awesome__accelo-automation): Automate Accelo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [passcreator-automation](https://doutorai.pt/en/components/skills__composio-awesome__passcreator-automation): Automate Passcreator tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [hotspotsystem-automation](https://doutorai.pt/en/components/skills__composio-awesome__hotspotsystem-automation): Automate Hotspotsystem tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [fomo-automation](https://doutorai.pt/en/components/skills__composio-awesome__fomo-automation): Automate Fomo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [rafflys-automation](https://doutorai.pt/en/components/skills__composio-awesome__rafflys-automation): Automate Rafflys tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Zoho Desk Automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-desk-automation-5ed6bc): Zoho Desk automation via Rube MCP -- toolkit not currently available in Composio; no ZOHO_DESK_ tools found
- [dromo-automation](https://doutorai.pt/en/components/skills__composio-awesome__dromo-automation): Automate Dromo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [finerworks-automation](https://doutorai.pt/en/components/skills__composio-awesome__finerworks-automation): Automate Finerworks tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Lemon Squeezy Automation](https://doutorai.pt/en/components/skills__composio-awesome__lemon-squeezy-automation-05e589): Automate Lemon Squeezy store management -- products, orders, subscriptions, customers, discounts, and checkout tracking -- using natural language through the Co
- [scrape-do-automation](https://doutorai.pt/en/components/skills__composio-awesome__scrape-do-automation): Automate Scrape Do tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [storerocket-automation](https://doutorai.pt/en/components/skills__composio-awesome__storerocket-automation): Automate Storerocket tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bigml-automation](https://doutorai.pt/en/components/skills__composio-awesome__bigml-automation): Automate Bigml tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mboum-automation](https://doutorai.pt/en/components/skills__composio-awesome__mboum-automation): Automate Mboum tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [prerender-automation](https://doutorai.pt/en/components/skills__composio-awesome__prerender-automation): Automate Prerender tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Mistral AI Automation](https://doutorai.pt/en/components/skills__composio-awesome__mistral-ai-automation-a4e460): Automate Mistral AI operations -- manage files and libraries, upload documents for fine-tuning, batch processing, and OCR, track fine-tuning jobs, and build RAG
- [tapfiliate-automation](https://doutorai.pt/en/components/skills__composio-awesome__tapfiliate-automation): Automate Tapfiliate tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [better-stack-automation](https://doutorai.pt/en/components/skills__composio-awesome__better-stack-automation): Automate Better Stack tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dropcontact-automation](https://doutorai.pt/en/components/skills__composio-awesome__dropcontact-automation): Automate Dropcontact tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [scrapingbee-automation](https://doutorai.pt/en/components/skills__composio-awesome__scrapingbee-automation): Automate Scrapingbee tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [neuronwriter-automation](https://doutorai.pt/en/components/skills__composio-awesome__neuronwriter-automation): Automate Neuronwriter tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [tomba-automation](https://doutorai.pt/en/components/skills__composio-awesome__tomba-automation): Automate Tomba tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [pilvio-automation](https://doutorai.pt/en/components/skills__composio-awesome__pilvio-automation): Automate Pilvio tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [polygon-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__polygon-io-automation): Automate Polygon IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [claid-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__claid-ai-automation): Automate Claid AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [serply-automation](https://doutorai.pt/en/components/skills__composio-awesome__serply-automation): Automate Serply tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Customer.io Automation](https://doutorai.pt/en/components/skills__composio-awesome__customerio-automation): Automate customer engagement workflows including broadcast triggers, message analytics, segment management, and newsletter tracking through Customer.io via Comp
- [google-search-console-automation](https://doutorai.pt/en/components/skills__composio-awesome__google-search-console-automation-bd4dc8): Automate Google Search Console tasks via Rube MCP (Composio): query search analytics, list sites, inspect URLs, submit sitemaps, monitor search performance. Alw
- [skyfire-automation](https://doutorai.pt/en/components/skills__composio-awesome__skyfire-automation): Automate Skyfire tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [everhour-automation](https://doutorai.pt/en/components/skills__composio-awesome__everhour-automation): Automate Everhour tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cloudcart-automation](https://doutorai.pt/en/components/skills__composio-awesome__cloudcart-automation): Automate Cloudcart tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [userlist-automation](https://doutorai.pt/en/components/skills__composio-awesome__userlist-automation): Automate Userlist tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dpd2-automation](https://doutorai.pt/en/components/skills__composio-awesome__dpd2-automation): Automate Dpd2 tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [SharePoint Automation](https://doutorai.pt/en/components/skills__composio-awesome__share-point-automation-d068f3): SharePoint Automation: manage sites, lists, documents, folders, pages, and search content across SharePoint and OneDrive
- [the-odds-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__the-odds-api-automation): Automate The Odds API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [twocaptcha-automation](https://doutorai.pt/en/components/skills__composio-awesome__twocaptcha-automation): Automate Twocaptcha tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [u301-automation](https://doutorai.pt/en/components/skills__composio-awesome__u301-automation): Automate U301 tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mailbluster-automation](https://doutorai.pt/en/components/skills__composio-awesome__mailbluster-automation): Automate Mailbluster tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [adobe-automation](https://doutorai.pt/en/components/skills__composio-awesome__adobe-automation): Automate Adobe tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [emailoctopus-automation](https://doutorai.pt/en/components/skills__composio-awesome__emailoctopus-automation): Automate Emailoctopus tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [alpha-vantage-automation](https://doutorai.pt/en/components/skills__composio-awesome__alpha-vantage-automation): Automate Alpha Vantage tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [loomio-automation](https://doutorai.pt/en/components/skills__composio-awesome__loomio-automation): Automate Loomio tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [rev-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__rev-ai-automation): Automate Rev AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [onesignal-rest-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__onesignal-rest-api-automation-839b1b): Automate OneSignal tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [conveyor-automation](https://doutorai.pt/en/components/skills__composio-awesome__conveyor-automation): Automate Conveyor tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cloudlayer-automation](https://doutorai.pt/en/components/skills__composio-awesome__cloudlayer-automation): Automate Cloudlayer tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [hyperise-automation](https://doutorai.pt/en/components/skills__composio-awesome__hyperise-automation): Automate Hyperise tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [new_relic-automation](https://doutorai.pt/en/components/skills__composio-awesome__new-relic-automation-a7b809): Automate New Relic tasks via Rube MCP (Composio): APM, alerts, dashboards, NRQL queries, and infrastructure monitoring. Always search tools first for current sc
- [Replicate Automation](https://doutorai.pt/en/components/skills__composio-awesome__replicate-automation): Automate Replicate AI model operations -- run predictions, upload files, inspect model schemas, list versions, and manage prediction history via the Composio MC
- [linkup-automation](https://doutorai.pt/en/components/skills__composio-awesome__linkup-automation): Automate Linkup tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [exa-automation](https://doutorai.pt/en/components/skills__composio-awesome__exa-automation): Automate Exa tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [kontent-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__kontent-ai-automation): Automate Kontent AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [postgrid-verify-automation](https://doutorai.pt/en/components/skills__composio-awesome__postgrid-verify-automation): Automate Postgrid Verify tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [remarkety-automation](https://doutorai.pt/en/components/skills__composio-awesome__remarkety-automation): Automate Remarkety tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [agiled-automation](https://doutorai.pt/en/components/skills__composio-awesome__agiled-automation): Automate Agiled tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [apitemplate-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__apitemplate-io-automation): Automate Apitemplate IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [astica-ai-automation](https://doutorai.pt/en/components/skills__composio-awesome__astica-ai-automation): Automate Astica AI tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bitwarden-automation](https://doutorai.pt/en/components/skills__composio-awesome__bitwarden-automation): Automate Bitwarden tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [lodgify-automation](https://doutorai.pt/en/components/skills__composio-awesome__lodgify-automation): Automate Lodgify tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ipdata-co-automation](https://doutorai.pt/en/components/skills__composio-awesome__ipdata-co-automation): Automate Ipdata co tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [appdrag-automation](https://doutorai.pt/en/components/skills__composio-awesome__appdrag-automation): Automate Appdrag tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [epic-games-automation](https://doutorai.pt/en/components/skills__composio-awesome__epic-games-automation): Automate Epic Games tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Docker Hub Automation](https://doutorai.pt/en/components/skills__composio-awesome__docker-hub-automation-ef86a3): Automate Docker Hub operations -- manage organizations, repositories, teams, members, and webhooks via the Composio MCP integration.
- [html-to-image-automation](https://doutorai.pt/en/components/skills__composio-awesome__html-to-image-automation): Automate Html To Image tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [project-bubble-automation](https://doutorai.pt/en/components/skills__composio-awesome__project-bubble-automation): Automate Project Bubble tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [plasmic-automation](https://doutorai.pt/en/components/skills__composio-awesome__plasmic-automation): Automate Plasmic tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [placid-automation](https://doutorai.pt/en/components/skills__composio-awesome__placid-automation): Automate Placid tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [craftmypdf-automation](https://doutorai.pt/en/components/skills__composio-awesome__craftmypdf-automation): Automate Craftmypdf tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zoho-bigin-automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-bigin-automation-18e5d6): Automate Zoho Bigin tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [realphonevalidation-automation](https://doutorai.pt/en/components/skills__composio-awesome__realphonevalidation-automation): Automate Realphonevalidation tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [eventzilla-automation](https://doutorai.pt/en/components/skills__composio-awesome__eventzilla-automation): Automate Eventzilla tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [endorsal-automation](https://doutorai.pt/en/components/skills__composio-awesome__endorsal-automation): Automate Endorsal tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [hyperbrowser-automation](https://doutorai.pt/en/components/skills__composio-awesome__hyperbrowser-automation): Automate Hyperbrowser tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [qualaroo-automation](https://doutorai.pt/en/components/skills__composio-awesome__qualaroo-automation): Automate Qualaroo tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cats-automation](https://doutorai.pt/en/components/skills__composio-awesome__cats-automation): Automate Cats tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [fraudlabs-pro-automation](https://doutorai.pt/en/components/skills__composio-awesome__fraudlabs-pro-automation): Automate Fraudlabs Pro tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [segmetrics-automation](https://doutorai.pt/en/components/skills__composio-awesome__segmetrics-automation): Automate Segmetrics tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Clockify Automation](https://doutorai.pt/en/components/skills__composio-awesome__clockify-automation): Automate time tracking workflows in Clockify -- create and manage time entries, workspaces, and users through natural language commands.
- [graphhopper-automation](https://doutorai.pt/en/components/skills__composio-awesome__graphhopper-automation): Automate Graphhopper tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Cloudinary Automation](https://doutorai.pt/en/components/skills__composio-awesome__cloudinary-automation): Automate Cloudinary media management including folder organization, upload presets, asset lookup, transformations, and usage monitoring through natural language
- [canny-automation](https://doutorai.pt/en/components/skills__composio-awesome__canny-automation): Automate Canny tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [tavily-automation](https://doutorai.pt/en/components/skills__composio-awesome__tavily-automation): Automate Tavily tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [hackernews-automation](https://doutorai.pt/en/components/skills__composio-awesome__hackernews-automation): Automate Hackernews tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [lever-sandbox-automation](https://doutorai.pt/en/components/skills__composio-awesome__lever-sandbox-automation): Automate Lever Sandbox tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [ably-automation](https://doutorai.pt/en/components/skills__composio-awesome__ably-automation): Automate Ably tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [leadoku-automation](https://doutorai.pt/en/components/skills__composio-awesome__leadoku-automation): Automate Leadoku tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [botpress-automation](https://doutorai.pt/en/components/skills__composio-awesome__botpress-automation): Automate Botpress tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [influxdb-cloud-automation](https://doutorai.pt/en/components/skills__composio-awesome__influxdb-cloud-automation): Automate Influxdb Cloud tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [benzinga-automation](https://doutorai.pt/en/components/skills__composio-awesome__benzinga-automation): Automate Benzinga tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [fireflies-automation](https://doutorai.pt/en/components/skills__composio-awesome__fireflies-automation): Automate Fireflies tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [gigasheet-automation](https://doutorai.pt/en/components/skills__composio-awesome__gigasheet-automation): Automate Gigasheet tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zoho-mail-automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-mail-automation-39307a): Automate Zoho Mail tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Neon Automation](https://doutorai.pt/en/components/skills__composio-awesome__neon-automation): Automate Neon serverless Postgres operations -- manage projects, branches, databases, roles, and connection URIs via the Composio MCP integration.
- [pushbullet-automation](https://doutorai.pt/en/components/skills__composio-awesome__pushbullet-automation): Automate Pushbullet tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [plisio-automation](https://doutorai.pt/en/components/skills__composio-awesome__plisio-automation): Automate Plisio tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [eodhd-apis-automation](https://doutorai.pt/en/components/skills__composio-awesome__eodhd-apis-automation): Automate Eodhd Apis tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [codacy-automation](https://doutorai.pt/en/components/skills__composio-awesome__codacy-automation): Automate Codacy tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [better-proposals-automation](https://doutorai.pt/en/components/skills__composio-awesome__better-proposals-automation): Automate Better Proposals tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [HeyGen Automation](https://doutorai.pt/en/components/skills__composio-awesome__heygen-automation): Automate AI video generation, avatar browsing, template-based video creation, and video status tracking through HeyGen's platform via Composio
- [griptape-automation](https://doutorai.pt/en/components/skills__composio-awesome__griptape-automation): Automate Griptape tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [seat-geek-automation](https://doutorai.pt/en/components/skills__composio-awesome__seat-geek-automation): Automate Seat Geek tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [placekey-automation](https://doutorai.pt/en/components/skills__composio-awesome__placekey-automation): Automate Placekey tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [pushover-automation](https://doutorai.pt/en/components/skills__composio-awesome__pushover-automation): Automate Pushover tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [servicem8-automation](https://doutorai.pt/en/components/skills__composio-awesome__servicem8-automation): Automate Servicem8 tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [streamtime-automation](https://doutorai.pt/en/components/skills__composio-awesome__streamtime-automation): Automate Streamtime tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [blackbaud-automation](https://doutorai.pt/en/components/skills__composio-awesome__blackbaud-automation): Automate Blackbaud tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [contentful-graphql-automation](https://doutorai.pt/en/components/skills__composio-awesome__contentful-graphql-automation): Automate Contentful Graphql tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [blackboard-automation](https://doutorai.pt/en/components/skills__composio-awesome__blackboard-automation): Automate Blackboard tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Productboard Automation](https://doutorai.pt/en/components/skills__composio-awesome__productboard-automation): Automate product management workflows in Productboard -- manage features, notes, objectives, components, and releases through natural language commands.
- [google-classroom-automation](https://doutorai.pt/en/components/skills__composio-awesome__google-classroom-automation-68fc62): Automate Google Classroom tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Attio Automation](https://doutorai.pt/en/components/skills__composio-awesome__attio-automation): Automate Attio CRM operations -- search records, query contacts and companies with advanced filters, manage notes, list attributes, and navigate your relationsh
- [googleads-automation](https://doutorai.pt/en/components/skills__composio-awesome__googleads-automation): Automate Google Ads analytics tasks via Rube MCP (Composio): list Google Ads links, run GA4 reports, check compatibility, list properties and accounts. Always s
- [ambee-automation](https://doutorai.pt/en/components/skills__composio-awesome__ambee-automation): Automate Ambee tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zoho-inventory-automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-inventory-automation-8bc6a7): Automate Zoho Inventory tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [browseai-automation](https://doutorai.pt/en/components/skills__composio-awesome__browseai-automation): Automate Browseai tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [connecteam-automation](https://doutorai.pt/en/components/skills__composio-awesome__connecteam-automation): Automate Connecteam tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [benchmark-email-automation](https://doutorai.pt/en/components/skills__composio-awesome__benchmark-email-automation): Automate Benchmark Email tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [nocrm-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__nocrm-io-automation): Automate Nocrm IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [zoho-automation](https://doutorai.pt/en/components/skills__composio-awesome__zoho-automation): Automate Zoho tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [shotstack-automation](https://doutorai.pt/en/components/skills__composio-awesome__shotstack-automation): Automate Shotstack tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cloudflare-browser-rendering-automation](https://doutorai.pt/en/components/skills__composio-awesome__cloudflare-browser-rendering-automation): Automate Cloudflare Browser Rendering tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [dictionary-api-automation](https://doutorai.pt/en/components/skills__composio-awesome__dictionary-api-automation): Automate Dictionary API tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mailcheck-automation](https://doutorai.pt/en/components/skills__composio-awesome__mailcheck-automation): Automate Mailcheck tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [metaads-automation](https://doutorai.pt/en/components/skills__composio-awesome__metaads-automation): Automate Metaads tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [stack-exchange-automation](https://doutorai.pt/en/components/skills__composio-awesome__stack-exchange-automation): Automate Stack Exchange tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [cloudconvert-automation](https://doutorai.pt/en/components/skills__composio-awesome__cloudconvert-automation): Automate Cloudconvert tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [emailable-automation](https://doutorai.pt/en/components/skills__composio-awesome__emailable-automation): Automate Emailable tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [writer-automation](https://doutorai.pt/en/components/skills__composio-awesome__writer-automation): Automate Writer tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mural-automation](https://doutorai.pt/en/components/skills__composio-awesome__mural-automation): Automate Mural tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [fibery-automation](https://doutorai.pt/en/components/skills__composio-awesome__fibery-automation): Automate Fibery tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [corrently-automation](https://doutorai.pt/en/components/skills__composio-awesome__corrently-automation): Automate Corrently tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Lever Automation](https://doutorai.pt/en/components/skills__composio-awesome__lever-automation): Automate recruiting workflows in Lever ATS -- manage opportunities, job postings, requisitions, pipeline stages, and candidate tags through the Composio Lever i
- [openperplex-automation](https://doutorai.pt/en/components/skills__composio-awesome__openperplex-automation): Automate Openperplex tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Kommo Automation](https://doutorai.pt/en/components/skills__composio-awesome__kommo-automation): Automate Kommo CRM operations -- manage leads, pipelines, pipeline stages, tasks, and custom fields -- using natural language through the Composio MCP integrati
- [botsonic-automation](https://doutorai.pt/en/components/skills__composio-awesome__botsonic-automation): Automate Botsonic tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [logo-dev-automation](https://doutorai.pt/en/components/skills__composio-awesome__logo-dev-automation): Automate Logo Dev tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [autobound-automation](https://doutorai.pt/en/components/skills__composio-awesome__autobound-automation): Automate Autobound tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [similarweb_digitalrank_api-automation](https://doutorai.pt/en/components/skills__composio-awesome__similarweb-digitalrank-api-automation-30fee0): Automate SimilarWeb tasks via Rube MCP (Composio): website traffic, rankings, and digital market intelligence. Always search tools first for current schemas.
- [fillout-forms-automation](https://doutorai.pt/en/components/skills__composio-awesome__fillout-forms-automation-2aa590): Automate Fillout tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [abyssale-automation](https://doutorai.pt/en/components/skills__composio-awesome__abyssale-automation): Automate Abyssale tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [enigma-automation](https://doutorai.pt/en/components/skills__composio-awesome__enigma-automation): Automate Enigma tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [basin-automation](https://doutorai.pt/en/components/skills__composio-awesome__basin-automation): Automate Basin tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [workable-automation](https://doutorai.pt/en/components/skills__composio-awesome__workable-automation): Automate Workable tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [productlane-automation](https://doutorai.pt/en/components/skills__composio-awesome__productlane-automation): Automate Productlane tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [y-gy-automation](https://doutorai.pt/en/components/skills__composio-awesome__y-gy-automation): Automate Y Gy tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [refiner-automation](https://doutorai.pt/en/components/skills__composio-awesome__refiner-automation): Automate Refiner tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [baselinker-automation](https://doutorai.pt/en/components/skills__composio-awesome__baselinker-automation): Automate Baselinker tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [Webex Automation](https://doutorai.pt/en/components/skills__composio-awesome__webex-automation): Automate Cisco Webex messaging, rooms, teams, webhooks, and people management through natural language commands
- [daffy-automation](https://doutorai.pt/en/components/skills__composio-awesome__daffy-automation): Automate Daffy tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [opengraph-io-automation](https://doutorai.pt/en/components/skills__composio-awesome__opengraph-io-automation): Automate Opengraph IO tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [leiga-automation](https://doutorai.pt/en/components/skills__composio-awesome__leiga-automation): Automate Leiga tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [mapulus-automation](https://doutorai.pt/en/components/skills__composio-awesome__mapulus-automation): Automate Mapulus tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bart-automation](https://doutorai.pt/en/components/skills__composio-awesome__bart-automation): Automate Bart tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [documenso-automation](https://doutorai.pt/en/components/skills__composio-awesome__documenso-automation): Automate Documenso tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [bigmailer-automation](https://doutorai.pt/en/components/skills__composio-awesome__bigmailer-automation): Automate Bigmailer tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [telnyx-automation](https://doutorai.pt/en/components/skills__composio-awesome__telnyx-automation): Automate Telnyx tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [hystruct-automation](https://doutorai.pt/en/components/skills__composio-awesome__hystruct-automation): Automate Hystruct tasks via Rube MCP (Composio). Always search tools first for current schemas.
- [googletasks-automation](https://doutorai.pt/en/components/skills__composio-awesome__googletasks-automation): Automate Google Tasks via Rube MCP (Composio): create, list, update, delete, move, and bulk-insert tasks and task lists. Always search tools first for current s
- [pptx](https://doutorai.pt/en/components/skills__composio-awesome__pptx): Presentation creation, editing, and analysis. When Claude needs to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or e
- [xlsx](https://doutorai.pt/en/components/skills__composio-awesome__xlsx): Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work wit
- [pdf](https://doutorai.pt/en/components/skills__composio-awesome__pdf): Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to 
- [docx](https://doutorai.pt/en/components/skills__composio-awesome__docx): Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude nee
- [requesting-code-review](https://doutorai.pt/en/components/skills__obra-superpowers__requesting-code-review): Use when completing tasks, implementing major features, or before merging to verify work meets requirements
- [writing-skills](https://doutorai.pt/en/components/skills__obra-superpowers__writing-skills): Use when creating new skills, editing existing skills, or verifying skills work before deployment
- [using-git-worktrees](https://doutorai.pt/en/components/skills__obra-superpowers__using-git-worktrees): Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via n
- [dispatching-parallel-agents](https://doutorai.pt/en/components/skills__obra-superpowers__dispatching-parallel-agents): Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
- [writing-plans](https://doutorai.pt/en/components/skills__obra-superpowers__writing-plans): Use when you have a spec or requirements for a multi-step task, before touching code
- [systematic-debugging](https://doutorai.pt/en/components/skills__obra-superpowers__systematic-debugging): Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
- [using-superpowers](https://doutorai.pt/en/components/skills__obra-superpowers__using-superpowers): Use when starting any conversation - establishes how to find and use skills, requiring skill invocation before ANY response including clarifying questions
- [receiving-code-review](https://doutorai.pt/en/components/skills__obra-superpowers__receiving-code-review): Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical 
- [test-driven-development](https://doutorai.pt/en/components/skills__obra-superpowers__test-driven-development): Use when implementing any feature or bugfix, before writing implementation code
- [verification-before-completion](https://doutorai.pt/en/components/skills__obra-superpowers__verification-before-completion): Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output be
- [executing-plans](https://doutorai.pt/en/components/skills__obra-superpowers__executing-plans): Use when you have a written implementation plan to execute in a separate session with review checkpoints
- [subagent-driven-development](https://doutorai.pt/en/components/skills__obra-superpowers__subagent-driven-development): Use when executing implementation plans with independent tasks in the current session
- [finishing-a-development-branch](https://doutorai.pt/en/components/skills__obra-superpowers__finishing-a-development-branch): Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting stru
- [brainstorming](https://doutorai.pt/en/components/skills__obra-superpowers__brainstorming): You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirem
- [write-tests](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__write-tests): Add missing test coverage for your local code changes by generating new test files (covers uncommitted and untracked changes, or the latest commit if everything
- [test-coverage](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__test-coverage): Use after writing tests to assess coverage quality across structural, mutation, requirements, and API/integration dimensions; organized knowledge for choosing a
- [fix-tests](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__fix-tests): Systematically fix all failing tests after business logic changes or refactoring
- [test-driven-development](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__test-driven-development): Use when implementing any feature or bugfix, before writing implementation code - write the test first, watch it fail, write minimal code to pass; ensures tests
- [design-testing-strategy](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__design-testing-strategy): Use before writing any type of tests. Distills 14 industry sources into deterministic decision gates, schemas, and worked test examples.
- [plan-do-check-act](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__plan-do-check-act): Iterative PDCA cycle for systematic experimentation and continuous improvement
- [kaizen](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__kaizen): Use when Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide teh
- [cause-and-effect](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__cause-and-effect): Systematic Fishbone analysis exploring problem causes across six categories
- [why](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__why): Iterative Five Whys root cause analysis drilling from symptoms to fundamentals
- [root-cause-tracing](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__root-cause-tracing): Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding
- [analyse-problem](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__analyse-problem): Comprehensive A3 one-page problem analysis with root cause and action plan
- [analyse](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__analyse): Auto-selects best Kaizen method (Gemba Walk, Value Stream, or Muda) for target
- [do-in-parallel](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__do-in-parallel): Run independent tasks concurrently across multiple files or targets using parallel sub-agents, with per-task model selection and LLM-as-a-judge verification. Us
- [launch-sub-agent](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__launch-sub-agent): Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self
- [multi-agent-patterns](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__multi-agent-patterns): Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when spe
- [do-and-judge](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__do-and-judge): Execute a task with sub-agent implementation and LLM-as-a-judge verification with automatic retry loop
- [do-competitively](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__do-competitively): Execute tasks through competitive multi-agent generation, meta-judge evaluation specification, multi-judge evaluation, and evidence-based synthesis
- [do-in-steps](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__do-in-steps): Execute one complex task as ordered, dependent steps run sequentially, passing context from each step to the next, with per-step LLM-as-a-judge verification. Us
- [judge](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__judge): Launch a meta-judge then a judge sub-agent to evaluate results produced in the current conversation
- [tree-of-thoughts](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__tree-of-thoughts): Execute tasks through systematic exploration, pruning, and expansion using Tree of Thoughts methodology with meta-judge evaluation specifications and multi-agen
- [subagent-driven-development](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__subagent-driven-development): Use when executing implementation plans with independent tasks in the current session or facing 3+ independent issues that can be investigated without shared st
- [judge-with-debate](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__judge-with-debate): Evaluate solutions through multi-round debate between independent judges until consensus
- [review-pr](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__review-pr): Review an existing GitHub pull request and post inline review comments on its diff. Use when the changes are on an opened PR rather than your local working tree
- [review-local-changes](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__review-local-changes): Review your local uncommitted working-tree changes (git diff plus untracked files) and return actionable improvement suggestions. Use before committing, when no
- [traiage-review](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__traiage-review): This skill should be used when need prioritize what changed code in repository human must review.
- [reset](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__reset): Reset the FPF reasoning cycle to start fresh
- [propose-hypotheses](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__propose-hypotheses): Execute complete FPF cycle from hypothesis generation to decision
- [query](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__query): Search the FPF knowledge base and display hypothesis details with assurance information
- [actualize](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__actualize): Reconcile the project's FPF state with recent repository changes
- [decay](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__decay): Manage evidence freshness by identifying stale decisions and providing governance actions
- [status](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__status): Display the current state of the FPF knowledge base
- [reflect](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__reflect): Reflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verification
- [critique](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__critique): Comprehensive multi-perspective review using specialized judges with debate and consensus building
- [memorize](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__memorize): Curates insights from reflections and critiques into CLAUDE.md using Agentic Context Engineering
- [write-concisely](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__write-concisely): Apply writing rules to any documentation that humans will read. Makes your writing clearer, stronger, and more professional.
- [update-docs](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__update-docs): Update and maintain project documentation for local code changes using multi-agent workflow with tech-writer agents. Covers docs/, READMEs, JSDoc, and API docum
- [implement-task](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__implement-task): Implement a task with automated LLM-as-Judge verification per step
- [create-ideas](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__create-ideas): Generate ideas in one shot using creative sampling
- [plan-task](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__plan-task): Refine, parallelize, and verify a draft task specification into a fully planned implementation-ready task
- [brainstorm](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__brainstorm): Use when creating or developing, before writing code or implementation plans - refines rough ideas into fully-formed designs through collaborative questioning, 
- [add-task](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__add-task): creates draft task file in .specs/tasks/draft/ with original user intent
- [git-notes](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__git-notes): Use when adding metadata to commits without changing history, tracking review status, test results, code quality annotations, or supplementing commit messages p
- [load-issues](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__load-issues): Load all open issues from GitHub and save them as markdown files
- [resolve-fixed-pr-comments](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__resolve-fixed-pr-comments): Verify what PR review comments have been addressed (committed/pushed OR uncommitted local changes) and resolve the threads that are genuinely fixed or no longer
- [commit](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__commit): Create well-formatted commits with conventional commit messages and emoji
- [analyze-issue](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__analyze-issue): Analyze a GitHub issue and create a detailed technical specification
- [load-pr-comments](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__load-pr-comments): Use to load open/unresolved PR review comments then aggregate them as tasks in .specs/comments/*.md for parallel agents to fix.
- [create-pr](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__create-pr): Create pull requests using GitHub CLI with proper templates and formatting
- [attach-review-to-pr](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__attach-review-to-pr): Add line-specific review comments to pull requests using GitHub CLI API
- [git-worktrees](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__git-worktrees): Use when working on multiple branches simultaneously, context switching without stashing, reviewing PRs while developing, testing in isolation, or comparing imp
- [create-command](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__create-command): Interactive assistant for creating new Claude commands with proper structure, patterns, and MCP tool integration
- [test-skill](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__test-skill): Use when creating or editing skills, before deployment, to verify they work under pressure and resist rationalization - applies RED-GREEN-REFACTOR cycle to proc
- [test-prompt](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__test-prompt): Use when creating or editing any prompt (commands, hooks, skills, subagent instructions) to verify it produces desired behavior - applies RED-GREEN-REFACTOR cyc
- [create-skill](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__create-skill): Guide for creating effective skills. This command should be used when users want to create a new skill (or update an existing skill) that extends Claude's capab
- [prompt-engineering](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__prompt-engineering): Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improvi
- [create-hook](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__create-hook): Create and configure git hooks with intelligent project analysis, suggestions, and automated testing
- [create-workflow-command](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__create-workflow-command): Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts
- [thought-based-reasoning](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__thought-based-reasoning): Use when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where s
- [apply-anthropic-skill-best-practices](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__apply-anthropic-skill-best-practices): Comprehensive guide for skill development based on Anthropic's official best practices - use for complex skills requiring detailed structure
- [create-rule](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__create-rule): Use when found gap or repetative issue, that produced by you or implemenataion agent. Esentially use it each time when you say "You absolutly right, I should ha
- [agent-evaluation](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__agent-evaluation): Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring impro
- [context-engineering](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__context-engineering): Understand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prom
- [create-agent](https://doutorai.pt/en/components/skills__neolab-context-engineering-kit__create-agent): Comprehensive guide for creating Claude Code agents with proper structure, triggering conditions, system prompts, and validation - combines official Anthropic b
- [cro-optimization](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__cro-optimization): Run conversion rate optimization through hypothesis-driven testing including audit, hypothesis generation, test design, statistical analysis, and rollout decisi
- [seo-offpage](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-offpage): Plan and execute off-page SEO including link building, digital PR, brand mentions, citation building, and external authority signals. Use this skill whenever th
- [creative-brief](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__creative-brief): Create or refine a creative brief that bridges discovery and execution. Use this skill whenever the user is starting a new project, kicking off a website, begin
- [journey-mapping](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__journey-mapping): Build customer journey maps and service blueprints that visualize the end-to-end user experience including touchpoints, emotions, friction, and underlying syste
- [scheduler-and-booking-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__scheduler-and-booking-design): Designing meeting schedulers and booking experiences that qualify leads, set up calls well, and convert at higher rates than a generic Calendly link. Availabili
- [launch-runbook](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__launch-runbook): Plan and execute a launch runbook covering pre-launch verification, go-live procedures, DNS cutover, post-launch monitoring, and rollback procedures. Use this s
- [feature-flagging](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__feature-flagging): Operational discipline for feature flags as production infrastructure. Flag types, naming, targeting rules, rollout strategy, lifecycle, governance, stale flag 
- [editorial-qa](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__editorial-qa): Pre-publish QA framework for content. Brief adherence, voice consistency, fact accuracy, structure and clarity, AI-content audit, SEO and AEO compliance, intern
- [okr-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__okr-design): OKR design as actually shipped, not as conference-talk theory. Outcome statements that drive decisions, key results that measure the right thing, scoring discip
- [dependency-management](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__dependency-management): Manage third-party libraries, runtimes, and SaaS dependencies. Use this skill when setting an update cadence, responding to security advisories, dealing with de
- [creative-brief-selector](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__creative-brief-selector): Produce a creative brief grounded in live reference sites and deliberately distinct from existing demos, for any new brand build. Composes with brand-archetype-
- [integration-orchestrator](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__integration-orchestrator): Generate a phased delivery orchestration plan for creative-direction-driven work: which skills run when, what locks at which gate, how handoffs occur, and how t
- [monitoring-and-alerting](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__monitoring-and-alerting): Design and run a monitoring system for a website or web app. Use this skill when setting up uptime checks, defining SLOs, configuring error tracking, choosing w
- [performance-optimization](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__performance-optimization): Diagnose and fix web performance issues including Core Web Vitals (LCP, INP, CLS), bundle size, asset optimization, render performance, and runtime efficiency. 
- [brand-archetype-system](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__brand-archetype-system): Apply pre-composed aesthetic archetype defaults to jumpstart brand design work covering color, typography, layout, voice, and imagery direction. Use this skill 
- [accessibility-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__accessibility-audit): Run a comprehensive WCAG accessibility audit covering perceivable, operable, understandable, and robust principles. Use this skill whenever the user wants to au
- [email-deliverability](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__email-deliverability): Make sure email actually reaches inboxes. Use this skill when setting up email authentication (SPF, DKIM, DMARC), diagnosing emails landing in spam, planning a 
- [incident-response](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__incident-response): Manage active production incidents through detection, triage, mitigation, communication, and resolution with structured roles and decision-making. Use this skil
- [seo-backlink-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-backlink-audit): Audit a backlink profile using Ahrefs MCP data: profile health, anchor text distribution, toxic link identification, lost link reclamation, and gap analysis aga
- [programmatic-seo](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__programmatic-seo): How to design and run a programmatic SEO program that produces durable traffic instead of penalty-bait. Data source identification, template design, schema patt
- [ux-research](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__ux-research): Plan and execute user research including research planning, recruiting, interview design, qualitative synthesis, and translating findings into product decisions
- [long-form-content-frameworks](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__long-form-content-frameworks): Patterns for individual long-form content pieces. Case studies, whitepapers, research reports, definitive guides, manifestos, ebooks, long-form tutorials. The s
- [content-brief-authoring](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-brief-authoring): How to author a content brief that actually guides a writer (human or AI) to produce a piece that ranks, converts, or both. Per-piece editorial brief: target ke
- [creative-direction](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__creative-direction): Walk the user through four directional axes (tone register, aesthetic philosophy, audience relationship, sensory ambition) and produce a structured aesthetic br
- [experiment-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__experiment-design): A discipline for designing experiments (A/B tests, multivariate, holdouts) so the results actually answer the question you asked. Hypothesis writing, sample siz
- [domain-strategy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__domain-strategy): Plan, manage, and optimize a domain portfolio. Use this skill for DNS architecture decisions, redirect strategies, registrar choice, parking unused domains, mul
- [experimentation-platform-orchestrator](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__experimentation-platform-orchestrator): A platform decision framework for experimentation. When to use Statsig vs PostHog vs GrowthBook vs Optimizely vs Amplitude vs Eppo vs Kameleoon. How to migrate 
- [comparison-tool-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__comparison-tool-design): Designing side-by-side comparison tools (plan-compare, product-compare, alternative-compare) that help users decide rather than just listing features. Axis sele
- [code-review-web](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__code-review-web): Review web application code for bugs, security issues, performance problems, and stack-specific anti-patterns. Use this skill whenever the user wants to review 
- [media-asset-management](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__media-asset-management): Plan and run a media pipeline for images, video, and downloadable assets. Use this skill when designing image storage and delivery, choosing formats (WebP, AVIF
- [information-architecture](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__information-architecture): Design the structure of a website or product including sitemap, navigation, URL structure, content types, taxonomy, and labeling. Use this skill whenever the us
- [seo-traffic-diagnosis](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-traffic-diagnosis): Diagnose organic traffic changes (drops, stalls, or unexpected wins) using Ahrefs MCP plus Search Console data. Use this skill when traffic suddenly dropped, ha
- [seo-technical](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-technical): Run a comprehensive technical SEO audit covering crawlability, indexability, rendering, site architecture, structured data, page experience, security, and inter
- [multi-step-form-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__multi-step-form-design): Designing forms with multiple steps, progress indicators, conditional logic, and save-and-resume mechanics. The discipline of breaking complex data collection i
- [seo-rank-tracking](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-rank-tracking): Set up and run rank tracking using Ahrefs MCP: pick the right keywords to track, segment them by purpose, set baselines, and define alert thresholds. Use this s
- [seo-content-gap-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-content-gap-audit): Audit content gaps and decay using Ahrefs MCP data: missing topics, thin coverage, outdated content, and decaying pages. Use this skill when planning a content 
- [jtbd-framing](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__jtbd-framing): The Jobs-to-be-Done framework as applied product methodology. Job statements, struggling moments, hire and fire criteria, the difference between feature-thinkin
- [seo-keyword-gap-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-keyword-gap-audit): Find keywords competitors rank for that the target property does not, and prioritize them by opportunity. Uses Ahrefs MCP for keyword and competitor data. Use t
- [stakeholder-communication](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__stakeholder-communication): Communicate effectively with stakeholders across functions and seniority levels. Use this skill when writing status updates, preparing executive reviews, sharin
- [vendor-evaluation](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__vendor-evaluation): Evaluate, select, and contract with vendors and SaaS tools. Use this skill when comparing alternatives, running an RFP, scoring vendors against criteria, negoti
- [documentation-strategy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__documentation-strategy): Design and run a documentation system for a team or product. Use this skill when planning what to document, choosing a documentation tool, organizing existing d
- [lead-magnet-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__lead-magnet-design): Designing lead magnets that earn the email. The discipline of building gated content (ebooks, templates, checklists, swipe files, mini-courses, free tools) that
- [experimentation-analytics](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__experimentation-analytics): How to read experiment results without fooling yourself. Confidence intervals, p-values, multiple testing, sequential testing, CUPED, heterogeneous treatment ef
- [interactive-product-tour](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__interactive-product-tour): Designing in-product tours, tooltips, and contextual help that teach product capabilities without becoming friction. Trigger logic, tour architecture, contextua
- [competitor-experience-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__competitor-experience-audit): Audit the brand, UX, and site design of the leading sites in a vertical as a set of observable cross-site patterns, producing the experience bar a new build mus
- [brand-style-guide](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__brand-style-guide): Build or audit a comprehensive brand style guide that documents the full brand system including story, logo system, color, typography, imagery, voice, applicati
- [brand-discovery](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__brand-discovery): Run upstream brand discovery covering audience research, competitive landscape, category dynamics, problem space, and positioning territory exploration. Use thi
- [landing-page-copy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__landing-page-copy): Write landing page copy with attention to the hero, value proposition, social proof, objection handling, and conversion-focused CTAs. Use this skill whenever th
- [ads-performance-analytics](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__ads-performance-analytics): How to read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon tr
- [backup-and-disaster-recovery](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__backup-and-disaster-recovery): Plan and run backups, set recovery objectives, and run disaster recovery drills. Use this skill when defining RPO/RTO targets, designing backup architecture, de
- [user-feedback-aggregation](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__user-feedback-aggregation): Collecting and synthesizing user feedback across channels (support tickets, NPS, in-app feedback, sales calls, social mentions, customer councils) into a contin
- [analytics-strategy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__analytics-strategy): Design measurement frameworks including event taxonomy, KPI hierarchy, dashboard architecture, attribution models, and analytics implementation strategy. Use th
- [seo-keyword](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-keyword): Run keyword research, classify by search intent, cluster into topical groups, and prioritize for content production. Use this skill whenever the user asks to do
- [chatbot-flow-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__chatbot-flow-design): Designing conversational flows for website chatbots and AI agents. Intent recognition architecture, branching logic, fallback handling, escalation to human, con
- [pm-spec-writing](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__pm-spec-writing): Translate ideas, feature requests, or vague concepts into specific, actionable dev briefs. Use this skill whenever the user has an idea they want to build, a fe
- [skill-creation-walkthrough](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__skill-creation-walkthrough): Step-by-step guide for creating your own Claude Skills, from deciding whether a skill is the right tool to writing the SKILL.md file, structuring reference mate
- [onboarding-wizard-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__onboarding-wizard-design): Designing first-run product onboarding wizards that get users to the ah-ha moment without overwhelming them. Step architecture, progressive disclosure, escape h
- [seo-aeo-geo](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-aeo-geo): Optimize content and site structure for AI-driven search experiences including AI overviews, large language model citations, generative answer engines, and AI a
- [paid-media-strategy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__paid-media-strategy): A discipline for running paid media that does not light money on fire. Hypothesis writing for paid spend, channel selection, budget allocation, audience targeti
- [frontend-component-build](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__frontend-component-build): Build production-ready frontend components with accessible markup, sensible props, defined states, and tested behavior. Use this skill whenever the user wants t
- [team-onboarding-playbook](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__team-onboarding-playbook): Design a structured onboarding experience that gets new team members productive in 30, 60, and 90 days. Use when a new hire is joining, when contractors or agen
- [security-baseline](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__security-baseline): Establish a security baseline for a website or web app. Use this skill when configuring HTTPS and TLS, setting security headers, planning secrets management, ev
- [art-direction](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__art-direction): Direct visual and creative work for campaigns, photography, illustration, video, and branded experiences. Use this skill whenever the user wants to brief a phot
- [roadmap-planning](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__roadmap-planning): Build a multi-quarter roadmap from a backlog of ideas, requests, and ongoing initiatives. Use this skill when planning the next quarter, sequencing dependent wo
- [seo-content-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-content-audit): Audit existing content across a site to decide what to keep, update, merge, redirect, or delete. Use this skill whenever the user wants to audit existing conten
- [content-and-copy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-and-copy): Write or edit website copy, blog content, and editorial pieces with attention to voice, structure, and goal. Use this skill whenever the user wants to write an 
- [content-distribution](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-distribution): Content distribution as a discipline. Owned channels (newsletter, blog, social), earned channels (PR, syndication, mentions), paid channels (boosted posts, synd
- [discovery-research-synthesis](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__discovery-research-synthesis): Turning research artifacts into actionable PM insight. Customer interviews, user research notes, support ticket reviews, sales call transcripts, survey data, in
- [upgrade-flow-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__upgrade-flow-design): Designing free-to-paid conversion flows for SaaS products. Trigger moments, paywall design, value demonstration, upsell vs downsell, win-back flows, churn preve
- [design-standards](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__design-standards): Apply production-grade design standards when building or reviewing pages, components, or UI. Use this skill whenever the user asks to build a page, design a com
- [brand-ideation](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__brand-ideation): Generate, evaluate, and narrow brand concepts during early ideation including positioning territories, naming candidates, mood directions, and narrative angles.
- [content-refresh-system](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-refresh-system): Systematic content refresh discipline. Quarterly audits, refresh prioritization (which pieces, when, how deep), refresh-vs-merge-vs-delete decisions, the lifecy
- [vertical-site-conventions](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__vertical-site-conventions): Compose pages and sites that read as their vertical: ecommerce-catalog storefronts that look like storefronts, hospitality-food sites that look like restaurants
- [after-action-report](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__after-action-report): Run a structured after-action review (postmortem, retrospective) on a launch, incident, or completed project to capture timeline, root cause analysis, contribut
- [funnel-flow-architecture](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__funnel-flow-architecture): Architecting cross-tool conversion flows that match audience and stage. Landing page to lead magnet to nurture sequence to offer to advanced funnels. Honest abo
- [quiz-and-assessment-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__quiz-and-assessment-design): Designing quizzes, personality assessments, and recommendation tools that segment users into actionable categories rather than generating clicks for clicks' sak
- [seo-audit-orchestration](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-audit-orchestration): Master orchestrator for a full SEO audit suite powered by the Ahrefs MCP. Use this skill when running a comprehensive SEO audit, scoping a quarterly health chec
- [usability-testing](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__usability-testing): Plan and run usability tests on existing or prototype designs including test design, task scripts, moderation, observation, and findings synthesis. Use this ski
- [email-sequences](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__email-sequences): Design and write email campaigns and sequences including onboarding flows, lifecycle campaigns, transactional emails, newsletters, broadcast sends, and launch a
- [data-warehouse-experimentation](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__data-warehouse-experimentation): Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitio
- [form-strategy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__form-strategy): Design forms that convert, validate well, resist spam, and integrate cleanly with downstream systems. Use this skill when designing or auditing any form (contac
- [ai-content-collaboration](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__ai-content-collaboration): How humans and AI compose in content workflows. Where AI legitimately participates, where humans must own, hybrid workflow patterns, voice ownership preservatio
- [brand-identity](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__brand-identity): Design or evaluate a brand visual identity system covering logo, color, typography, imagery direction, iconography, and motion principles. Use this skill whenev
- [ads-creative-development](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__ads-creative-development): How to produce ad creative that converts at performance scale. Hook patterns, format selection, video pacing, variation systems, sequential testing methodology,
- [feature-launch-playbook](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__feature-launch-playbook): The operational playbook for launching a feature well. Positioning, internal alignment, customer comms, sales enablement, support readiness, rollout strategy, m
- [pillar-content-architecture](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__pillar-content-architecture): How to design a content hub that earns topical authority. Pillar topic selection, cluster planning, internal linking architecture, URL structure, pillar and clu
- [brand-voice](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__brand-voice): Develop or document a complete brand voice and tone system covering voice attributes, tone shifts by context, vocabulary preferences, grammar rules, and copy ex
- [seo-competitor](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-competitor): Run a competitive SEO analysis comparing the user's site to chosen competitors across SERP overlap, content depth, backlink profiles, technical posture, and bra
- [product-configurator-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__product-configurator-design): Designing build-your-own product configurators (Tesla-style, custom-pricing, plan-builders) with constraint logic, real-time pricing, validation, and save-and-s
- [calculator-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__calculator-design): Designing interactive calculators (ROI calculators, pricing estimators, savings projections, mortgage calculators, custom assessments) that deliver real decisio
- [internationalization](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__internationalization): Plan and run a multi-language or multi-region site. Use this skill when adding new locales, choosing URL structure for languages (subfolders vs subdomains vs cc
- [content-migration](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-migration): Move content between platforms, domains, or URL structures while preserving SEO equity, user bookmarks, and integrations. Use this skill when planning a CMS mig
- [seo-onpage](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-onpage): Run a comprehensive on-page SEO audit or optimization pass covering title tags, meta descriptions, header structure, content quality, internal links, image opti
- [logo-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__logo-design): Generate logo variants for a brand, producing multiple production-grade marks across different architectures (wordmark, lockup, symbol-only, letterform-as-symbo
- [content-repurposing](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-repurposing): Cross-format content adaptation. Turning one substantial piece into many derivative formats (blog series, email sequences, social posts, webinars, podcasts, vid
- [product-analytics-setup](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__product-analytics-setup): How to actually instrument product analytics correctly. Event taxonomy, property design, naming conventions, schema versioning, identity stitching, funnel desig
- [design-system](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__design-system): Build or audit a design system including component library, design tokens, naming conventions, contribution model, and documentation. Use this skill whenever th
- [seo-site-health-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-site-health-audit): Triage technical SEO findings from Ahrefs Site Audit (and similar crawlers) by SEO impact, not just severity. Use this skill when reviewing crawl results, prior
- [content-strategy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-strategy): Develop a content strategy covering editorial positioning, content pillars, formats, calendar, governance, and topical authority planning. Use this skill whenev
- [cost-optimization](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__cost-optimization): Audit and reduce infrastructure and tooling costs without sacrificing reliability or velocity. Use this skill when reviewing monthly cloud or SaaS spend, findin
- [qa-testing](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__qa-testing): Run QA testing on a page, feature, or full site at one of three depth tiers (smoke, standard, full). Use this skill whenever the user asks to test a page, audit
- [beta-program-management](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__beta-program-management): Running closed and open betas that produce real signal. Beta participant selection, structured feedback collection, beta-to-GA decision criteria, and the differ
- [evidence-based-reviews](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__evidence-based-reviews): Produce honest product reviews and buying guides without fabricated first-hand experience, using disclosed evidence tiers: verified specs, owner-experience synt
- [grok-build](https://doutorai.pt/en/components/skills__sanjay3290-ai-skills__grok-build): Orchestrate coding work by delegating well-specified implementation tasks to xAI's Grok Build CLI (grok) running headlessly, while the coding assistant plans, w
- [mysql](https://doutorai.pt/en/components/skills__sanjay3290-ai-skills__mysql): Execute read-only SQL queries against multiple MySQL databases. Use when: (1) querying MySQL databases, (2) exploring database schemas/tables, (3) running SELEC
- [deep-research](https://doutorai.pt/en/components/skills__sanjay3290-ai-skills__deep-research): Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical
- [mssql](https://doutorai.pt/en/components/skills__sanjay3290-ai-skills__mssql): Execute read-only SQL queries against multiple Microsoft SQL Server databases. Use when: (1) querying MSSQL/SQL Server databases, (2) exploring database schemas
- [postgres](https://doutorai.pt/en/components/skills__sanjay3290-ai-skills__postgres): Execute read-only SQL queries against multiple PostgreSQL databases. Use when: (1) querying PostgreSQL databases, (2) exploring database schemas/tables, (3) run
- [manus](https://doutorai.pt/en/components/skills__sanjay3290-ai-skills__manus): Delegate complex, long-running tasks to Manus AI agent for autonomous execution. Use when user says 'use manus', 'delegate to manus', 'send to manus', 'have man
- [whatsapp](https://doutorai.pt/en/components/skills__sanjay3290-ai-skills__whatsapp): Send and receive WhatsApp messages via the unofficial linked-device client pywhats (pip install pywhats) - pair with QR, send text/images, group chat, read rece
- [outline](https://doutorai.pt/en/components/skills__sanjay3290-ai-skills__outline): Search, read, and manage Outline wiki documents. Use when: (1) searching wiki for documentation, (2) reading wiki pages or articles, (3) listing wiki collection
- [telegram](https://doutorai.pt/en/components/skills__sanjay3290-ai-skills__telegram): Send Telegram messages, files, and alerts via bot API; read replies; ask questions with inline buttons and wait for the answer (approve-from-phone). Supports mu
- [jules](https://doutorai.pt/en/components/skills__sanjay3290-ai-skills__jules): Delegate coding tasks to Google Jules AI agent for asynchronous execution. Use when user says: 'have Jules fix', 'delegate to Jules', 'send to Jules', 'ask Jule
- [notebooklm](https://doutorai.pt/en/components/skills__sanjay3290-ai-skills__notebooklm): Query and manage Google NotebookLM notebooks with persistent profile auth, source sync, batch/multi queries, and structured exports. Use when user asks to query
- [ship-learn-next](https://doutorai.pt/en/components/skills__tapestry-skills__ship-learn-next): Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when us
- [scrum-sage](https://doutorai.pt/en/components/skills__tapestry-skills__scrum-sage): AI-powered Scrum Master and Enterprise Agility Coach based on Jeff Sutherland, Taiichi Ohno, and First Principles thinking. Use when user needs help with Scrum,
- [learn-this](https://doutorai.pt/en/components/skills__tapestry-skills__learn-this): Unified content extraction and action planning. Use when user says "learn-this ", "learn this ", "weave ", "help me plan ", "extract and plan ", "make this acti
- [article-extractor](https://doutorai.pt/en/components/skills__tapestry-skills__article-extractor): Extract clean article content from URLs (blog posts, articles, tutorials) and save as readable text. Use when user wants to download, extract, or save an articl
- [session-log](https://doutorai.pt/en/components/skills__tapestry-skills__session-log): Summarize the current conversation session and append results to the weekly agent-log. Use when user says "log this", "session log", "summarize this session", o
- [unblock-action](https://doutorai.pt/en/components/skills__tapestry-skills__unblock-action): Help the user unblock a vague or stuck action item by clarifying the intended output, scoping it to today, and identifying the concrete next action. Use when us
- [youtube-transcript](https://doutorai.pt/en/components/skills__tapestry-skills__youtube-transcript): Download YouTube video transcripts when user provides a YouTube URL or asks to download/get/fetch a transcript from YouTube. Also use when user wants to transcr
- [block-no-verify-hook](https://doutorai.pt/en/components/skills__wshobson-agents__block-no-verify-hook): Configure a PreToolUse hook to prevent AI agents from skipping git pre-commit hooks with --no-verify and other bypass flags. Use when setting up Claude Code pro
- [fastapi-templates](https://doutorai.pt/en/components/skills__wshobson-agents__fastapi-templates): Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications
- [hads](https://doutorai.pt/en/components/skills__wshobson-agents__hads): Use when writing technical documentation that needs to be readable by both humans and AI models, converting existing docs to HADS format, validating a HADS docu
- [pptx-deck-context](https://doutorai.pt/en/components/skills__wshobson-agents__pptx-deck-context): Use when preparing the narrative, sources, and design context for a new editable PPTX deck.
- [pptx-slide-specification](https://doutorai.pt/en/components/skills__wshobson-agents__pptx-slide-specification): Use when authoring or repairing a coordinate-explicit JSON specification for an editable PPTX deck.
- [pptx-reference-deck-analysis](https://doutorai.pt/en/components/skills__wshobson-agents__pptx-reference-deck-analysis): Use when analyzing a reference PPTX for read-only structure, theme, typography, layout rhythm, diagnostics, derived template catalogs, or safe OOXML package ins
- [pptx-visual-assets](https://doutorai.pt/en/components/skills__wshobson-agents__pptx-visual-assets): Use when selecting and placing approved supporting icons, images, SVGs, diagrams, or infographics in an editable PPTX deck.
- [pptx-quality-gates](https://doutorai.pt/en/components/skills__wshobson-agents__pptx-quality-gates): Use when validating or repairing an editable PPTX deck for geometry, accessibility, native editability, source lineage, and OOXML package integrity.
- [gitlab-ci-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__gitlab-ci-patterns): Build GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for scalable automation. Use when implementing GitLab CI/CD, optimizin
- [github-actions-templates](https://doutorai.pt/en/components/skills__wshobson-agents__github-actions-templates): Create production-ready GitHub Actions workflows for automated testing, building, and deploying applications. Use when setting up CI/CD with GitHub Actions, aut
- [deployment-pipeline-design](https://doutorai.pt/en/components/skills__wshobson-agents__deployment-pipeline-design): Design multi-stage CI/CD pipelines with approval gates, security checks, and deployment orchestration. Use this skill when designing zero-downtime deployment pi
- [secrets-management](https://doutorai.pt/en/components/skills__wshobson-agents__secrets-management): Implement secure secrets management for CI/CD pipelines using Vault, AWS Secrets Manager, or native platform solutions. Use when handling sensitive credentials,
- [tailwind-design-system](https://doutorai.pt/en/components/skills__wshobson-agents__tailwind-design-system): Build scalable design systems with Tailwind CSS v4, design tokens, component libraries, and responsive patterns. Use when creating component libraries, implemen
- [nextjs-app-router-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__nextjs-app-router-patterns): Master Next.js 14+ App Router with Server Components, streaming, parallel routes, and advanced data fetching. Use when building Next.js applications, implementi
- [react-state-management](https://doutorai.pt/en/components/skills__wshobson-agents__react-state-management): Master modern React state management with Redux Toolkit, Zustand, Jotai, and React Query. Use when setting up global state, managing server state, or choosing b
- [react-native-architecture](https://doutorai.pt/en/components/skills__wshobson-agents__react-native-architecture): Build production React Native apps with Expo, navigation, native modules, offline sync, and cross-platform patterns. Use when developing mobile apps, implementi
- [postmortem-writing](https://doutorai.pt/en/components/skills__wshobson-agents__postmortem-writing): Write effective blameless postmortems with root cause analysis, timelines, and action items. Use when conducting incident reviews, writing postmortem documents,
- [on-call-handoff-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__on-call-handoff-patterns): Master on-call shift handoffs with context transfer, escalation procedures, and documentation. Use this skill when transitioning on-call responsibilities betwee
- [incident-runbook-templates](https://doutorai.pt/en/components/skills__wshobson-agents__incident-runbook-templates): Create structured incident response runbooks with step-by-step procedures, escalation paths, and recovery actions. Use this skill when building a service outage
- [react-native-design](https://doutorai.pt/en/components/skills__wshobson-agents__react-native-design): Master React Native styling, navigation, and Reanimated animations for cross-platform mobile development. Use when building React Native apps, implementing navi
- [mobile-ios-design](https://doutorai.pt/en/components/skills__wshobson-agents__mobile-ios-design): Master iOS Human Interface Guidelines and SwiftUI patterns for building native iOS apps. Use when designing iOS interfaces, implementing SwiftUI views, or ensur
- [design-system-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__design-system-patterns): Build scalable design systems with design tokens, theming infrastructure, and component architecture patterns. Use when creating design tokens, implementing the
- [responsive-design](https://doutorai.pt/en/components/skills__wshobson-agents__responsive-design): Implement modern responsive layouts using container queries, fluid typography, CSS Grid, and mobile-first breakpoint strategies. Use when building adaptive inte
- [visual-design-foundations](https://doutorai.pt/en/components/skills__wshobson-agents__visual-design-foundations): Apply typography, color theory, spacing systems, and iconography principles to create cohesive visual designs. Use when establishing design tokens, building sty
- [web-component-design](https://doutorai.pt/en/components/skills__wshobson-agents__web-component-design): Master React, Vue, and Svelte component patterns including CSS-in-JS, composition strategies, and reusable component architecture. Use when building UI componen
- [interaction-design](https://doutorai.pt/en/components/skills__wshobson-agents__interaction-design): Design and implement microinteractions, motion design, transitions, and user feedback patterns. Use when adding polish to UI interactions, implementing loading 
- [accessibility-compliance](https://doutorai.pt/en/components/skills__wshobson-agents__accessibility-compliance): Implement WCAG 2.2 compliant interfaces with mobile accessibility, inclusive design patterns, and assistive technology support. Use when auditing accessibility,
- [mobile-android-design](https://doutorai.pt/en/components/skills__wshobson-agents__mobile-android-design): Master Material Design 3 and Jetpack Compose patterns for building native Android apps. Use when designing Android interfaces, implementing Compose UI, or follo
- [llm-evaluation](https://doutorai.pt/en/components/skills__wshobson-agents__llm-evaluation): Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, 
- [hybrid-search-implementation](https://doutorai.pt/en/components/skills__wshobson-agents__hybrid-search-implementation): Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides su
- [similarity-search-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__similarity-search-patterns): Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval p
- [rag-implementation](https://doutorai.pt/en/components/skills__wshobson-agents__rag-implementation): Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, 
- [langchain-architecture](https://doutorai.pt/en/components/skills__wshobson-agents__langchain-architecture): Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI ag
- [vector-index-tuning](https://doutorai.pt/en/components/skills__wshobson-agents__vector-index-tuning): Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search 
- [embedding-strategies](https://doutorai.pt/en/components/skills__wshobson-agents__embedding-strategies): Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizi
- [bash-defensive-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__bash-defensive-patterns): Master defensive Bash programming techniques for production-grade scripts. Use when writing robust shell scripts, CI/CD pipelines, or system utilities requiring
- [bats-testing-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__bats-testing-patterns): Master Bash Automated Testing System (Bats) for comprehensive shell script testing. Use when writing tests for shell scripts, CI/CD pipelines, or requiring test
- [shellcheck-configuration](https://doutorai.pt/en/components/skills__wshobson-agents__shellcheck-configuration): Master ShellCheck static analysis configuration and usage for shell script quality. Use when setting up linting infrastructure, fixing code issues, or ensuring 
- [prometheus-configuration](https://doutorai.pt/en/components/skills__wshobson-agents__prometheus-configuration): Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, set
- [grafana-dashboards](https://doutorai.pt/en/components/skills__wshobson-agents__grafana-dashboards): Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualiz
- [slo-implementation](https://doutorai.pt/en/components/skills__wshobson-agents__slo-implementation): Define and implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targ
- [distributed-tracing](https://doutorai.pt/en/components/skills__wshobson-agents__distributed-tracing): Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservice
- [signed-audit-trails-recipe](https://doutorai.pt/en/components/skills__wshobson-agents__signed-audit-trails-recipe): Step-by-step cookbook for setting up cryptographically signed audit trails on Claude Code tool calls. Use when explaining, evaluating, or demonstrating the patt
- [file-conversion](https://doutorai.pt/en/components/skills__wshobson-agents__file-conversion): Convert files between formats - PDF to Word, HEIC to JPG, MP4 to MP3, CSV to JSON, EPUB to MOBI, and 999 total routes across images, video, audio, documents, da
- [spark-optimization](https://doutorai.pt/en/components/skills__wshobson-agents__spark-optimization): Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or sc
- [airflow-dag-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__airflow-dag-patterns): Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workfl
- [dbt-transformation-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__dbt-transformation-patterns): Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data trans
- [data-quality-frameworks](https://doutorai.pt/en/components/skills__wshobson-agents__data-quality-frameworks): Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rule
- [before-you-build](https://doutorai.pt/en/components/skills__wshobson-agents__before-you-build): Pre-build product and feature risk review for founders, product managers, and AI-assisted builders. Use this skill when the user is about to build a landing pag
- [risk-metrics-calculation](https://doutorai.pt/en/components/skills__wshobson-agents__risk-metrics-calculation): Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or bu
- [backtesting-frameworks](https://doutorai.pt/en/components/skills__wshobson-agents__backtesting-frameworks): Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing t
- [nft-standards](https://doutorai.pt/en/components/skills__wshobson-agents__nft-standards): Implement NFT standards (ERC-721, ERC-1155) with proper metadata handling, minting strategies, and marketplace integration. Use when creating NFT contracts, bui
- [defi-protocol-templates](https://doutorai.pt/en/components/skills__wshobson-agents__defi-protocol-templates): Implement DeFi protocols with production-ready templates for staking, AMMs, governance, and flash loans. Use when building decentralized finance applications or
- [solidity-security](https://doutorai.pt/en/components/skills__wshobson-agents__solidity-security): Master smart contract security best practices to prevent common vulnerabilities and implement secure Solidity patterns. Use when writing smart contracts, auditi
- [web3-testing](https://doutorai.pt/en/components/skills__wshobson-agents__web3-testing): Test smart contracts comprehensively using Hardhat and Foundry with unit tests, integration tests, and mainnet forking. Use when testing Solidity contracts, set
- [angular-migration](https://doutorai.pt/en/components/skills__wshobson-agents__angular-migration): Migrate from AngularJS to Angular using hybrid mode, incremental component rewriting, and dependency injection updates. Use when upgrading AngularJS application
- [database-migration](https://doutorai.pt/en/components/skills__wshobson-agents__database-migration): Execute database migrations across ORMs and platforms with zero-downtime strategies, data transformation, and rollback procedures. Use when migrating databases,
- [react-modernization](https://doutorai.pt/en/components/skills__wshobson-agents__react-modernization): Upgrade React applications to latest versions, migrate from class components to hooks, and adopt concurrent features. Use when modernizing React codebases, migr
- [dependency-upgrade](https://doutorai.pt/en/components/skills__wshobson-agents__dependency-upgrade): Manage major dependency version upgrades with compatibility analysis, staged rollout, and comprehensive testing. Use when upgrading framework versions, updating
- [competitive-landscape](https://doutorai.pt/en/components/skills__wshobson-agents__competitive-landscape): Analyze competition, identify differentiation opportunities, and develop winning market positioning strategies using Porter's Five Forces, Blue Ocean Strategy, 
- [team-composition-analysis](https://doutorai.pt/en/components/skills__wshobson-agents__team-composition-analysis): Design optimal team structures, hiring plans, compensation strategies, and equity allocation for early-stage startups from pre-seed through Series A. Use this s
- [startup-metrics-framework](https://doutorai.pt/en/components/skills__wshobson-agents__startup-metrics-framework): Track, calculate, and optimize key performance metrics for SaaS, marketplace, consumer, and B2B startups from seed through Series A, including unit economics, g
- [market-sizing-analysis](https://doutorai.pt/en/components/skills__wshobson-agents__market-sizing-analysis): Calculate TAM/SAM/SOM for market opportunities using top-down, bottom-up, and value theory methodologies. Use this skill when sizing markets, estimating address
- [startup-financial-modeling](https://doutorai.pt/en/components/skills__wshobson-agents__startup-financial-modeling): Build comprehensive 3-5 year financial models with revenue projections, cost structures, cash flow analysis, and scenario planning for early-stage startups. Use
- [scan](https://doutorai.pt/en/components/skills__wshobson-agents__scan): Scans the codebase to generate project-doc.md and AGENTS.md. Use when bootstrapping a new agent-driven repo, refreshing project documentation after architectura
- [dotnet-backend-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__dotnet-backend-patterns): Master C#/.NET backend development patterns for building robust APIs, MCP servers, and enterprise applications. Covers async/await, dependency injection, Entity
- [go-concurrency-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__go-concurrency-patterns): Master Go concurrency with goroutines, channels, sync primitives, and context. Use when building concurrent Go applications, implementing worker pools, or debug
- [rust-async-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__rust-async-patterns): Master Rust async programming with Tokio, async traits, error handling, and concurrent patterns. Use when building async Rust applications, implementing concurr
- [memory-safety-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__memory-safety-patterns): Implement memory-safe programming with RAII, ownership, smart pointers, and resource management across Rust, C++, and C. Use when writing safe systems code, man
- [team-composition-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__team-composition-patterns): Design optimal agent team compositions with sizing heuristics, preset configurations, and agent type selection. Use this skill when deciding how many agents to 
- [team-communication-protocols](https://doutorai.pt/en/components/skills__wshobson-agents__team-communication-protocols): Structured messaging protocols for agent team communication including message type selection, plan approval, shutdown procedures, and anti-patterns to avoid. Us
- [task-coordination-strategies](https://doutorai.pt/en/components/skills__wshobson-agents__task-coordination-strategies): Decompose complex tasks, design dependency graphs, and coordinate multi-agent work with proper task descriptions and workload balancing. Use this skill when bre
- [multi-reviewer-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__multi-reviewer-patterns): Coordinate parallel code reviews across multiple quality dimensions with finding deduplication, severity calibration, and consolidated reporting. Use this skill
- [parallel-debugging](https://doutorai.pt/en/components/skills__wshobson-agents__parallel-debugging): Debug complex issues using competing hypotheses with parallel investigation, evidence collection, and root cause arbitration. Use this skill when debugging bugs
- [parallel-feature-development](https://doutorai.pt/en/components/skills__wshobson-agents__parallel-feature-development): Coordinate parallel feature development with file ownership strategies, conflict avoidance rules, and integration patterns for multi-agent implementation. Use t
- [review-agent-setup](https://doutorai.pt/en/components/skills__wshobson-agents__review-agent-setup): Configure human-in-the-loop gating for AI agent review actions in Claude Code. Use when setting up a project where an agent may post PR reviews, comments, merge
- [attack-tree-construction](https://doutorai.pt/en/components/skills__wshobson-agents__attack-tree-construction): Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stak
- [threat-mitigation-mapping](https://doutorai.pt/en/components/skills__wshobson-agents__threat-mitigation-mapping): Map identified threats to appropriate security controls and mitigations. Use when prioritizing security investments, creating remediation plans, or validating c
- [sast-configuration](https://doutorai.pt/en/components/skills__wshobson-agents__sast-configuration): Configure Static Application Security Testing (SAST) tools for automated vulnerability detection in application code. Use when setting up security scanning, imp
- [stride-analysis-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__stride-analysis-patterns): Apply STRIDE methodology to systematically identify threats. Use when analyzing system security, conducting threat modeling sessions, or creating security docum
- [security-requirement-extraction](https://doutorai.pt/en/components/skills__wshobson-agents__security-requirement-extraction): Derive security requirements from threat models and business context. Use when translating threats into actionable requirements, creating security user stories,
- [postgresql-table-design](https://doutorai.pt/en/components/skills__wshobson-agents__postgresql): Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and adv
- [binary-analysis-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__binary-analysis-patterns): Master binary analysis patterns including disassembly, decompilation, control flow analysis, and code pattern recognition. Use when analyzing executables, under
- [anti-reversing-techniques](https://doutorai.pt/en/components/skills__wshobson-agents__anti-reversing-techniques): Understand anti-reversing, obfuscation, and protection techniques encountered during software analysis. Use this skill when analyzing malware evasion techniques
- [memory-forensics](https://doutorai.pt/en/components/skills__wshobson-agents__memory-forensics): Master memory forensics techniques including memory acquisition, process analysis, and artifact extraction using Volatility and related tools. Use when analyzin
- [protocol-reverse-engineering](https://doutorai.pt/en/components/skills__wshobson-agents__protocol-reverse-engineering): Master network protocol reverse engineering including packet analysis, protocol dissection, and custom protocol documentation. Use when analyzing network traffi
- [python-background-jobs](https://doutorai.pt/en/components/skills__wshobson-agents__python-background-jobs): Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-runn
- [python-error-handling](https://doutorai.pt/en/components/skills__wshobson-agents__python-error-handling): Python error handling patterns including input validation, exception hierarchies, and partial failure handling. Use when implementing validation logic, designin
- [python-project-structure](https://doutorai.pt/en/components/skills__wshobson-agents__python-project-structure): Python project organization, module architecture, and public API design. Use when setting up new projects, organizing modules, defining public interfaces with _
- [python-resilience](https://doutorai.pt/en/components/skills__wshobson-agents__python-resilience): Python resilience patterns including automatic retries, exponential backoff, timeouts, and fault-tolerant decorators. Use when adding retry logic, implementing 
- [python-testing-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__python-testing-patterns): Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, o
- [python-observability](https://doutorai.pt/en/components/skills__wshobson-agents__python-observability): Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting 
- [python-packaging](https://doutorai.pt/en/components/skills__wshobson-agents__python-packaging): Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creati
- [python-performance-optimization](https://doutorai.pt/en/components/skills__wshobson-agents__python-performance-optimization): Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, 
- [python-design-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__python-design-patterns): Python design patterns including KISS, Separation of Concerns, Single Responsibility, and composition over inheritance. Use this skill when designing a new serv
- [async-python-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__async-python-patterns): Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or 
- [uv-package-manager](https://doutorai.pt/en/components/skills__wshobson-agents__uv-package-manager): Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python proje
- [python-configuration](https://doutorai.pt/en/components/skills__wshobson-agents__python-configuration): Python configuration management via environment variables and typed settings. Use when externalizing config, setting up pydantic-settings, managing secrets, or 
- [python-code-style](https://doutorai.pt/en/components/skills__wshobson-agents__python-code-style): Python code style, linting, formatting, naming conventions, and documentation standards. Use when writing new code, reviewing style, configuring linters, writin
- [python-anti-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__python-anti-patterns): Use this skill when reviewing Python code for common anti-patterns to avoid. Use as a checklist when reviewing code, before finalizing implementations, or when 
- [python-type-safety](https://doutorai.pt/en/components/skills__wshobson-agents__python-type-safety): Python type safety with type hints, generics, protocols, and strict type checking. Use when adding type annotations, implementing generic classes, defining stru
- [python-resource-management](https://doutorai.pt/en/components/skills__wshobson-agents__python-resource-management): Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or b
- [auth-implementation-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__auth-implementation-patterns): Master authentication and authorization patterns including JWT, OAuth2, session management, and RBAC to build secure, scalable access control systems. Use when 
- [turborepo-caching](https://doutorai.pt/en/components/skills__wshobson-agents__turborepo-caching): Configure Turborepo for efficient monorepo builds with local and remote caching. Use when setting up Turborepo, optimizing build pipelines, or implementing dist
- [sql-optimization-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__sql-optimization-patterns): Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debug
- [code-review-excellence](https://doutorai.pt/en/components/skills__wshobson-agents__code-review-excellence): Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when 
- [git-advanced-workflows](https://doutorai.pt/en/components/skills__wshobson-agents__git-advanced-workflows): Master advanced Git workflows including rebasing, cherry-picking, bisect, worktrees, and reflog to maintain clean history and recover from any situation. Use wh
- [monorepo-management](https://doutorai.pt/en/components/skills__wshobson-agents__monorepo-management): Master monorepo management with Turborepo, Nx, and pnpm workspaces to build efficient, scalable multi-package repositories with optimized builds and dependency 
- [nx-workspace-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__nx-workspace-patterns): Configure and optimize Nx monorepo workspaces. Use when setting up Nx, configuring project boundaries, optimizing build caching, or implementing affected comman
- [debugging-strategies](https://doutorai.pt/en/components/skills__wshobson-agents__debugging-strategies): Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use whe
- [error-handling-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__error-handling-patterns): Master error handling patterns across languages including exceptions, Result types, error propagation, and graceful degradation to build resilient applications.
- [bazel-build-optimization](https://doutorai.pt/en/components/skills__wshobson-agents__bazel-build-optimization): Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codeb
- [e2e-testing-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__e2e-testing-patterns): Master end-to-end testing with Playwright and Cypress to build reliable test suites that catch bugs, improve confidence, and enable fast deployment. Use when im
- [unity-ecs-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__unity-ecs-patterns): Master Unity ECS (Entity Component System) with DOTS, Jobs, and Burst for high-performance game development. Use when building data-oriented games, optimizing p
- [godot-gdscript-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__godot-gdscript-patterns): Master Godot 4 GDScript patterns including signals, scenes, state machines, and optimization. Use when building Godot games, implementing game systems, or learn
- [data-storytelling](https://doutorai.pt/en/components/skills__wshobson-agents__data-storytelling): Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data r
- [kpi-dashboard-design](https://doutorai.pt/en/components/skills__wshobson-agents__kpi-dashboard-design): Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use this skill when building an executi
- [changelog-automation](https://doutorai.pt/en/components/skills__wshobson-agents__changelog-automation): Automate changelog generation from commits, PRs, and releases following Keep a Changelog format. Use when setting up release workflows, generating release notes
- [openapi-spec-generation](https://doutorai.pt/en/components/skills__wshobson-agents__openapi-spec-generation): Generate and maintain OpenAPI 3.1 specifications from code, design-first specs, and validation patterns. Use when creating API documentation, generating SDKs, o
- [architecture-decision-records](https://doutorai.pt/en/components/skills__wshobson-agents__architecture-decision-records): Write and maintain Architecture Decision Records (ADRs) following best practices for technical decision documentation. Use when documenting significant technica
- [protect-mcp-setup](https://doutorai.pt/en/components/skills__wshobson-agents__protect-mcp-setup): Configure Cedar policy enforcement and Ed25519 signed receipts for Claude Code tool calls. Use when setting up projects that need cryptographic audit trails, po
- [k8s-manifest-generator](https://doutorai.pt/en/components/skills__wshobson-agents__k8s-manifest-generator): Create production-ready Kubernetes manifests for Deployments, Services, ConfigMaps, and Secrets following best practices and security standards. Use when genera
- [gitops-workflow](https://doutorai.pt/en/components/skills__wshobson-agents__gitops-workflow): Implement GitOps workflows with ArgoCD and Flux for automated, declarative Kubernetes deployments with continuous reconciliation. Use when implementing GitOps p
- [k8s-security-policies](https://doutorai.pt/en/components/skills__wshobson-agents__k8s-security-policies): Implement Kubernetes security policies including NetworkPolicy, PodSecurityPolicy, and RBAC for production-grade security. Use when securing Kubernetes clusters
- [helm-chart-scaffolding](https://doutorai.pt/en/components/skills__wshobson-agents__helm-chart-scaffolding): Design, organize, and manage Helm charts for templating and packaging Kubernetes applications with reusable configurations. Use when creating Helm charts, packa
- [trace-to-training-data](https://doutorai.pt/en/components/skills__wshobson-agents__trace-to-training-data): Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become traini
- [checkpoint-promotion](https://doutorai.pt/en/components/skills__wshobson-agents__checkpoint-promotion): Gate fine-tuned checkpoints with drift budgets, paired comparison, and forgetting checks before promotion. Use after a training run produces a checkpoint, when 
- [dataset-curation](https://doutorai.pt/en/components/skills__wshobson-agents__dataset-curation): Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat temp
- [grpo-rlvr-training](https://doutorai.pt/en/components/skills__wshobson-agents__grpo-rlvr-training): Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically check
- [preference-optimization](https://doutorai.pt/en/components/skills__wshobson-agents__preference-optimization): Align a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO. Use when preference pairs or thumbs-up/down feedback exist, when choosing between 
- [vision-sft](https://doutorai.pt/en/components/skills__wshobson-agents__vision-sft): Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-visi
- [finetuning-method-selection](https://doutorai.pt/en/components/skills__wshobson-agents__finetuning-method-selection): Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fi
- [eval-harness-first](https://doutorai.pt/en/components/skills__wshobson-agents__eval-harness-first): Build the evaluation harness that gates every fine-tuning run - golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when sta
- [quantized-export](https://doutorai.pt/en/components/skills__wshobson-agents__quantized-export): Export a promoted fine-tuned model in the right deployment format - merged safetensors, LoRA-only, GGUF with imatrix, or FP8. Use after a checkpoint passes prom
- [lora-qlora-recipes](https://doutorai.pt/en/components/skills__wshobson-agents__lora-qlora-recipes): Configure LoRA and QLoRA supervised fine-tuning with current best-practice hyperparameters. Use when writing or reviewing a LoRA/QLoRA training configuration, c
- [cqrs-implementation](https://doutorai.pt/en/components/skills__wshobson-agents__cqrs-implementation): Implement Command Query Responsibility Segregation for scalable architectures. Use when separating read and write models, optimizing query performance, or build
- [workflow-orchestration-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__workflow-orchestration-patterns): Design durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constra
- [temporal-python-testing](https://doutorai.pt/en/components/skills__wshobson-agents__temporal-python-testing): Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setu
- [projection-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__projection-patterns): Build read models and projections from event streams. Use when implementing CQRS read sides, building materialized views, or optimizing query performance in eve
- [saga-orchestration](https://doutorai.pt/en/components/skills__wshobson-agents__saga-orchestration): Implement saga patterns for distributed transactions and cross-aggregate workflows. Use this skill when implementing distributed transactions across microservic
- [event-store-design](https://doutorai.pt/en/components/skills__wshobson-agents__event-store-design): Design and implement event stores for event-sourced systems. Use when building event sourcing infrastructure, choosing event store technologies, or implementing
- [architecture-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__architecture-patterns): Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use this skill when designing cle
- [microservices-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__microservices-patterns): Design microservices architectures with service boundaries, event-driven communication, and resilience patterns. Use when building distributed systems, decompos
- [api-design-principles](https://doutorai.pt/en/components/skills__wshobson-agents__api-design-principles): Master REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers. Use when designing new APIs, reviewin
- [multi-cloud-architecture](https://doutorai.pt/en/components/skills__wshobson-agents__multi-cloud-architecture): Design multi-cloud architectures using a decision framework to select and integrate services across AWS, Azure, GCP, and OCI. Use when building multi-cloud syst
- [istio-traffic-management](https://doutorai.pt/en/components/skills__wshobson-agents__istio-traffic-management): Configure Istio traffic management including routing, load balancing, circuit breakers, and canary deployments. Use when implementing service mesh traffic polic
- [mtls-configuration](https://doutorai.pt/en/components/skills__wshobson-agents__mtls-configuration): Configure mutual TLS (mTLS) for zero-trust service-to-service communication. Use when implementing zero-trust networking, certificate management, or securing in
- [terraform-module-library](https://doutorai.pt/en/components/skills__wshobson-agents__terraform-module-library): Build reusable Terraform modules for AWS, Azure, GCP, and OCI infrastructure following infrastructure-as-code best practices. Use when creating infrastructure m
- [hybrid-cloud-networking](https://doutorai.pt/en/components/skills__wshobson-agents__hybrid-cloud-networking): Configure secure, high-performance connectivity between on-premises infrastructure and cloud platforms using VPN and dedicated connections. Use when building hy
- [linkerd-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__linkerd-patterns): Implement Linkerd service mesh patterns for lightweight, security-focused service mesh deployments. Use when setting up Linkerd, configuring traffic policies, o
- [service-mesh-observability](https://doutorai.pt/en/components/skills__wshobson-agents__service-mesh-observability): Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debuggi
- [cost-optimization](https://doutorai.pt/en/components/skills__wshobson-agents__cost-optimization): Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reduc
- [gdpr-data-handling](https://doutorai.pt/en/components/skills__wshobson-agents__gdpr-data-handling): Implement GDPR-compliant data handling with consent management, data subject rights, and privacy by design. Use when building systems that process EU personal d
- [employment-contract-templates](https://doutorai.pt/en/components/skills__wshobson-agents__employment-contract-templates): Create employment contracts, offer letters, and HR policy documents following legal best practices. Use when drafting employment agreements, creating HR policie
- [track-management](https://doutorai.pt/en/components/skills__wshobson-agents__track-management): Use this skill when creating, managing, or working with Conductor tracks - the logical work units for features, bugs, and refactors. Applies to spec.md, plan.md
- [workflow-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__workflow-patterns): Use this skill when implementing tasks according to Conductor's TDD workflow, handling phase checkpoints, managing git commits for tasks, or understanding the v
- [pci-compliance](https://doutorai.pt/en/components/skills__wshobson-agents__pci-compliance): Implement PCI DSS compliance requirements for secure handling of payment card data and payment systems. Use when securing payment processing, achieving PCI comp
- [stripe-integration](https://doutorai.pt/en/components/skills__wshobson-agents__stripe-integration): Implement Stripe payment processing for robust, PCI-compliant payment flows including checkout, subscriptions, and webhooks. Use when integrating Stripe payment
- [billing-automation](https://doutorai.pt/en/components/skills__wshobson-agents__billing-automation): Build automated billing systems for recurring payments, invoicing, subscription lifecycle, and dunning management. Use when implementing subscription billing, a
- [paypal-integration](https://doutorai.pt/en/components/skills__wshobson-agents__paypal-integration): Integrate PayPal payment processing with support for express checkout, subscriptions, and refund management. Use when implementing PayPal payments, processing o
- [evaluation-methodology](https://doutorai.pt/en/components/skills__wshobson-agents__evaluation-methodology): PluginEval quality methodology - dimensions, rubrics, statistical methods, and scoring formulas. Use this skill when understanding how plugin quality is measure
- [recsys-pipeline-architect](https://doutorai.pt/en/components/skills__wshobson-agents__recsys-pipeline-architect): Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI
- [ml-pipeline-workflow](https://doutorai.pt/en/components/skills__wshobson-agents__ml-pipeline-workflow): Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementi
- [wcag-audit-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__wcag-audit-patterns): Conduct WCAG 2.2 accessibility audits with automated testing, manual verification, and remediation guidance. Use when auditing websites for accessibility, fixin
- [screen-reader-testing](https://doutorai.pt/en/components/skills__wshobson-agents__screen-reader-testing): Test web applications with screen readers including VoiceOver, NVDA, and JAWS. Use when validating screen reader compatibility, debugging accessibility issues, 
- [modern-javascript-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__modern-javascript-patterns): Master ES6+ features including async/await, destructuring, spread operators, arrow functions, promises, modules, iterators, generators, and functional programmi
- [nodejs-backend-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__nodejs-backend-patterns): Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, an
- [typescript-advanced-types](https://doutorai.pt/en/components/skills__wshobson-agents__typescript-advanced-types): Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applic
- [javascript-testing-patterns](https://doutorai.pt/en/components/skills__wshobson-agents__javascript-testing-patterns): Implement comprehensive testing strategies using Jest, Vitest, and Testing Library for unit tests, integration tests, and end-to-end testing with mocking, fixtu
- [spark-memory-thermal-ops](https://doutorai.pt/en/components/skills__wshobson-agents__spark-memory-thermal-ops): Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOM
- [spark-environment-setup](https://doutorai.pt/en/components/skills__wshobson-agents__spark-environment-setup): Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitt
- [spark-training-gotchas](https://doutorai.pt/en/components/skills__wshobson-agents__spark-training-gotchas): Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128G
- [memstack](https://doutorai.pt/en/components/skills__davepoon-subagents__memstack): 127 production skills across 10 categories (Security, Deployment, Dev, Business, Content, SEO, Marketing, Product, Automation, Core). Skills auto-load based on 
- [youtube-full](https://doutorai.pt/en/components/skills__davepoon-subagents__youtube-full): Use when YouTube is or could be relevant - video/channel/playlist links, video IDs, @handles, creator lookups, summaries, quotes, topic research, tutorials, tal
- [recap](https://doutorai.pt/en/components/skills__davepoon-subagents__recap): Triggered by "monthly recap", "how did I do this month", "spending summary", "financial review", "weekly recap", "quarterly review", "year in review
- [tidy](https://doutorai.pt/en/components/skills__davepoon-subagents__tidy): Triggered by "tidy up", "clean up transactions", "categorize uncategorized", "organize my transactions
- [about-atlantic-home-mortgage](https://doutorai.pt/en/components/skills__davepoon-subagents__about-atlantic-home-mortgage): Background information about Lendtrain powered by Atlantic Home Mortgage - company history, credentials, founder bio, and contact information for borrower trust
- [closing-costs](https://doutorai.pt/en/components/skills__davepoon-subagents__closing-costs): Calculates itemized state-specific closing costs for mortgage refinance transactions across 10 licensed states, with product-specific fees for Conventional, FHA
- [mortgage-loan-officer](https://doutorai.pt/en/components/skills__davepoon-subagents__mortgage-loan-officer): Guides borrowers through mortgage refinance evaluation - collects loan data, extracts mortgage statement fields, evaluates qualification, and delivers recommend
- [mortgage-compliance](https://doutorai.pt/en/components/skills__davepoon-subagents__mortgage-compliance): Enforces mortgage regulatory compliance - TRID, RESPA, TILA, ECOA/Fair Lending, state licensing, required disclosures, and data privacy rules for all borrower i
- [security-guardrails](https://doutorai.pt/en/components/skills__davepoon-subagents__security-guardrails): Adversarial defense layer for the mortgage plugin - protects against prompt injection, system prompt extraction, PII leakage, workflow bypass, and social engine
- [kegg-analysis](https://doutorai.pt/en/components/skills__davepoon-subagents__kegg-analysis): Multi-step KEGG bioinformatics workflows - pathway enrichment from gene lists, drug-target investigation, cross-species metabolic comparison, and compound-react
- [envelope-team](https://doutorai.pt/en/components/skills__davepoon-subagents__envelope-team): Design and generate .envelope.json AI agent team definitions - the open standard for multi-agent teams with hierarchy, access policies, human-in-the-loop gates,
- [close](https://doutorai.pt/en/components/skills__davepoon-subagents__close): Commit the session to durable vault memory and prepare a clean resume point
- [open](https://doutorai.pt/en/components/skills__davepoon-subagents__open): Boot the session, load context from the vault, and surface what matters
- [vuln](https://doutorai.pt/en/components/skills__davepoon-subagents__vuln): Look up a vulnerability by ID or list all vulnerabilities for a package
- [exploits-search](https://doutorai.pt/en/components/skills__davepoon-subagents__exploits-search): Search for exploits across all vulnerabilities with filtering by ecosystem, severity, source, and EPSS
- [fix](https://doutorai.pt/en/components/skills__davepoon-subagents__fix): Get fix intelligence for a vulnerability and propose concrete remediation for the current repository
- [dashboard](https://doutorai.pt/en/components/skills__davepoon-subagents__dashboard): View all tracked vulnerabilities and their current status
- [exploits](https://doutorai.pt/en/components/skills__davepoon-subagents__exploits): Analyze exploit intelligence for a vulnerability against the current repository
- [remediation](https://doutorai.pt/en/components/skills__davepoon-subagents__remediation): Get a context-aware remediation plan for a vulnerability with fix verification steps
- [package-search](https://doutorai.pt/en/components/skills__davepoon-subagents__package-search): Search for packages and assess security risk before adding as dependencies
- [drill-status](https://doutorai.pt/en/components/skills__davepoon-subagents__status): Show drill-me learning progress - topics studied, cards due for review, weakest concepts, and what to study next. Use when the user asks what's due, how their l
- [drill-me](https://doutorai.pt/en/components/skills__davepoon-subagents__me): Teach the user a topic as an adaptive tutor - retrieval practice, spaced repetition with decay, and persistent memory in ~/.drill-me/. Use when the user wants t
- [architecture](https://doutorai.pt/en/components/skills__davepoon-subagents__architecture): Architecture design skill with ADR records, system design checklists, scalability assessment, and architecture patterns
- [testing](https://doutorai.pt/en/components/skills__davepoon-subagents__testing): Testing strategies and methodologies including TDD, E2E testing, and multi-framework support
- [setup](https://doutorai.pt/en/components/skills__davepoon-subagents__setup): Interactive setup wizard for the claude-ops plugin. Installs missing CLIs, configures env vars for each channel (Telegram, WhatsApp, Email, Slack, Notion, Linea
- [ops-package](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-package): Ship parcels via any configured carrier - MyParcel, Sendcloud, DHL Parcel NL, PostNL, DPD, UPS, FedEx. Auto-selects the first carrier whose credentials are conf
- [ops](https://doutorai.pt/en/components/skills__davepoon-subagents__ops): Business operations command center. Routes to the right ops command based on what you need - briefing, inbox, fires, projects, comms, triage, linear, revenue, d
- [ops-merge](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-merge): Autonomous PR merge pipeline. Scans all repos for open PRs, dispatches subagents to fix CI, resolve conflicts, address review comments, then merges. Use --main 
- [ops-speedup](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-speedup): Cross-platform, hardware-adaptive system optimizer. Auto-detects macOS / Linux / WSL / Windows (MINGW/Cygwin/MSYS2) and CPU/RAM/disk/GPU profile, then picks the
- [ops-voice](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-voice): Voice operations - make phone calls (Bland AI), text-to-speech (ElevenLabs), transcribe audio (Whisper/Groq). Replace OpenClaw voice capabilities.
- [ops-integrate](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-integrate): Add any SaaS API as a first-class integration. Provide the service name - ops-integrate discovers auth patterns, tests connectivity, and registers the API in yo
- [ops-daemon](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-daemon): Check claude-ops background daemon end-to-end and auto-fix common issues. Detects stale plist paths after plugin upgrades, missing service commands, dead proces
- [ops-whatsapp-biz](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-whatsapp-biz): WhatsApp Business Cloud API - send approved template messages at scale, manage templates with approval tracking, and integrate product catalogs. Separate from w
- [ops-dash](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-dash): Interactive pixel-art command center dashboard. Visual business HQ with instant hotkey navigation to all ops commands, live status indicators, fire alerts, C-su
- [ops-revenue](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-revenue): Revenue and costs tracker. AWS spend via aws ce, credits tracker, project revenue stages. Shows burn rate, runway estimate, credits expiring.
- [uninstall](https://doutorai.pt/en/components/skills__davepoon-subagents__uninstall): Completely remove claude-ops plugin, all stored credentials, cached files, shell exports, and MCP registrations. Confirms each step before deletion.
- [ops-go](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-go): Token-efficient morning briefing. Pre-gathers all data via shell scripts, then presents a unified business dashboard with prioritized actions.
- [ops-settings](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-settings): Post-setup credential manager. Shows current integration status (configured/missing/expired) and lets you update individual credentials without re-running the f
- [ops-inbox](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-inbox): Full inbox management across all channels - WhatsApp (wacli), Email (Gmail MCP), Slack (MCP), Telegram (user-auth MCP), Discord (webhook + REST read), Notion (M
- [ops-fires](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-fires): Production incidents dashboard. Reads ECS health, Sentry errors, CI failures. Offers to dispatch fix agents for active fires.
- [ops-triage](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-triage): Cross-platform issue triage. Pulls from Sentry (MCP), Linear (MCP), GitHub Issues (gh). Cross-references against code to find already-fixed issues. Auto-resolve
- [ops-projects](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-projects): Portfolio dashboard for all GSD-tracked projects. Scans ~/Projects and ~/gsd-workspaces for .planning/ directories, shows phase status, git state, blockers, and
- [ops-next](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-next): Business-level "what should I do next". Priority stack - fires > unread comms > ready-to-merge PRs > Linear sprint > revenue-generating GSD work. Uses pre-gathe
- [ops-yolo](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-yolo): YOLO mode. Spawns 4 parallel C-suite agents (CEO, CTO, CFO, COO). Each analyzes the business from their perspective using ALL available data. Produces unfiltere
- [ops-deploy](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-deploy): Deploy status across all projects. Shows ECS service versions, Vercel deployments, recent deploys, pending deploys, and CI/CD pipeline state.
- [ops-monitor](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-monitor): Unified APM and monitoring surface. Polls Datadog, New Relic, and OpenTelemetry backends for active alerts, error traces, and entity health. Use --watch for liv
- [ops-status](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-status): Lightweight green/red status panel for every configured integration. No gather, no actions.
- [ops-marketing](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-marketing): Marketing command center. Email campaigns (Klaviyo), paid ads (Meta/Google), analytics (GA4), SEO, and social media metrics. One dashboard for all marketing cha
- [ops-linear](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-linear): Linear command center. Shows current sprint, creates/updates issues, manages priorities, syncs with GSD phases.
- [ops-doctor](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-doctor): Health check and auto-repair for the ops plugin. Diagnoses manifest errors, broken permissions, invalid configs, stale caches, and missing files - then spawns a
- [ops-orchestrate](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-orchestrate): Autonomous multi-project orchestration engine. Audits all registered projects, structures work into dependency-wired tasks, dispatches parallel agents (subagent
- [ops-gtm](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-gtm): Go-to-market strategy planner. Generates a complete GTM plan across paid, unpaid, marketing, sales, and AI-automation channels for any project - and hands execu
- [ops-comms](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-comms): Send and read messages across all channels. Routes based on arguments - whatsapp, email, slack, telegram, discord, notion, or natural language like "send [msg] 
- [ops-ecom](https://doutorai.pt/en/components/skills__davepoon-subagents__ops-ecom): Shopify store command center. Orders, inventory, fulfillment, analytics, and store health. Works with any Shopify store via Admin API.
- [ThumbGate - Pre-Action Gates for AI Coding Agents](https://doutorai.pt/en/components/skills__davepoon-subagents__thumbgate): ThumbGate adds deterministic pre-action gates to AI coding agents. Before any destructive operation executes, ThumbGate checks it against enforceable rules.
- [meeting](https://doutorai.pt/en/components/skills__davepoon-subagents__meeting): Convene a meeting of AI personas (3 to 10 participants) who debate a subject and reach a synthesis. Teams adapt to the theme (dev, design, product, business, li
- [Inspiration Analyzer](https://doutorai.pt/en/components/skills__davepoon-subagents__inspiration-analyzer): Analyze websites for design inspiration, extracting colors, typography, layouts, and patterns. Use when you have specific URLs to analyze for a design project.
- [Design Wizard](https://doutorai.pt/en/components/skills__davepoon-subagents__design-wizard): Interactive design wizard that guides through a complete frontend design process with discovery, aesthetic selection, and code generation. Use for creating dist
- [Typography Selector](https://doutorai.pt/en/components/skills__davepoon-subagents__typography-selector): Browse and select fonts from Google Fonts or curated pairings. Use to find the perfect typography for a design project.
- [Trend Researcher](https://doutorai.pt/en/components/skills__davepoon-subagents__trend-researcher): Research latest UI/UX trends from Dribbble and design communities. Use when starting a design project to understand current visual trends, color palettes, and l
- [Moodboard Creator](https://doutorai.pt/en/components/skills__davepoon-subagents__moodboard-creator): Create visual moodboards from collected inspiration with iterative refinement. Use after trend research or website analysis to synthesize design direction befor
- [Color Curator](https://doutorai.pt/en/components/skills__davepoon-subagents__color-curator): Browse and select color palettes from Coolors or curated fallbacks. Use to find the perfect color palette for a design project.
- [onboard](https://doutorai.pt/en/components/skills__davepoon-subagents__onboard): Set up CIAgent regression testing for the AI agent in this repo - write a runner, record golden baselines, generate a test spec, and verify it. Use when the use
- [check](https://doutorai.pt/en/components/skills__davepoon-subagents__check): Run CIAgent regression checks after changing an AI agent's code, prompts, or knowledge base in a repo that has agentci_spec.yaml, and interpret the results. Use
- [invoice-organizer](https://doutorai.pt/en/components/skills__davepoon-subagents__invoice-organizer): Automatically organizes invoices and receipts for tax preparation by reading messy files, extracting key information, renaming them consistently, and sorting th
- [canva-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__canva-automation): Automate Canva tasks via Rube MCP (Composio): designs, exports, folders, brand templates, autofill. Always search tools first for current schemas.
- [github-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__github-automation): Automate GitHub repositories, issues, pull requests, branches, CI/CD, and permissions via Rube MCP (Composio). Manage code workflows, review PRs, search code, a
- [make-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__make-automation): Automate Make (Integromat) tasks via Rube MCP (Composio): operations, enums, language and timezone lookups. Always search tools first for current schemas.
- [changelog-generator](https://doutorai.pt/en/components/skills__davepoon-subagents__changelog-generator): Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, 
- [image-enhancer](https://doutorai.pt/en/components/skills__davepoon-subagents__image-enhancer): Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documen
- [todoist-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__todoist-automation): Automate Todoist task management, projects, sections, filtering, and bulk operations via Rube MCP (Composio). Always search tools first for current schemas.
- [posthog-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__posthog-automation): Automate PostHog tasks via Rube MCP (Composio): events, feature flags, projects, user profiles, annotations. Always search tools first for current schemas.
- [aurakit](https://doutorai.pt/en/components/skills__davepoon-subagents__aurakit): Sonnet Amplified fullstack engine. 34 modes, SEC-01~15 OWASP security, 13 runtime hooks, 75% token reduction. Install: npx @smorky85/aurakit
- [routerbase-model-gateway](https://doutorai.pt/en/components/skills__davepoon-subagents__routerbase-model-gateway): Integrate and route AI model requests through RouterBase. Use when migrating OpenAI-compatible clients, choosing model IDs, configuring fallbacks, or building c
- [supabase-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__supabase-automation): Automate Supabase database queries, table management, project administration, storage, edge functions, and SQL execution via Rube MCP (Composio). Always search 
- [google-analytics-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__google-analytics-automation): Automate Google Analytics tasks via Rube MCP (Composio): run reports, list accounts/properties, funnels, pivots, key events. Always search tools first for curre
- [tracedocs](https://doutorai.pt/en/components/skills__davepoon-subagents__tracedocs): Turn any codebase into evidence-grounded Markdown docs plus a machine-readable index.json. Every claim cites its source; never invents deployment steps.
- [twitter-algorithm-optimizer](https://doutorai.pt/en/components/skills__davepoon-subagents__twitter-algorithm-optimizer): Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility 
- [confluence-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__confluence-automation): Automate Confluence page creation, content search, space management, labels, and hierarchy navigation via Rube MCP (Composio). Always search tools first for cur
- [coinpaprika-api](https://doutorai.pt/en/components/skills__davepoon-subagents__coinpaprika-api): Access cryptocurrency market data from CoinPaprika: prices, tickers, OHLCV, exchanges, contract lookups for 12,000+ coins and 350+ exchanges. Free tier, no API 
- [googlesheets-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__googlesheets-automation): Automate Google Sheets operations (read, write, format, filter, manage spreadsheets) via Rube MCP (Composio). Read/write data, manage tabs, apply formatting, an
- [competitive-ads-extractor](https://doutorai.pt/en/components/skills__davepoon-subagents__competitive-ads-extractor): Extracts and analyzes competitors' ads from ad libraries (Facebook, LinkedIn, etc.) to understand what messaging, problems, and creative approaches are working.
- [gitlab-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__gitlab-automation): Automate GitLab project management, issues, merge requests, pipelines, branches, and user operations via Rube MCP (Composio). Always search tools first for curr
- [square-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__square-automation): Automate Square tasks via Rube MCP (Composio): payments, orders, invoices, locations. Always search tools first for current schemas.
- [intercom-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__intercom-automation): Automate Intercom tasks via Rube MCP (Composio): conversations, contacts, companies, segments, admins. Always search tools first for current schemas.
- [figma-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__figma-automation): Automate Figma tasks via Rube MCP (Composio): files, components, design tokens, comments, exports. Always search tools first for current schemas.
- [amplitude-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__amplitude-automation): Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.
- [artifacts-builder](https://doutorai.pt/en/components/skills__davepoon-subagents__artifacts-builder): Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for
- [helpdesk-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__helpdesk-automation): Automate HelpDesk tasks via Rube MCP (Composio): list tickets, manage views, use canned responses, and configure custom fields. Always search tools first for cu
- [tweetclaw](https://doutorai.pt/en/components/skills__davepoon-subagents__tweetclaw): Use TweetClaw as an OpenClaw plugin for X/Twitter automation: search tweets, search tweet replies, post tweets/replies, export followers, look up users, handle 
- [json-canvas](https://doutorai.pt/en/components/skills__davepoon-subagents__json-canvas): Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections. Use when working with .canvas files, creating visual canvases, mind maps
- [pptx](https://doutorai.pt/en/components/skills__davepoon-subagents__pptx): Presentation creation, editing, and analysis. When Claude needs to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or e
- [calendly-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__calendly-automation): Automate Calendly scheduling, event management, invitee tracking, availability checks, and organization administration via Rube MCP (Composio). Always search to
- [macos-design](https://doutorai.pt/en/components/skills__davepoon-subagents__macos-design): Design and build native-feeling macOS application UIs. Use this skill whenever the user asks to create a desktop app, macOS app, Mac-style interface, Apple-styl
- [reddit-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__reddit-automation): Automate Reddit tasks via Rube MCP (Composio): search subreddits, create posts, manage comments, and browse top content. Always search tools first for current s
- [notion-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__notion-automation): Automate Notion tasks via Rube MCP (Composio): pages, databases, blocks, comments, users. Always search tools first for current schemas.
- [google-calendar-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__google-calendar-automation): Automate Google Calendar events, scheduling, availability checks, and attendee management via Rube MCP (Composio). Create events, find free slots, manage attend
- [meeting-insights-analyzer](https://doutorai.pt/en/components/skills__davepoon-subagents__meeting-insights-analyzer): Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict,
- [microsoft-teams-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__microsoft-teams-automation): Automate Microsoft Teams tasks via Rube MCP (Composio): send messages, manage channels, create meetings, handle chats, and search messages. Always search tools 
- [mixpanel-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__mixpanel-automation): Automate Mixpanel tasks via Rube MCP (Composio): events, segmentation, funnels, cohorts, user profiles, JQL queries. Always search tools first for current schem
- [datadog-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__datadog-automation): Automate Datadog tasks via Rube MCP (Composio): query metrics, search logs, manage monitors/dashboards, create events and downtimes. Always search tools first f
- [klaviyo-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__klaviyo-automation): Automate Klaviyo tasks via Rube MCP (Composio): manage email/SMS campaigns, inspect campaign messages, track tags, and monitor send jobs. Always search tools fi
- [lobsterdomains](https://doutorai.pt/en/components/skills__davepoon-subagents__lobsterdomains): Register ICANN domains with crypto payments (USDC/USDT/ETH/BTC) via API - built for AI agents
- [coding-agent-pm](https://doutorai.pt/en/components/skills__davepoon-subagents__coding-agent-pm): Plan, delegate, review, and recover coding-agent implementation work with tight scope control, acceptance criteria, and validation.
- [miro-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__miro-automation): Automate Miro tasks via Rube MCP (Composio): boards, items, sticky notes, frames, sharing, connectors. Always search tools first for current schemas.
- [zoom-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__zoom-automation): Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). Always search tools first for current schema
- [activecampaign-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__activecampaign-automation): Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first fo
- [google-drive-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__google-drive-automation): Automate Google Drive file operations (upload, download, search, share, organize) via Rube MCP (Composio). Upload/download files, manage folders, share with per
- [clawring](https://doutorai.pt/en/components/skills__davepoon-subagents__clawring): Phone calling skill for OpenClaw: agent makes real outbound phone calls to users for alerts, briefings, reminders, and urgent notifications. Managed service, no
- [trello-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__trello-automation): Automate Trello boards, cards, and workflows via Rube MCP (Composio). Create cards, manage lists, assign members, and search across boards programmatically.
- [clickup-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__clickup-automation): Automate ClickUp project management including tasks, spaces, folders, lists, comments, and team operations via Rube MCP (Composio). Always search tools first fo
- [convertkit-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__convertkit-automation): Automate ConvertKit (Kit) tasks via Rube MCP (Composio): manage subscribers, tags, broadcasts, and broadcast stats. Always search tools first for current schema
- [skill-share](https://doutorai.pt/en/components/skills__davepoon-subagents__skill-share): A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill discovery.
- [obsidian-rest-api](https://doutorai.pt/en/components/skills__davepoon-subagents__obsidian-rest-api): Call the Obsidian Local REST API directly (over HTTP) for vault operations the mcp__obsidian__* tools do NOT expose - move/rename a note, overwrite a whole file
- [salesforce-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__salesforce-automation): Automate Salesforce tasks via Rube MCP (Composio): leads, contacts, accounts, opportunities, SOQL queries. Always search tools first for current schemas.
- [webapp-testing](https://doutorai.pt/en/components/skills__davepoon-subagents__webapp-testing): Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing br
- [circleci-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__circleci-automation): Automate CircleCI tasks via Rube MCP (Composio): trigger pipelines, monitor workflows/jobs, retrieve artifacts and test metadata. Always search tools first for 
- [cal-com-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__cal-com-automation): Automate Cal.com tasks via Rube MCP (Composio): manage bookings, check availability, configure webhooks, and handle teams. Always search tools first for current
- [whatsapp-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__whatsapp-automation): Automate WhatsApp Business tasks via Rube MCP (Composio): send messages, manage templates, upload media, and handle contacts. Always search tools first for curr
- [immich-photo-manager](https://doutorai.pt/en/components/skills__davepoon-subagents__immich-photo-manager): Manage your self-hosted Immich photo library through conversation - natural language search, geographic album curation, duplicate detection, library health audi
- [dispatch](https://doutorai.pt/en/components/skills__davepoon-subagents__dispatch): Delegate tasks to OpenAI Codex CLI and Google Antigravity CLI from Claude Code. Say 'check with codex' or 'ask gemini for a second opinion' and Claude runs the 
- [linkedin-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__linkedin-automation): Automate LinkedIn tasks via Rube MCP (Composio): create posts, manage profile, company info, comments, and image uploads. Always search tools first for current 
- [freshservice-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__freshservice-automation): Automate Freshservice ITSM tasks via Rube MCP (Composio): create/update tickets, bulk operations, service requests, and outbound emails. Always search tools fir
- [ontoly-software-graph](https://doutorai.pt/en/components/skills__davepoon-subagents__ontoly-software-graph): Use Ontoly's deterministic Software Graph and MCP server for codebase architecture, request tracing, dependency analysis, and impact analysis.
- [pipedrive-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__pipedrive-automation): Automate Pipedrive CRM operations including deals, contacts, organizations, activities, notes, and pipeline management via Rube MCP (Composio). Always search to
- [developer-growth-analysis](https://doutorai.pt/en/components/skills__davepoon-subagents__developer-growth-analysis): Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from
- [gmail-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__gmail-automation): Automate Gmail tasks via Rube MCP (Composio): send/reply, search, labels, drafts, attachments. Always search tools first for current schemas.
- [dropbox-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__dropbox-automation): Automate Dropbox file management, sharing, search, uploads, downloads, and folder operations via Rube MCP (Composio). Always search tools first for current sche
- [outlook-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__outlook-automation): Automate Outlook tasks via Rube MCP (Composio): emails, calendar, contacts, folders, attachments. Always search tools first for current schemas.
- [telegram-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__telegram-automation): Automate Telegram tasks via Rube MCP (Composio): send messages, manage chats, share photos/documents, and handle bot commands. Always search tools first for cur
- [gingiris-growth-playbooks](https://doutorai.pt/en/components/skills__davepoon-subagents__gingiris-growth-playbooks): Open-source growth playbooks for AI products, B2B SaaS, and developer tools. Covers Product Hunt launch, GitHub star growth, KOL/UGC strategy, ASO, and communit
- [postmark-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__postmark-automation): Automate Postmark email delivery tasks via Rube MCP (Composio): send templated emails, manage templates, monitor delivery stats and bounces. Always search tools
- [submit-to-agentlaunch](https://doutorai.pt/en/components/skills__davepoon-subagents__submit-to-agentlaunch): Submit an AI agent to agentlaunch (a Product Hunt-style directory for AI agents) and read the directory via a public, no-auth REST API. Use when listing, launch
- [youtube-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__youtube-automation): Automate YouTube tasks via Rube MCP (Composio): upload videos, manage playlists, search content, get analytics, and handle comments. Always search tools first f
- [oiloil-ui-ux-guide](https://doutorai.pt/en/components/skills__davepoon-subagents__oiloil-ui-ux-guide): Modern, clean UI/UX guidance + review skill. Use when you need actionable UX/UI recommendations, design principles, or a design review checklist for new feature
- [mailchimp-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__mailchimp-automation): Automate Mailchimp email marketing including campaigns, audiences, subscribers, segments, and analytics via Rube MCP (Composio). Always search tools first for c
- [morning-ai](https://doutorai.pt/en/components/skills__davepoon-subagents__morning-ai): AI news tracking skill that monitors 80+ entities across 6 free sources (Reddit, HN, GitHub, HuggingFace, arXiv, X/Twitter). Generates scored daily reports with
- [skill-creator](https://doutorai.pt/en/components/skills__davepoon-subagents__skill-creator): Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabil
- [asana-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__asana-automation): Automate Asana tasks via Rube MCP (Composio): tasks, projects, sections, teams, workspaces. Always search tools first for current schemas.
- [pagerduty-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__pagerduty-automation): Automate PagerDuty tasks via Rube MCP (Composio): manage incidents, services, schedules, escalation policies, and on-call rotations. Always search tools first f
- [nika](https://doutorai.pt/en/components/skills__davepoon-subagents__nika): Runs repeatable AI work as checked, budgeted workflow files. The agent captures a repeated task as a .nika.yaml DAG, audits cost/permits/schema before a single 
- [docusign-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__docusign-automation): Automate DocuSign tasks via Rube MCP (Composio): templates, envelopes, signatures, document management. Always search tools first for current schemas.
- [hubspot-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__hubspot-automation): Automate HubSpot CRM operations (contacts, companies, deals, tickets, properties) via Rube MCP using Composio integration.
- [internal-comms](https://doutorai.pt/en/components/skills__davepoon-subagents__internal-comms): A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever 
- [content-research-writer](https://doutorai.pt/en/components/skills__davepoon-subagents__content-research-writer): Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on ea
- [obsidian-markdown](https://doutorai.pt/en/components/skills__davepoon-subagents__obsidian-markdown): Create and edit Obsidian Flavored Markdown with wikilinks, embeds, callouts, properties, and other Obsidian-specific syntax. Use when working with .md files in 
- [box-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__box-automation): Automate Box cloud storage operations including file upload/download, search, folder management, sharing, collaborations, and metadata queries via Rube MCP (Com
- [basecamp-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__basecamp-automation): Automate Basecamp project management, to-dos, messages, people, and to-do list organization via Rube MCP (Composio). Always search tools first for current schem
- [file-organizer](https://doutorai.pt/en/components/skills__davepoon-subagents__file-organizer): Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating c
- [discord-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__discord-automation): Automate Discord tasks via Rube MCP (Composio): messages, channels, roles, webhooks, reactions. Always search tools first for current schemas.
- [raffle-winner-picker](https://doutorai.pt/en/components/skills__davepoon-subagents__raffle-winner-picker): Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.
- [outlook-calendar-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__outlook-calendar-automation): Automate Outlook Calendar tasks via Rube MCP (Composio): create events, manage attendees, find meeting times, and handle invitations. Always search tools first 
- [ios-hig-design-guide](https://doutorai.pt/en/components/skills__davepoon-subagents__ios-hig-design-guide): Build, update, and apply iOS design specifications using Apple Human Interface Guidelines (HIG) source data. Use when a task asks for iOS UI/UX rules, Apple des
- [render-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__render-automation): Automate Render tasks via Rube MCP (Composio): services, deployments, projects. Always search tools first for current schemas.
- [canvas-design](https://doutorai.pt/en/components/skills__davepoon-subagents__canvas-design): Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, d
- [stripe-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__stripe-automation): Automate Stripe tasks via Rube MCP (Composio): customers, charges, subscriptions, invoices, products, refunds. Always search tools first for current schemas.
- [xlsx](https://doutorai.pt/en/components/skills__davepoon-subagents__xlsx): Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work wit
- [zendesk-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__zendesk-automation): Automate Zendesk tasks via Rube MCP (Composio): tickets, users, organizations, replies. Always search tools first for current schemas.
- [slack-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__slack-automation): Automate Slack messaging, channel management, search, reactions, and threads via Rube MCP (Composio). Send messages, search conversations, manage channels/users
- [hermes-tweet](https://doutorai.pt/en/components/skills__davepoon-subagents__hermes-tweet): Use Hermes Tweet when a Claude Code workflow needs a source-aware handoff to Hermes Agent for X/Twitter research, account context, monitoring, or approval-gated
- [theme-factory](https://doutorai.pt/en/components/skills__davepoon-subagents__theme-factory): Toolkit for styling artifacts with a theme. These artifacts can be slides, docs, reportings, HTML landing pages, etc. There are 10 pre-set themes with colors/fo
- [pressreleasesonline](https://doutorai.pt/en/components/skills__davepoon-subagents__pressreleasesonline): Draft and publish AI-powered press releases - submit a URL + notes, get a live release page instantly. Free, no API key required.
- [x-twitter-scraper](https://doutorai.pt/en/components/skills__davepoon-subagents__x-twitter-scraper): X (Twitter) data extraction and monitoring via Xquik: tweet search, user lookup, follower extraction, giveaway draws, trending topics, account monitoring with w
- [video-downloader](https://doutorai.pt/en/components/skills__davepoon-subagents__video-downloader): Download YouTube videos with customizable quality and format options. Use this skill when the user asks to download, save, or grab YouTube videos. Supports vari
- [domain-name-brainstormer](https://doutorai.pt/en/components/skills__davepoon-subagents__domain-name-brainstormer): Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming an
- [cf-proxy](https://doutorai.pt/en/components/skills__davepoon-subagents__cf-proxy): Deploy a free VLESS proxy/VPN node on Cloudflare Pages using edgetunnel. Automates code download, UUID generation, Pages deployment, free domain registration (D
- [bamboohr-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__bamboohr-automation): Automate BambooHR tasks via Rube MCP (Composio): employees, time-off, benefits, dependents, employee updates. Always search tools first for current schemas.
- [tailored-resume-generator](https://doutorai.pt/en/components/skills__davepoon-subagents__tailored-resume-generator): Analyzes job descriptions and generates tailored resumes that highlight relevant experience, skills, and achievements to maximize interview chances
- [agent-analytics](https://doutorai.pt/en/components/skills__davepoon-subagents__agent-analytics): Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your
- [skyvern](https://doutorai.pt/en/components/skills__davepoon-subagents__skyvern): AI-powered browser automation - navigate sites, fill forms, extract structured data, log in with stored credentials, and build reusable multi-step workflows usi
- [twitter-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__twitter-automation): Automate Twitter/X tasks via Rube MCP (Composio): posts, search, users, bookmarks, lists, media. Always search tools first for current schemas.
- [coda-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__coda-automation): Automate Coda tasks via Rube MCP (Composio): manage docs, pages, tables, rows, formulas, permissions, and publishing. Always search tools first for current sche
- [pdf](https://doutorai.pt/en/components/skills__davepoon-subagents__pdf): Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to 
- [freshdesk-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__freshdesk-automation): Automate Freshdesk helpdesk operations including tickets, contacts, companies, notes, and replies via Rube MCP (Composio). Always search tools first for current
- [resemble-detect](https://doutorai.pt/en/components/skills__davepoon-subagents__resemble-detect): Deepfake detection and media safety - detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, an
- [vercel-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__vercel-automation): Automate Vercel tasks via Rube MCP (Composio): manage deployments, domains, DNS, env vars, projects, and teams. Always search tools first for current schemas.
- [airtable-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__airtable-automation): Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.
- [wrike-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__wrike-automation): Automate Wrike project management via Rube MCP (Composio): create tasks/folders, manage projects, assign work, and track progress. Always search tools first for
- [shopify-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__shopify-automation): Automate Shopify tasks via Rube MCP (Composio): products, orders, customers, inventory, collections. Always search tools first for current schemas.
- [docx](https://doutorai.pt/en/components/skills__davepoon-subagents__docx): Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude nee
- [dexpaprika-api](https://doutorai.pt/en/components/skills__davepoon-subagents__dexpaprika-api): Access DeFi data from DexPaprika: token prices, liquidity pools, OHLCV, transactions across 34+ blockchains and 30M+ pools. Free, no API key needed. Install MCP
- [webflow-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__webflow-automation): Automate Webflow CMS collections, site publishing, page management, asset uploads, and ecommerce orders via Rube MCP (Composio). Always search tools first for c
- [brand-guidelines](https://doutorai.pt/en/components/skills__davepoon-subagents__brand-guidelines): Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand color
- [sentry-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__sentry-automation): Automate Sentry tasks via Rube MCP (Composio): manage issues/events, configure alerts, track releases, monitor projects and teams. Always search tools first for
- [lead-research-assistant](https://doutorai.pt/en/components/skills__davepoon-subagents__lead-research-assistant): Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategie
- [close-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__close-automation): Automate Close CRM tasks via Rube MCP (Composio): create leads, manage calls/SMS, handle tasks, and track notes. Always search tools first for current schemas.
- [instagram-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__instagram-automation): Automate Instagram tasks via Rube MCP (Composio): create posts, carousels, manage media, get insights, and publishing limits. Always search tools first for curr
- [one-drive-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__one-drive-automation): Automate OneDrive file management, search, uploads, downloads, sharing, permissions, and folder operations via Rube MCP (Composio). Always search tools first fo
- [tycana](https://doutorai.pt/en/components/skills__davepoon-subagents__tycana): Persistent task management and productivity intelligence via MCP. Captures tasks from conversation, plans your day, tracks patterns, and gives personalized reco
- [sendgrid-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__sendgrid-automation): Automate SendGrid email operations including sending emails, managing contacts/lists, sender identities, templates, and analytics via Rube MCP (Composio). Alway
- [sequenzy-email-marketing](https://doutorai.pt/en/components/skills__davepoon-subagents__sequenzy-email-marketing): Agent guide for operating Sequenzy. Use when Codex needs to authenticate, inspect identity, manage subscribers, create or edit campaigns/sequences/templates, ge
- [szamlazz-invoicing](https://doutorai.pt/en/components/skills__davepoon-subagents__szamlazz-invoicing): Issue, cancel, and fetch Hungarian invoices via the szamlazz.hu Agent API. Handles VAT calculation, NAV taxpayer lookup, partner caching, and PDF generation. Us
- [brevo-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__brevo-automation): Automate Brevo (Sendinblue) tasks via Rube MCP (Composio): manage email campaigns, create/edit templates, track senders, and monitor campaign performance. Alway
- [socialclaw](https://doutorai.pt/en/components/skills__davepoon-subagents__socialclaw): Social media scheduling and publishing for AI agents. Use when the user wants to schedule posts, connect social accounts, upload media, or publish campaigns to 
- [slack-gif-creator](https://doutorai.pt/en/components/skills__davepoon-subagents__slack-gif-creator): Toolkit for creating animated GIFs optimized for Slack, with validators for size constraints and composable animation primitives. This skill applies when users 
- [bitbucket-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__bitbucket-automation): Automate Bitbucket repositories, pull requests, branches, issues, and workspace management via Rube MCP (Composio). Always search tools first for current schema
- [obsidian-bases](https://doutorai.pt/en/components/skills__davepoon-subagents__obsidian-bases): Create and edit Obsidian Bases (.base files) with views, filters, formulas, and summaries. Use when working with .base files, creating database-like views of no
- [linear-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__linear-automation): Automate Linear tasks via Rube MCP (Composio): issues, projects, cycles, teams, labels. Always search tools first for current schemas.
- [jira-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__jira-automation): Automate Jira tasks via Rube MCP (Composio): issues, projects, sprints, boards, comments, users. Always search tools first for current schemas.
- [segment-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__segment-automation): Automate Segment tasks via Rube MCP (Composio): track events, identify users, manage groups, page views, aliases, batch operations. Always search tools first fo
- [tiktok-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__tiktok-automation): Automate TikTok tasks via Rube MCP (Composio): upload/publish videos, post photos, manage content, and view user profiles/stats. Always search tools first for c
- [zoho-crm-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__zoho-crm-automation): Automate Zoho CRM tasks via Rube MCP (Composio): create/update records, search contacts, manage leads, and convert leads. Always search tools first for current 
- [monday-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__monday-automation): Automate Monday.com work management including boards, items, columns, groups, subitems, and updates via Rube MCP (Composio). Always search tools first for curre
- [certificate-monitoring](https://doutorai.pt/en/components/skills__davepoon-subagents__certificate-monitoring): Use when the user asks about SSL/TLS certificates, certificate expiration, monitoring domains for cert health, or issuing free Let's Encrypt certificates. Trigg
- [ralph-review-trio](https://doutorai.pt/en/components/skills__davepoon-subagents__ralph-review-trio): Run a sequential three-tier code review on a finished implementation branch - Haiku (surface) → Sonnet (logic) → Opus (deep). Restarts from Tier 1 on any tier f
- [spend](https://doutorai.pt/en/components/skills__davepoon-subagents__spend): Show current Claude Code spend by project and branch via BudgetClaw
- [build](https://doutorai.pt/en/components/skills__davepoon-subagents__build): Autonomous app-building skill - transforms plain-English descriptions into fully built, tested, and deployed applications via a 9-phase pipeline
- [feedoracle-compliance](https://doutorai.pt/en/components/skills__davepoon-subagents__compliance): MiCA compliance evidence and stablecoin risk scoring. Use when the user asks about stablecoin compliance, MiCA status, peg stability, or needs verifiable eviden
- [review-findings](https://doutorai.pt/en/components/skills__davepoon-subagents__review-findings): Addresses and fixes findings from a QA code review. Reads the review report, fixes critical and warning issues, and prepares for re-verification. Delegates to t
- [release-check](https://doutorai.pt/en/components/skills__davepoon-subagents__release-check): Pre-release verification checklist. Validates features, tests, docs, security, and quality gates before shipping. Delegates to the Centinela (QA) agent.
- [code-health](https://doutorai.pt/en/components/skills__davepoon-subagents__code-health): Scans the codebase for dead code, tech debt, outdated dependencies, and code quality issues. Delegates to the Centinela (QA) agent.
- [security-audit](https://doutorai.pt/en/components/skills__davepoon-subagents__security-audit): Deep security audit covering OWASP Top 10, authentication, authorization, data protection, dependency vulnerabilities, and secrets scanning. Delegates to the Ce
- [implement-feature](https://doutorai.pt/en/components/skills__davepoon-subagents__implement-feature): Implements a feature from its specification. Reads the spec, designs architecture, writes code and tests. Delegates to the Forja (Dev) agent.
- [feature-spec](https://doutorai.pt/en/components/skills__davepoon-subagents__feature-spec): Creates a complete product feature specification with acceptance criteria, scope, dependencies, and risks. Delegates to the Prometeo (PM) agent.
- [fund-operations](https://doutorai.pt/en/components/skills__davepoon-subagents__fund-operations): Compute fund KPIs (TVPI, DPI, IRR, MOIC), model carried interest and management fees, and generate LP quarterly update narratives. Triggered by: "/venture-capit
- [explain-equity-terms](https://doutorai.pt/en/components/skills__davepoon-subagents__explain-equity-terms): Activate for ANY equity, legal, or term sheet question related to startup investing or fundraising. Triggers include: "what is a SAFE", "explain this term sheet
- [analyze-pitch-deck](https://doutorai.pt/en/components/skills__davepoon-subagents__analyze-pitch-deck): Activate for ANY pitch deck analysis, feedback, or review request. Triggers include: "analyze this deck", "review my pitch deck", "critique my pitch", "feedback
- [soft-screening-startup](https://doutorai.pt/en/components/skills__davepoon-subagents__soft-screening-startup): Activate for ANY startup evaluation, investment screening, or company assessment. Triggers include: "evaluate this startup", "screen this company", "should I in
- [market-size](https://doutorai.pt/en/components/skills__davepoon-subagents__market-size): Run TAM/SAM/SOM market sizing with top-down and bottom-up methods, competitive landscape, and tech stack analysis. Triggered by: "/venture-capital-intelligence:
- [financial-model](https://doutorai.pt/en/components/skills__davepoon-subagents__financial-model): Run deterministic financial models for startup valuation and SaaS health analysis. Triggered by: "/venture-capital-intelligence:financial-model", "run a financi
- [hard-screening-startup](https://doutorai.pt/en/components/skills__davepoon-subagents__hard-screening-startup): Deterministic Python-scored startup screening with full audit trail. Use when you need a reproducible, weighted-score verdict on a startup - not just a qualitat
- [cap-table-waterfall](https://doutorai.pt/en/components/skills__davepoon-subagents__cap-table-waterfall): Model cap table dilution, SAFE conversion, and exit waterfall across scenarios. Triggered by: "/venture-capital-intelligence:cap-table-waterfall", "model my cap
- [deal-sourcing-signals](https://doutorai.pt/en/components/skills__davepoon-subagents__deal-sourcing-signals): Scan a company or sector for deal-sourcing signals across 6 dimensions. Triggered by: "/venture-capital-intelligence:deal-sourcing-signals", "scan signals for X
- [using-startup-superpowers](https://doutorai.pt/en/components/skills__davepoon-subagents__using-startup-superpowers): Use at the start of any conversation about a startup idea, product validation, founder strategy, or work inside a `startup/` workspace. Establishes file convent
- [market-research](https://doutorai.pt/en/components/skills__davepoon-subagents__market-research): Manages the founder's market understanding - running initial market research, updating findings, and answering questions about market size, customer segments, b
- [interviews](https://doutorai.pt/en/components/skills__davepoon-subagents__interviews): Manages the founder's customer discovery interview lifecycle - drafting scripts tailored to a target segment, refining existing ones, managing script status, an
- [surveys](https://doutorai.pt/en/components/skills__davepoon-subagents__surveys): Manages the founder's survey-based validation - crafting the right questions, deploying a survey to the internet, and analyzing results against hypotheses. Use 
- [hypotheses](https://doutorai.pt/en/components/skills__davepoon-subagents__hypotheses): Manages the project's testable hypotheses - surfacing new ones, refining existing ones, updating status, reviewing the full set, and assessing hypothesis state 
- [mvp](https://doutorai.pt/en/components/skills__davepoon-subagents__mvp): Guides the founder through designing and optionally building the simplest MVP or prototype that validates their current hypotheses. Use when the founder wants t
- [whats-next](https://doutorai.pt/en/components/skills__davepoon-subagents__whats-next): Assesses the current state of the startup project and recommends what to focus on next. Use when there is a need or a question from the user to understand what 
- [competitors](https://doutorai.pt/en/components/skills__davepoon-subagents__competitors): Manages the founder's competitive landscape - discovering competitors, updating existing competitor files, adding new ones, and dispatching ad-hoc research. Use
- [auth-patterns](https://doutorai.pt/en/components/skills__davepoon-subagents__auth-patterns): This skill should be used when the user asks about "authentication in Next.js", "NextAuth", "Auth.js", "middleware auth", "protected routes", "session managemen
- [server-components](https://doutorai.pt/en/components/skills__davepoon-subagents__server-components): This skill should be used when the user asks about "Server Components", "Client Components", "'use client' directive", "when to use server vs client", "RSC patt
- [server-actions](https://doutorai.pt/en/components/skills__davepoon-subagents__server-actions): This skill should be used when the user asks about "Server Actions", "form handling in Next.js", "mutations", "useFormState", "useFormStatus", "revalidatePath",
- [route-handlers](https://doutorai.pt/en/components/skills__davepoon-subagents__route-handlers): This skill should be used when the user asks to "create an API route", "add an endpoint", "build a REST API", "handle POST requests", "create route handlers", "
- [app-router](https://doutorai.pt/en/components/skills__davepoon-subagents__app-router): This skill should be used when the user asks to "create a Next.js route", "add a page", "set up layouts", "implement loading states", "add error boundaries", "o
- [ultracost](https://doutorai.pt/en/components/skills__davepoon-subagents__ultracost): Quality-first per-stage model routing AND a pre-flight cost gate for Claude Code dynamic workflows. Use when authoring or running ultracode / dynamic-workflow s
- [work-status](https://doutorai.pt/en/components/skills__davepoon-subagents__work-status): Shows WORK progress and TASK status. Use when the user asks about WORK list, WORK progress, TASK status, or pipeline status (e.g., "WORK list", "WORK-01 progres
- [sdd-pipeline references](https://doutorai.pt/en/components/skills__davepoon-subagents__sdd-pipeline): 파이프라인 에이전트(specifier, planner, scheduler, builder, verifier, committer)가 참조하는 규칙/스키마 문서 모음.
- [uctm-init](https://doutorai.pt/en/components/skills__davepoon-subagents__init-80dda3): Initialize uc-taskmanager for the current project. Creates works/ directory and configures Bash permissions in .claude/settings.local.json. Use when the user sa
- [work-pipeline](https://doutorai.pt/en/components/skills__davepoon-subagents__work-pipeline): Triggers the WORK-PIPELINE when a user request starts with a [] tag (e.g., [new-feature], [bugfix], [WORK start]). Use this skill whenever you detect a [] tag a
- [hookradar-creative-intelligence](https://doutorai.pt/en/components/skills__davepoon-subagents__hookradar-creative-intelligence): Use when researching competitors, paid ads, Meta Ad Library creatives, TikTok ads, Instagram/TikTok organic videos, HookRadar MCP data, creative trends, AI crea
- [gsd:map-codebase](https://doutorai.pt/en/components/skills__davepoon-subagents__map-codebase): Analyze codebase with parallel mapper agents to produce .planning/codebase/ documents
- [gsd:analyze-dependencies](https://doutorai.pt/en/components/skills__davepoon-subagents__analyze-dependencies): Analyze phase dependencies and suggest Depends on entries for ROADMAP.md
- [gsd:import](https://doutorai.pt/en/components/skills__davepoon-subagents__import): Ingest external plans with conflict detection against project decisions before writing anything.
- [gsd:reapply-patches](https://doutorai.pt/en/components/skills__davepoon-subagents__reapply-patches): Reapply local modifications after a GSD update
- [gsd:quick](https://doutorai.pt/en/components/skills__davepoon-subagents__quick): Execute a quick task with GSD guarantees (atomic commits, state tracking) but skip optional agents
- [gsd:forensics](https://doutorai.pt/en/components/skills__davepoon-subagents__forensics): Post-mortem investigation for failed GSD workflows - analyzes git history, artifacts, and state to diagnose what went wrong
- [gsd:profile-user](https://doutorai.pt/en/components/skills__davepoon-subagents__profile-user): Generate developer behavioral profile and create Claude-discoverable artifacts
- [gsd:validate-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__validate-phase): Retroactively audit and fill Nyquist validation gaps for a completed phase
- [gsd:review-backlog](https://doutorai.pt/en/components/skills__davepoon-subagents__review-backlog): Review and promote backlog items to active milestone
- [gsd:spike-wrap-up](https://doutorai.pt/en/components/skills__davepoon-subagents__spike-wrap-up): Package spike findings into a persistent project skill for future build conversations
- [gsd:manager](https://doutorai.pt/en/components/skills__davepoon-subagents__manager): Interactive command center for managing multiple phases from one terminal
- [gsd:from-gsd2](https://doutorai.pt/en/components/skills__davepoon-subagents__from-gsd2): Import a GSD-2 (.gsd/) project back to GSD v1 (.planning/) format
- [gsd:ui-review](https://doutorai.pt/en/components/skills__davepoon-subagents__ui-review): Retroactive 6-pillar visual audit of implemented frontend code
- [gsd:settings](https://doutorai.pt/en/components/skills__davepoon-subagents__settings): Configure GSD workflow toggles and model profile
- [gsd:pr-branch](https://doutorai.pt/en/components/skills__davepoon-subagents__pr-branch): Create a clean PR branch by filtering out .planning/ commits - ready for code review
- [gsd:undo](https://doutorai.pt/en/components/skills__davepoon-subagents__undo): Safe git revert. Roll back phase or plan commits using the phase manifest with dependency checks.
- [gsd:ship](https://doutorai.pt/en/components/skills__davepoon-subagents__ship): Create PR, run review, and prepare for merge after verification passes
- [gsd:debug](https://doutorai.pt/en/components/skills__davepoon-subagents__debug): Systematic debugging with persistent state across context resets
- [gsd:pause-work](https://doutorai.pt/en/components/skills__davepoon-subagents__pause-work): Create context handoff when pausing work mid-phase
- [gsd:code-review](https://doutorai.pt/en/components/skills__davepoon-subagents__code-review): Review source files changed during a phase for bugs, security issues, and code quality problems
- [gsd:update](https://doutorai.pt/en/components/skills__davepoon-subagents__update): Update GSD to latest version with changelog display
- [gsd:execute-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__execute-phase): Execute all plans in a phase with wave-based parallelization
- [gsd:milestone-summary](https://doutorai.pt/en/components/skills__davepoon-subagents__milestone-summary): Generate a comprehensive project summary from milestone artifacts for team onboarding and review
- [gsd:new-project](https://doutorai.pt/en/components/skills__davepoon-subagents__new-project): Initialize a new project with deep context gathering and PROJECT.md
- [gsd:resume-work](https://doutorai.pt/en/components/skills__davepoon-subagents__resume-work): Resume work from previous session with full context restoration
- [gsd:mvp-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__mvp-phase): Plan an MVP-mode phase - captures an "As a / I want to / So that" user story, runs SPIDR splitting, then delegates to plan-phase
- [gsd:ui-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__ui-phase): Generate UI design contract (UI-SPEC.md) for frontend phases
- [gsd:review](https://doutorai.pt/en/components/skills__davepoon-subagents__review-e9781a): Request cross-AI peer review of phase plans from external AI CLIs
- [gsd:join-discord](https://doutorai.pt/en/components/skills__davepoon-subagents__join-discord): Join the GSD Discord community
- [gsd:verify-work](https://doutorai.pt/en/components/skills__davepoon-subagents__verify-work): Validate built features through conversational UAT
- [gsd:intel](https://doutorai.pt/en/components/skills__davepoon-subagents__intel): Query, inspect, or refresh codebase intelligence files in .planning/intel/
- [gsd:plant-seed](https://doutorai.pt/en/components/skills__davepoon-subagents__plant-seed): Capture a forward-looking idea with trigger conditions - surfaces automatically at the right milestone
- [gsd:session-report](https://doutorai.pt/en/components/skills__davepoon-subagents__session-report): Generate a session report with token usage estimates, work summary, and outcomes
- [gsd:note](https://doutorai.pt/en/components/skills__davepoon-subagents__note): Zero-friction idea capture. Append, list, or promote notes to todos.
- [gsd:autonomous](https://doutorai.pt/en/components/skills__davepoon-subagents__autonomous): Run all remaining phases autonomously - discuss→plan→execute per phase
- [gsd:spike](https://doutorai.pt/en/components/skills__davepoon-subagents__spike): Spike an idea through experiential exploration, or propose what to spike next (frontier mode)
- [gsd:sketch](https://doutorai.pt/en/components/skills__davepoon-subagents__sketch): Sketch UI/design ideas with throwaway HTML mockups, or propose what to sketch next (frontier mode)
- [gsd:explore](https://doutorai.pt/en/components/skills__davepoon-subagents__explore): Socratic ideation and idea routing - think through ideas before committing to plans
- [gsd:new-workspace](https://doutorai.pt/en/components/skills__davepoon-subagents__new-workspace): Create an isolated workspace with repo copies and independent .planning/
- [gsd:sketch-wrap-up](https://doutorai.pt/en/components/skills__davepoon-subagents__sketch-wrap-up): Package sketch design findings into a persistent project skill for future build conversations
- [gsd:next](https://doutorai.pt/en/components/skills__davepoon-subagents__next): Automatically advance to the next logical step in the GSD workflow
- [gsd:remove-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__remove-phase): Remove a future phase from roadmap and renumber subsequent phases
- [gsd:add-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__add-phase): Add phase to end of current milestone in roadmap
- [gsd:plan-milestone-gaps](https://doutorai.pt/en/components/skills__davepoon-subagents__plan-milestone-gaps): Create phases to close all gaps identified by milestone audit
- [gsd:set-profile](https://doutorai.pt/en/components/skills__davepoon-subagents__set-profile): Switch model profile for GSD agents (quality/balanced/budget/inherit)
- [gsd:list-workspaces](https://doutorai.pt/en/components/skills__davepoon-subagents__list-workspaces): List active GSD workspaces and their status
- [gsd:do](https://doutorai.pt/en/components/skills__davepoon-subagents__do): Route freeform text to the right GSD command automatically
- [gsd:ingest-docs](https://doutorai.pt/en/components/skills__davepoon-subagents__ingest-docs): Scan a repo for mixed ADRs, PRDs, SPECs, and DOCs and bootstrap or merge the full .planning/ setup from them. Classifies each doc in parallel, synthesizes a con
- [gsd:scan](https://doutorai.pt/en/components/skills__davepoon-subagents__scan): Rapid codebase assessment - lightweight alternative to /gsd:map-codebase
- [gsd:audit-uat](https://doutorai.pt/en/components/skills__davepoon-subagents__audit-uat): Cross-phase audit of all outstanding UAT and verification items
- [gsd:inbox](https://doutorai.pt/en/components/skills__davepoon-subagents__inbox): Triage and review all open GitHub issues and PRs against project templates and contribution guidelines
- [gsd:secure-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__secure-phase): Retroactively verify threat mitigations for a completed phase
- [gsd:new-milestone](https://doutorai.pt/en/components/skills__davepoon-subagents__new-milestone): Start a new milestone cycle - update PROJECT.md and route to requirements
- [gsd:check-todos](https://doutorai.pt/en/components/skills__davepoon-subagents__check-todos): List pending todos and select one to work on
- [gsd:eval-review](https://doutorai.pt/en/components/skills__davepoon-subagents__eval-review): Retroactively audit an executed AI phase's evaluation coverage - scores each eval dimension as COVERED/PARTIAL/MISSING and produces an actionable EVAL-REVIEW.md
- [gsd:audit-fix](https://doutorai.pt/en/components/skills__davepoon-subagents__audit-fix): Autonomous audit-to-fix pipeline - find issues, classify, fix, test, commit
- [gsd:resume-at](https://doutorai.pt/en/components/skills__davepoon-subagents__resume-at): Schedule a future resume of work - e.g. '/gsd:resume-at 09:00', '/gsd:resume-at +2h', or '/gsd:resume-at 04:00 --cmd /gsd:execute-phase 9
- [gsd:complete-milestone](https://doutorai.pt/en/components/skills__davepoon-subagents__complete-milestone): Archive completed milestone and prepare for next version
- [gsd:code-review-fix](https://doutorai.pt/en/components/skills__davepoon-subagents__code-review-fix): Auto-fix issues found by code review in REVIEW.md. Spawns fixer agent, commits each fix atomically, produces REVIEW-FIX.md summary.
- [gsd:research-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__research-phase): Research how to implement a phase (standalone - usually use /gsd:plan-phase instead)
- [gsd:fast](https://doutorai.pt/en/components/skills__davepoon-subagents__fast): Execute a trivial task inline - no subagents, no planning overhead
- [gsd:help](https://doutorai.pt/en/components/skills__davepoon-subagents__help-b22e4a): Show available GSD commands and usage guide
- [gsd:remove-workspace](https://doutorai.pt/en/components/skills__davepoon-subagents__remove-workspace): Remove a GSD workspace and clean up worktrees
- [gsd:spec-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__spec-phase): Socratic spec refinement - clarify WHAT a phase delivers with ambiguity scoring before discuss-phase. Produces a SPEC.md with falsifiable requirements locked be
- [gsd:ai-integration-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__ai-integration-phase): Generate AI design contract (AI-SPEC.md) for phases that involve building AI systems - framework selection, implementation guidance from official docs, and eval
- [gsd:progress](https://doutorai.pt/en/components/skills__davepoon-subagents__progress): Check project progress, show context, and route to next action (execute or plan). Use --forensic to append a 6-check integrity audit after the standard report.
- [gsd:cleanup](https://doutorai.pt/en/components/skills__davepoon-subagents__cleanup): Archive accumulated phase directories from completed milestones
- [gsd:list-phase-assumptions](https://doutorai.pt/en/components/skills__davepoon-subagents__list-phase-assumptions): Surface Claude's assumptions about a phase approach before planning
- [gsd:add-todo](https://doutorai.pt/en/components/skills__davepoon-subagents__add-todo): Capture idea or task as todo from current conversation context
- [gsd:audit-milestone](https://doutorai.pt/en/components/skills__davepoon-subagents__audit-milestone): Audit milestone completion against original intent before archiving
- [gsd:insert-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__insert-phase): Insert urgent work as decimal phase (e.g., 72.1) between existing phases
- [gsd:health](https://doutorai.pt/en/components/skills__davepoon-subagents__health): Diagnose planning directory health and optionally repair issues
- [gsd:stats](https://doutorai.pt/en/components/skills__davepoon-subagents__stats): Display project statistics - phases, plans, requirements, git metrics, and timeline
- [gsd:discuss-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__discuss-phase): Gather phase context through adaptive questioning before planning. Use --all to skip area selection and discuss all gray areas interactively. Use --auto to skip
- [gsd:ultraplan-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__ultraplan-phase): [BETA] Offload plan phase to Claude Code's ultraplan cloud - drafts remotely while terminal stays free, review in browser with inline comments, import back via 
- [gsd:add-backlog](https://doutorai.pt/en/components/skills__davepoon-subagents__add-backlog): Add an idea to the backlog parking lot (999.x numbering)
- [gsd:plan-phase](https://doutorai.pt/en/components/skills__davepoon-subagents__plan-phase): Create detailed phase plan (PLAN.md) with verification loop
- [gsd:workstreams](https://doutorai.pt/en/components/skills__davepoon-subagents__workstreams): Manage parallel workstreams - list, create, switch, status, progress, complete, and resume
- [gsd:thread](https://doutorai.pt/en/components/skills__davepoon-subagents__thread): Manage persistent context threads for cross-session work
- [gsd:graphify](https://doutorai.pt/en/components/skills__davepoon-subagents__graphify): Build, query, and inspect the project knowledge graph in .planning/graphs/
- [gsd:extract-learnings](https://doutorai.pt/en/components/skills__davepoon-subagents__extract-learnings): Extract decisions, lessons, patterns, and surprises from completed phase artifacts
- [gsd:docs-update](https://doutorai.pt/en/components/skills__davepoon-subagents__docs-update): Generate or update project documentation verified against the codebase
- [gsd:add-tests](https://doutorai.pt/en/components/skills__davepoon-subagents__add-tests): Generate tests for a completed phase based on UAT criteria and implementation
- [claude-docs-consultant](https://doutorai.pt/en/components/skills__centminmod-cc-setup__claude-docs-consultant): Consult official Claude Code documentation from code.claude.com using selective fetching. Use when working on hooks, skills, subagents, plugins, agent teams, MC
- [consult-codex](https://doutorai.pt/en/components/skills__centminmod-cc-setup__consult-codex): Dual-AI code analysis pairing OpenAI Codex GPT-5.6-terra with Claude code-searcher - the lightest consult variant, two citation-verified perspectives. Use for a
- [consult-zai](https://doutorai.pt/en/components/skills__centminmod-cc-setup__consult-zai): Dual-AI code analysis pairing z.ai GLM 5.2 with Claude code-searcher - a lightweight two-model second opinion. Use for a quick z.ai-backed check on a code quest
- [ai-image-creator](https://doutorai.pt/en/components/skills__centminmod-cc-setup__ai-image-creator): Generate, edit-from-reference, or analyze images with AI via OpenRouter (gemini, geminipro, riverflow, flux2, seedream, gpt5, gpt5.4; Cloudflare AI Gateway BYOK
- [ask-matt](https://doutorai.pt/en/components/skills__mattpocock-skills__ask-matt): Ask which skill or flow fits your situation. A router over the skills in this repo.
- [claude-handoff](https://doutorai.pt/en/components/skills__mattpocock-skills__claude-handoff): Hand the current conversation off to a fresh background agent that picks up the work immediately.
- [code-review](https://doutorai.pt/en/components/skills__mattpocock-skills__code-review): Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes - Standards (does the code follow this repo's documented coding stand
- [codebase-design](https://doutorai.pt/en/components/skills__mattpocock-skills__codebase-design): Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a se
- [diagnosing-bugs](https://doutorai.pt/en/components/skills__mattpocock-skills__diagnosing-bugs): Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
- [domain-modeling](https://doutorai.pt/en/components/skills__mattpocock-skills__domain-modeling): Build and sharpen a project's domain model. Use when the user wants to pin down domain terminology or a ubiquitous language, record an architectural decision, o
- [git-guardrails-claude-code](https://doutorai.pt/en/components/skills__mattpocock-skills__git-guardrails-claude-code): Set up Claude Code hooks to block dangerous git commands (push, reset --hard, clean, branch -D, etc.) before they execute. Use when user wants to prevent destru
- [grill-me](https://doutorai.pt/en/components/skills__mattpocock-skills__grill-me): A relentless interview to sharpen a plan or design.
- [grill-with-docs](https://doutorai.pt/en/components/skills__mattpocock-skills__grill-with-docs): A relentless interview to sharpen a plan or design, which also creates docs (ADR's and glossary) as we go.
- [grilling](https://doutorai.pt/en/components/skills__mattpocock-skills__grilling): Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.
- [handoff](https://doutorai.pt/en/components/skills__mattpocock-skills__handoff): Compact the current conversation into a handoff document for another agent to pick up.
- [implement](https://doutorai.pt/en/components/skills__mattpocock-skills__implement): Implement a piece of work based on a spec or set of tickets.
- [improve-codebase-architecture](https://doutorai.pt/en/components/skills__mattpocock-skills__improve-codebase-architecture): Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
- [loop-me](https://doutorai.pt/en/components/skills__mattpocock-skills__loop-me): Grill me about specs for the workflows I want to build, within this workspace.
- [migrate-to-shoehorn](https://doutorai.pt/en/components/skills__mattpocock-skills__migrate-to-shoehorn): Migrate test files from `as` type assertions to @total-typescript/shoehorn. Use when user mentions shoehorn, wants to replace `as` in tests, or needs partial te
- [prototype](https://doutorai.pt/en/components/skills__mattpocock-skills__prototype): Build a throwaway prototype to answer a design question. Use when the user wants to sanity-check whether a state model or logic feels right, or explore what a U
- [research](https://doutorai.pt/en/components/skills__mattpocock-skills__research): Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, d
- [resolving-merge-conflicts](https://doutorai.pt/en/components/skills__mattpocock-skills__resolving-merge-conflicts): Use when you need to resolve an in-progress git merge/rebase conflict.
- [scaffold-exercises](https://doutorai.pt/en/components/skills__mattpocock-skills__scaffold-exercises): Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exe
- [setup-matt-pocock-skills](https://doutorai.pt/en/components/skills__mattpocock-skills__setup-matt-pocock-skills): Configure this repo for the engineering skills - set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the othe
- [setup-pre-commit](https://doutorai.pt/en/components/skills__mattpocock-skills__setup-pre-commit): Set up Husky pre-commit hooks with lint-staged (Prettier), type checking, and tests in the current repo. Use when user wants to add pre-commit hooks, set up Hus
- [setup-ts-deep-modules](https://doutorai.pt/en/components/skills__mattpocock-skills__setup-ts-deep-modules): Wire dependency-cruiser into a TypeScript repo so each package is a deep module - implementation hidden in subfolders, reachable only through its entry-point fi
- [tdd](https://doutorai.pt/en/components/skills__mattpocock-skills__tdd): Test-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.
- [teach](https://doutorai.pt/en/components/skills__mattpocock-skills__teach): Teach the user a new skill or concept, within this workspace.
- [to-questionnaire](https://doutorai.pt/en/components/skills__mattpocock-skills__to-questionnaire): Turn a decision you can't fully answer into a questionnaire for someone else to fill in.
- [to-spec](https://doutorai.pt/en/components/skills__mattpocock-skills__to-spec): Turn the current conversation into a spec and publish it to the project issue tracker - no interview, just synthesis of what you've already discussed.
- [to-tickets](https://doutorai.pt/en/components/skills__mattpocock-skills__to-tickets): Break a plan, spec, or the current conversation into a set of tracer-bullet tickets, each declaring its blocking edges, published to the configured tracker - ed
- [triage](https://doutorai.pt/en/components/skills__mattpocock-skills__triage): Move issues and external PRs through a state machine of triage roles - categorise, verify, grill if needed, and write agent-ready briefs.
- [wait-what](https://doutorai.pt/en/components/skills__mattpocock-skills__wait-what): Stop. That last message did not land - re-pitch it.
- [wayfinder](https://doutorai.pt/en/components/skills__mattpocock-skills__wayfinder): Plan a huge chunk of work - more than one agent session can hold - as a shared map of decision tickets on your issue tracker, and resolve them one at a time unt
- [wizard](https://doutorai.pt/en/components/skills__mattpocock-skills__wizard): Generate an interactive bash wizard that walks a human through steps only they can perform. Use when provisioning infrastructure, setting up credentials or CI s
- [writing-beats](https://doutorai.pt/en/components/skills__mattpocock-skills__writing-beats): Writing, exploit - assemble raw material into a journey of beats, grounding each term before a beat leans on it.
- [writing-for-agents](https://doutorai.pt/en/components/skills__mattpocock-skills__writing-for-agents): Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.
- [writing-fragments](https://doutorai.pt/en/components/skills__mattpocock-skills__writing-fragments): Writing, explore - mine raw fragments, no structure yet.
- [writing-shape](https://doutorai.pt/en/components/skills__mattpocock-skills__writing-shape): Writing, exploit - shape raw material into an article, paragraph by paragraph.
- [diagnosing-superpowers](https://doutorai.pt/en/components/skills__obra-superpowers__diagnosing-superpowers): Use when a superpowers session went wrong and your human partner wants to know why - repeated work, ignored plans, stumbles, poor results, a skill that didn't f
- [cro-optimization](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__cro-optimization-ba9722): Run conversion rate optimization through hypothesis-driven testing including audit, hypothesis generation, test design, statistical analysis, and rollout decisi
- [seo-offpage](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-offpage-8e82f2): Plan and execute off-page SEO including link building, digital PR, brand mentions, citation building, and external authority signals. Use this skill whenever th
- [creative-brief](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__creative-brief-891f8f): Create or refine a creative brief that bridges discovery and execution. Use this skill whenever the user is starting a new project, kicking off a website, begin
- [journey-mapping](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__journey-mapping-ba21a0): Build customer journey maps and service blueprints that visualize the end-to-end user experience including touchpoints, emotions, friction, and underlying syste
- [scheduler-and-booking-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__scheduler-and-booking-design-d19873): Designing meeting schedulers and booking experiences that qualify leads, set up calls well, and convert at higher rates than a generic Calendly link. Availabili
- [launch-runbook](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__launch-runbook-79bc77): Plan and execute a launch runbook covering pre-launch verification, go-live procedures, DNS cutover, post-launch monitoring, and rollback procedures. Use this s
- [feature-flagging](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__feature-flagging-72c714): Operational discipline for feature flags as production infrastructure. Flag types, naming, targeting rules, rollout strategy, lifecycle, governance, stale flag 
- [editorial-qa](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__editorial-qa-3474ca): Pre-publish QA framework for content. Brief adherence, voice consistency, fact accuracy, structure and clarity, AI-content audit, SEO and AEO compliance, intern
- [okr-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__okr-design-15a1af): OKR design as actually shipped, not as conference-talk theory. Outcome statements that drive decisions, key results that measure the right thing, scoring discip
- [dependency-management](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__dependency-management-f59027): Manage third-party libraries, runtimes, and SaaS dependencies. Use this skill when setting an update cadence, responding to security advisories, dealing with de
- [creative-brief-selector](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__creative-brief-selector-fb4e4e): Produce a creative brief grounded in live reference sites and deliberately distinct from existing demos, for any new brand build. Composes with brand-archetype-
- [integration-orchestrator](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__integration-orchestrator-da0ef9): Generate a phased delivery orchestration plan for creative-direction-driven work: which skills run when, what locks at which gate, how handoffs occur, and how t
- [monitoring-and-alerting](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__monitoring-and-alerting-c13b4e): Design and run a monitoring system for a website or web app. Use this skill when setting up uptime checks, defining SLOs, configuring error tracking, choosing w
- [performance-optimization](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__performance-optimization-072179): Diagnose and fix web performance issues including Core Web Vitals (LCP, INP, CLS), bundle size, asset optimization, render performance, and runtime efficiency. 
- [brand-archetype-system](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__brand-archetype-system-b5791c): Apply pre-composed aesthetic archetype defaults to jumpstart brand design work covering color, typography, layout, voice, and imagery direction. Use this skill 
- [accessibility-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__accessibility-audit-2d4bda): Run a comprehensive WCAG accessibility audit covering perceivable, operable, understandable, and robust principles. Use this skill whenever the user wants to au
- [email-deliverability](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__email-deliverability-006257): Make sure email actually reaches inboxes. Use this skill when setting up email authentication (SPF, DKIM, DMARC), diagnosing emails landing in spam, planning a 
- [incident-response](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__incident-response-d1bfd5): Manage active production incidents through detection, triage, mitigation, communication, and resolution with structured roles and decision-making. Use this skil
- [seo-backlink-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-backlink-audit-c61832): Audit a backlink profile using Ahrefs MCP data: profile health, anchor text distribution, toxic link identification, lost link reclamation, and gap analysis aga
- [programmatic-seo](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__programmatic-seo-849514): How to design and run a programmatic SEO program that produces durable traffic instead of penalty-bait. Data source identification, template design, schema patt
- [ux-research](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__ux-research-2f530e): Plan and execute user research including research planning, recruiting, interview design, qualitative synthesis, and translating findings into product decisions
- [long-form-content-frameworks](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__long-form-content-frameworks-c5a53a): Patterns for individual long-form content pieces. Case studies, whitepapers, research reports, definitive guides, manifestos, ebooks, long-form tutorials. The s
- [content-brief-authoring](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-brief-authoring-1e8948): How to author a content brief that actually guides a writer (human or AI) to produce a piece that ranks, converts, or both. Per-piece editorial brief: target ke
- [creative-direction](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__creative-direction-16878f): Walk the user through four directional axes (tone register, aesthetic philosophy, audience relationship, sensory ambition) and produce a structured aesthetic br
- [experiment-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__experiment-design-a7e0f2): A discipline for designing experiments (A/B tests, multivariate, holdouts) so the results actually answer the question you asked. Hypothesis writing, sample siz
- [domain-strategy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__domain-strategy-d03345): Plan, manage, and optimize a domain portfolio. Use this skill for DNS architecture decisions, redirect strategies, registrar choice, parking unused domains, mul
- [experimentation-platform-orchestrator](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__experimentation-platform-orchestrator-b39282): A platform decision framework for experimentation. When to use Statsig vs PostHog vs GrowthBook vs Optimizely vs Amplitude vs Eppo vs Kameleoon. How to migrate 
- [comparison-tool-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__comparison-tool-design-fbcea6): Designing side-by-side comparison tools (plan-compare, product-compare, alternative-compare) that help users decide rather than just listing features. Axis sele
- [code-review-web](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__code-review-web-5e5e1c): Review web application code for bugs, security issues, performance problems, and stack-specific anti-patterns. Use this skill whenever the user wants to review 
- [media-asset-management](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__media-asset-management-1301f2): Plan and run a media pipeline for images, video, and downloadable assets. Use this skill when designing image storage and delivery, choosing formats (WebP, AVIF
- [information-architecture](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__information-architecture-e7fa0d): Design the structure of a website or product including sitemap, navigation, URL structure, content types, taxonomy, and labeling. Use this skill whenever the us
- [seo-traffic-diagnosis](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-traffic-diagnosis-aeb395): Diagnose organic traffic changes (drops, stalls, or unexpected wins) using Ahrefs MCP plus Search Console data. Use this skill when traffic suddenly dropped, ha
- [seo-technical](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-technical-d1f314): Run a comprehensive technical SEO audit covering crawlability, indexability, rendering, site architecture, structured data, page experience, security, and inter
- [multi-step-form-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__multi-step-form-design-c40b35): Designing forms with multiple steps, progress indicators, conditional logic, and save-and-resume mechanics. The discipline of breaking complex data collection i
- [seo-rank-tracking](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-rank-tracking-e22ba4): Set up and run rank tracking using Ahrefs MCP: pick the right keywords to track, segment them by purpose, set baselines, and define alert thresholds. Use this s
- [seo-content-gap-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-content-gap-audit-b14432): Audit content gaps and decay using Ahrefs MCP data: missing topics, thin coverage, outdated content, and decaying pages. Use this skill when planning a content 
- [jtbd-framing](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__jtbd-framing-b34d02): The Jobs-to-be-Done framework as applied product methodology. Job statements, struggling moments, hire and fire criteria, the difference between feature-thinkin
- [seo-keyword-gap-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-keyword-gap-audit-904c87): Find keywords competitors rank for that the target property does not, and prioritize them by opportunity. Uses Ahrefs MCP for keyword and competitor data. Use t
- [stakeholder-communication](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__stakeholder-communication-26c38d): Communicate effectively with stakeholders across functions and seniority levels. Use this skill when writing status updates, preparing executive reviews, sharin
- [vendor-evaluation](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__vendor-evaluation-3a5fa0): Evaluate, select, and contract with vendors and SaaS tools. Use this skill when comparing alternatives, running an RFP, scoring vendors against criteria, negoti
- [documentation-strategy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__documentation-strategy-ad3bab): Design and run a documentation system for a team or product. Use this skill when planning what to document, choosing a documentation tool, organizing existing d
- [lead-magnet-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__lead-magnet-design-a2f277): Designing lead magnets that earn the email. The discipline of building gated content (ebooks, templates, checklists, swipe files, mini-courses, free tools) that
- [evidence-based-reviews](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__evidence-based-reviews-37d606): Produce honest product reviews and buying guides without fabricated first-hand experience, using disclosed evidence tiers: verified specs, owner-experience synt
- [experimentation-analytics](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__experimentation-analytics-fd75cc): How to read experiment results without fooling yourself. Confidence intervals, p-values, multiple testing, sequential testing, CUPED, heterogeneous treatment ef
- [interactive-product-tour](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__interactive-product-tour-23506c): Designing in-product tours, tooltips, and contextual help that teach product capabilities without becoming friction. Trigger logic, tour architecture, contextua
- [competitor-experience-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__competitor-experience-audit-55a22a): Audit the brand, UX, and site design of the leading sites in a vertical as a set of observable cross-site patterns, producing the experience bar a new build mus
- [brand-style-guide](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__brand-style-guide-0da5b4): Build or audit a comprehensive brand style guide that documents the full brand system including story, logo system, color, typography, imagery, voice, applicati
- [brand-discovery](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__brand-discovery-6c4829): Run upstream brand discovery covering audience research, competitive landscape, category dynamics, problem space, and positioning territory exploration. Use thi
- [landing-page-copy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__landing-page-copy-7bb86c): Write landing page copy with attention to the hero, value proposition, social proof, objection handling, and conversion-focused CTAs. Use this skill whenever th
- [ads-performance-analytics](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__ads-performance-analytics-e8aca9): How to read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon tr
- [backup-and-disaster-recovery](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__backup-and-disaster-recovery-c5ec51): Plan and run backups, set recovery objectives, and run disaster recovery drills. Use this skill when defining RPO/RTO targets, designing backup architecture, de
- [user-feedback-aggregation](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__user-feedback-aggregation-ff60b5): Collecting and synthesizing user feedback across channels (support tickets, NPS, in-app feedback, sales calls, social mentions, customer councils) into a contin
- [analytics-strategy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__analytics-strategy-765377): Design measurement frameworks including event taxonomy, KPI hierarchy, dashboard architecture, attribution models, and analytics implementation strategy. Use th
- [seo-keyword](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-keyword-8a905a): Run keyword research, classify by search intent, cluster into topical groups, and prioritize for content production. Use this skill whenever the user asks to do
- [chatbot-flow-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__chatbot-flow-design-708454): Designing conversational flows for website chatbots and AI agents. Intent recognition architecture, branching logic, fallback handling, escalation to human, con
- [pm-spec-writing](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__pm-spec-writing-19255c): Translate ideas, feature requests, or vague concepts into specific, actionable dev briefs. Use this skill whenever the user has an idea they want to build, a fe
- [skill-creation-walkthrough](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__skill-creation-walkthrough-7680e2): Step-by-step guide for creating your own Claude Skills, from deciding whether a skill is the right tool to writing the SKILL.md file, structuring reference mate
- [onboarding-wizard-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__onboarding-wizard-design-f5d897): Designing first-run product onboarding wizards that get users to the ah-ha moment without overwhelming them. Step architecture, progressive disclosure, escape h
- [seo-aeo-geo](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-aeo-geo-dab3d3): Optimize content and site structure for AI-driven search experiences including AI overviews, large language model citations, generative answer engines, and AI a
- [paid-media-strategy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__paid-media-strategy-03f16f): A discipline for running paid media that does not light money on fire. Hypothesis writing for paid spend, channel selection, budget allocation, audience targeti
- [frontend-component-build](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__frontend-component-build-15f525): Build production-ready frontend components with accessible markup, sensible props, defined states, and tested behavior. Use this skill whenever the user wants t
- [team-onboarding-playbook](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__team-onboarding-playbook-19fb70): Design a structured onboarding experience that gets new team members productive in 30, 60, and 90 days. Use when a new hire is joining, when contractors or agen
- [security-baseline](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__security-baseline-141954): Establish a security baseline for a website or web app. Use this skill when configuring HTTPS and TLS, setting security headers, planning secrets management, ev
- [art-direction](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__art-direction-883d71): Direct visual and creative work for campaigns, photography, illustration, video, and branded experiences. Use this skill whenever the user wants to brief a phot
- [roadmap-planning](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__roadmap-planning-fc589b): Build a multi-quarter roadmap from a backlog of ideas, requests, and ongoing initiatives. Use this skill when planning the next quarter, sequencing dependent wo
- [seo-content-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-content-audit-56c346): Audit existing content across a site to decide what to keep, update, merge, redirect, or delete. Use this skill whenever the user wants to audit existing conten
- [content-and-copy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-and-copy-06d1cc): Write or edit website copy, blog content, and editorial pieces with attention to voice, structure, and goal. Use this skill whenever the user wants to write an 
- [content-distribution](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-distribution-526052): Content distribution as a discipline. Owned channels (newsletter, blog, social), earned channels (PR, syndication, mentions), paid channels (boosted posts, synd
- [discovery-research-synthesis](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__discovery-research-synthesis-a59c59): Turning research artifacts into actionable PM insight. Customer interviews, user research notes, support ticket reviews, sales call transcripts, survey data, in
- [upgrade-flow-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__upgrade-flow-design-06aaa0): Designing free-to-paid conversion flows for SaaS products. Trigger moments, paywall design, value demonstration, upsell vs downsell, win-back flows, churn preve
- [design-standards](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__design-standards-a1848e): Apply production-grade design standards when building or reviewing pages, components, or UI. Use this skill whenever the user asks to build a page, design a com
- [brand-ideation](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__brand-ideation-4f7103): Generate, evaluate, and narrow brand concepts during early ideation including positioning territories, naming candidates, mood directions, and narrative angles.
- [content-refresh-system](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-refresh-system-9bc81e): Systematic content refresh discipline. Quarterly audits, refresh prioritization (which pieces, when, how deep), refresh-vs-merge-vs-delete decisions, the lifecy
- [vertical-site-conventions](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__vertical-site-conventions-946312): Compose pages and sites that read as their vertical: ecommerce-catalog storefronts that look like storefronts, hospitality-food sites that look like restaurants
- [after-action-report](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__after-action-report-96bd37): Run a structured after-action review (postmortem, retrospective) on a launch, incident, or completed project to capture timeline, root cause analysis, contribut
- [funnel-flow-architecture](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__funnel-flow-architecture-8f0500): Architecting cross-tool conversion flows that match audience and stage. Landing page to lead magnet to nurture sequence to offer to advanced funnels. Honest abo
- [quiz-and-assessment-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__quiz-and-assessment-design-70d7bf): Designing quizzes, personality assessments, and recommendation tools that segment users into actionable categories rather than generating clicks for clicks' sak
- [seo-audit-orchestration](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-audit-orchestration-f6010b): Master orchestrator for a full SEO audit suite powered by the Ahrefs MCP. Use this skill when running a comprehensive SEO audit, scoping a quarterly health chec
- [usability-testing](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__usability-testing-1daa79): Plan and run usability tests on existing or prototype designs including test design, task scripts, moderation, observation, and findings synthesis. Use this ski
- [email-sequences](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__email-sequences-6c1308): Design and write email campaigns and sequences including onboarding flows, lifecycle campaigns, transactional emails, newsletters, broadcast sends, and launch a
- [data-warehouse-experimentation](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__data-warehouse-experimentation-09e77e): Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitio
- [form-strategy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__form-strategy-7bcd7d): Design forms that convert, validate well, resist spam, and integrate cleanly with downstream systems. Use this skill when designing or auditing any form (contac
- [ai-content-collaboration](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__ai-content-collaboration-c988d8): How humans and AI compose in content workflows. Where AI legitimately participates, where humans must own, hybrid workflow patterns, voice ownership preservatio
- [brand-identity](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__brand-identity-a15057): Design or evaluate a brand visual identity system covering logo, color, typography, imagery direction, iconography, and motion principles. Use this skill whenev
- [ads-creative-development](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__ads-creative-development-0b4411): How to produce ad creative that converts at performance scale. Hook patterns, format selection, video pacing, variation systems, sequential testing methodology,
- [feature-launch-playbook](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__feature-launch-playbook-99f90e): The operational playbook for launching a feature well. Positioning, internal alignment, customer comms, sales enablement, support readiness, rollout strategy, m
- [pillar-content-architecture](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__pillar-content-architecture-13485a): How to design a content hub that earns topical authority. Pillar topic selection, cluster planning, internal linking architecture, URL structure, pillar and clu
- [brand-voice](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__brand-voice-49cc28): Develop or document a complete brand voice and tone system covering voice attributes, tone shifts by context, vocabulary preferences, grammar rules, and copy ex
- [seo-competitor](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-competitor-5c6d48): Run a competitive SEO analysis comparing the user's site to chosen competitors across SERP overlap, content depth, backlink profiles, technical posture, and bra
- [product-configurator-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__product-configurator-design-cc0775): Designing build-your-own product configurators (Tesla-style, custom-pricing, plan-builders) with constraint logic, real-time pricing, validation, and save-and-s
- [calculator-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__calculator-design-293b6c): Designing interactive calculators (ROI calculators, pricing estimators, savings projections, mortgage calculators, custom assessments) that deliver real decisio
- [internationalization](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__internationalization-69d8bb): Plan and run a multi-language or multi-region site. Use this skill when adding new locales, choosing URL structure for languages (subfolders vs subdomains vs cc
- [content-migration](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-migration-e32e8c): Move content between platforms, domains, or URL structures while preserving SEO equity, user bookmarks, and integrations. Use this skill when planning a CMS mig
- [seo-onpage](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-onpage-da3ff5): Run a comprehensive on-page SEO audit or optimization pass covering title tags, meta descriptions, header structure, content quality, internal links, image opti
- [logo-design](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__logo-design-97f7b2): Generate logo variants for a brand, producing multiple production-grade marks across different architectures (wordmark, lockup, symbol-only, letterform-as-symbo
- [content-repurposing](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-repurposing-af84b5): Cross-format content adaptation. Turning one substantial piece into many derivative formats (blog series, email sequences, social posts, webinars, podcasts, vid
- [product-analytics-setup](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__product-analytics-setup-d2d06d): How to actually instrument product analytics correctly. Event taxonomy, property design, naming conventions, schema versioning, identity stitching, funnel desig
- [design-system](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__design-system-76c90e): Build or audit a design system including component library, design tokens, naming conventions, contribution model, and documentation. Use this skill whenever th
- [seo-site-health-audit](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__seo-site-health-audit-219963): Triage technical SEO findings from Ahrefs Site Audit (and similar crawlers) by SEO impact, not just severity. Use this skill when reviewing crawl results, prior
- [content-strategy](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__content-strategy-438dd1): Develop a content strategy covering editorial positioning, content pillars, formats, calendar, governance, and topical authority planning. Use this skill whenev
- [cost-optimization](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__cost-optimization-63c2be): Audit and reduce infrastructure and tooling costs without sacrificing reliability or velocity. Use this skill when reviewing monthly cloud or SaaS spend, findin
- [qa-testing](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__qa-testing-4ae211): Run QA testing on a page, feature, or full site at one of three depth tiers (smoke, standard, full). Use this skill whenever the user asks to QA a page or site,
- [beta-program-management](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__beta-program-management-e76a53): Running closed and open betas that produce real signal. Beta participant selection, structured feedback collection, beta-to-GA decision criteria, and the differ
- [evidence-based-reviews](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__evidence-based-reviews-d82713): Produce honest product reviews and buying guides without fabricated first-hand experience, using disclosed evidence tiers: verified specs, owner-experience synt
- [production-fast-api-template](https://doutorai.pt/en/components/skills__davepoon-subagents__production-fast-api-template): Structure and execution rules for a unified, production-ready backend combining domain-oriented FastAPI, Retrieval-Augmented Generation (RAG) and Agentic AI in 
- [de-ai-writer](https://doutorai.pt/en/components/skills__davepoon-subagents__de-ai-writer): AI 文案去味器。用户提供 AI 生成的文本（公众号/小红书/电商文案等），觉得“太 AI 味”要改得更自然、更像人写的时使用。输出自然有人味的中文。De-AI writer: rewrite AI-generated copy (WeChat articles, Xiaohongshu, e-commerce) in
- [atlas-cloud-media](https://doutorai.pt/en/components/skills__davepoon-subagents__atlas-cloud-media): Discover Atlas Cloud image and video models, inspect their live schemas, and submit one confirmed media generation request with bounded GET polling. Use when in
- [bulkpublish-social-scheduling](https://doutorai.pt/en/components/skills__davepoon-subagents__bulkpublish-social-scheduling): Schedule, publish and analyze social posts across 15 networks via BulkPublish (MCP or REST) - Facebook, Instagram, X, TikTok, YouTube, Threads, Bluesky, Pintere
- [agenttrace-session-audit](https://doutorai.pt/en/components/skills__davepoon-subagents__agenttrace-session-audit): Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.
- [hexanon-x402-apis](https://doutorai.pt/en/components/skills__davepoon-subagents__hexanon-x402-apis): Pay-per-call access to the Hexanon family of eight x402 APIs for autonomous agents - no signup, no API keys, USDC on Base. Covers US/Canada vehicle & VIN intell
- [rag-evaluation-harness](https://doutorai.pt/en/components/skills__davepoon-subagents__rag-evaluation-harness): Evaluate retrieval and citation behavior for RAG pipelines from deterministic JSONL fixtures. Use when an agent needs offline Recall@K, reciprocal rank, context
- [llm-crawler-access-check](https://doutorai.pt/en/components/skills__davepoon-subagents__llm-crawler-access-check): Check whether a website's robots.txt allows the AI crawlers that decide visibility in ChatGPT Search, Perplexity, Claude, Gemini, and Microsoft Copilot. Use whe
- [checkpointed-agent-loop](https://doutorai.pt/en/components/skills__davepoon-subagents__checkpointed-agent-loop): Run long or failure-prone Claude Code tasks as bounded, resumable loops with a durable state machine, attempt budget, and verification evidence checkpoint.
- [verify-watermark-removal](https://doutorai.pt/en/components/skills__davepoon-subagents__verify-watermark-removal): Verify that a watermark-removal or "humanizer" step actually removed a statistical text watermark, by measuring a sample whose key the user holds. Use after any
- [competitor-research](https://doutorai.pt/en/components/skills__davepoon-subagents__competitor-research): Research competitor products, launch history, and acquisition evidence from public sources or supplied materials. Use when evaluating growth claims, comparing c
- [ai-search-visibility-audit](https://doutorai.pt/en/components/skills__davepoon-subagents__ai-search-visibility-audit): Audit whether a website can be found, crawled, and cited by AI answer engines such as ChatGPT Search, Perplexity, Google AI Overviews, and Microsoft Copilot. Us
- [red-handed-audit](https://doutorai.pt/en/components/skills__davepoon-subagents__red-handed-audit): Check whether the tests the agent said passed actually ran. Reads the Claude Code session transcript and git state locally, then reports each gap with a timesta
- [you-web-search](https://doutorai.pt/en/components/skills__davepoon-subagents__you-web-search): Search the live web and read URLs through the You.com MCP server. Use when you need current information beyond training data: recent releases, docs, pricing, ne
- [postwire](https://doutorai.pt/en/components/skills__davepoon-subagents__postwire): Publish and schedule to TikTok, Instagram, YouTube, LinkedIn, X, Bluesky, Mastodon, Facebook, Threads, Reddit, Telegram and Discord through one PostWire API cal
- [upload-post](https://doutorai.pt/en/components/skills__davepoon-subagents__upload-post): Publish and schedule content to 15 social platforms through one Upload-Post API call: TikTok, Instagram, YouTube, LinkedIn, Facebook, X, Threads, Pinterest, Blu
- [anti-ui-slop](https://doutorai.pt/en/components/skills__davepoon-subagents__anti-ui-slop): Stop coding agents from shipping generic UI. Use UIZZE's 800,000+ real web and iOS screens to build product-specific interfaces, define a design contract, cover
- [voice-persona](https://doutorai.pt/en/components/skills__davepoon-subagents__voice-persona): 让 Agent 变成能语音对话的机器人：语音文件转文字（支持微信 silk 格式）+ 多音色人格回复（Edge TTS 免费中文音色），全本地零 API 成本。Voice persona chat: transcribe voice messages (incl. WeChat silk) and reply in p
- [gsc-seo-optimizer](https://doutorai.pt/en/components/skills__davepoon-subagents__gsc-seo-optimizer): Analyze Google Search Console exports or authorized API data to prioritize page improvements. Use for traffic changes, page/query opportunities, indexing eviden
- [muapi-media](https://doutorai.pt/en/components/skills__davepoon-subagents__muapi-media): Discover current MuAPI image models and run one explicitly confirmed asynchronous image generation request with bounded polling. Use when an agent needs hosted 
- [video-to-text](https://doutorai.pt/en/components/skills__davepoon-subagents__video-to-text): 视频转文字。用户发抖音分享链接（获取标题/作者/点赞等元数据），或本地视频文件（语音转写全文）时使用。抖音链接用 SSR 解析无需 Cookie。Video to text: extract metadata from Douyin share links, or transcribe speech from loca
- [apify-product-lookup](https://doutorai.pt/en/components/skills__davepoon-subagents__apify-product-lookup): Fetch a real product's current price, stock, rating, or images from retailer pages over the Apify MCP server, and return them as typed fields rather than prose.
- [agent-memory-discipline](https://doutorai.pt/en/components/skills__davepoon-subagents__agent-memory-discipline): Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MC
- [youtube-transcript](https://doutorai.pt/en/components/skills__davepoon-subagents__youtube-transcript): Use when the user wants a YouTube video's transcript fetched, wants to summarize/analyze/quote a YouTube video by its spoken content, wants to search YouTube (g
- [x-research](https://doutorai.pt/en/components/skills__davepoon-subagents__x-research): Search and inspect current public X posts, trends, profiles, profile posts, media, and replies through AgentBody. Use proactively whenever a user asks to search
- [ctrl-clip](https://doutorai.pt/en/components/skills__davepoon-subagents__ctrl-clip): ⧉ Copy a message to the system clipboard cleanly formatted - no blockquote pipes, no terminal bars, no Claude commentary. Use whenever you draft a message the u
- [project-milestone-tracker](https://doutorai.pt/en/components/skills__davepoon-subagents__project-milestone-tracker): 项目管理里程碑跟踪器（Project Milestone Tracker） - - 当用户要求代理在代码库/项目会话中规划、跟踪里程碑、记录承诺或事件、生成进度报告时使用。核心：三层里程碑规则（文件依据/客户提供/待确认）+ 原话+时间戳事件记录。Project milestone tracker: plan and 
- [doc-ocr](https://doutorai.pt/en/components/skills__davepoon-subagents__doc-ocr): 文档文字识别。用户提供 PDF/扫描件/图片（合同、发票、书页、截图），需要提取文字、转成可编辑文本时使用。扫描件自动 OCR（macOS Vision，中英文）。Document OCR: extract editable text from PDFs, scans, and images (contracts, i
- [jev-social](https://doutorai.pt/en/components/skills__davepoon-subagents__jev-social): Run browser-grounded social research without remote account mutations through Jev Social when a user wants posts, profiles, comments, video evidence, or a sourc
- [cohesivity](https://doutorai.pt/en/components/skills__davepoon-subagents__cohesivity): Backend and infrastructure through Cohesivity. Use only when the user names Cohesivity, a valid .cohesivity file exists, or the Cohesivity plugin or MCP is alre
- [ecommerce-material-studio](https://doutorai.pt/en/components/skills__davepoon-subagents__ecommerce-material-studio): 电商素材工坊（中英双语）。用户需要生成电商主图、详情图、场景图、产品图合成、品牌叠加时使用。自动识别品类→匹配风格→场景感知合成→统一文字→自动质检→多平台适配→标准化交付。E-commerce product image studio: category detection, style matching, scen
- [proposal-drafter](https://doutorai.pt/en/components/skills__davepoon-subagents__proposal-drafter): Draft a tailored freelance proposal or platform profile bio from a real job posting/brief and the freelancer's actual background. Use when responding to a listi
- [orca-replay](https://doutorai.pt/en/components/skills__davepoon-subagents__orca-replay): Answers questions about a past agent run from its recording rather than from memory, and replays or forks that run. Use when asked why an earlier run did someth
- [kavel-image-studio](https://doutorai.pt/en/components/skills__davepoon-subagents__kavel-image-studio): Turn a photo into a specific AI look (new hairstyle, collectible figurine, royal pet portrait, wedding photo, 90s yearbook, sticker, HD restore) or a photo into
- [dsh-deepread](https://doutorai.pt/en/components/skills__davepoon-subagents__dsh-deepread): 精读一本书或一篇文章：提取核心观点、论证结构与关键论据。当用户要求「精读/分析这篇文章」「提取核心观点」「梳理论证逻辑」「做知识地图」「用费曼读书法读书」时使用本技能。五种模式：quick 快速要点 / deep 深度精读 / map 知识地图 / feynman 费曼读书法 / book 全书精读。Deep-read
- [answering-cross-repo-request](https://doutorai.pt/en/components/skills__davepoon-subagents__answering-cross-repo-request): Use when a from another Claude session asks for a change in this repo (a new endpoint, a field, a client update, a package bump). Covers getting the user's go-a
- [cross-repo-request](https://doutorai.pt/en/components/skills__davepoon-subagents__cross-repo-request): Use when work in this repo depends on a change in a different repo that has its own running Claude session (frontend needing a backend endpoint, backend needing
- [cohesivity](https://doutorai.pt/en/components/skills__davepoon-subagents__cohesivity-84ce07): Backend and infra for a project via Cohesivity (cohesivity.ai). Provisions Postgres, hosting and deploys, auth and social login, realtime websockets, an agent-n
- [serply-web-search](https://doutorai.pt/en/components/skills__davepoon-subagents__serply-web-search): Search Google, Bing, Google News and Google Scholar, and read public pages, through the Serply MCP server. Use when you need current information beyond training
- [card-prices](https://doutorai.pt/en/components/skills__davepoon-subagents__card-prices): Use when the user asks what a trading card is worth, whether a card is worth grading, PSA 9 vs PSA 10 prices, card price history, trending or rising cards, or t
- [browser-extension-launch](https://doutorai.pt/en/components/skills__davepoon-subagents__browser-extension-launch): 面向没有编程经验的用户，把想法做成可试用的浏览器插件，并完成检查、商店材料、审核提交和上线验证；也用于继续已有插件、排错和发布新版。用户说“帮我做个插件”“把插件上架”“继续我的插件”时使用。普通网站开发、仅查询插件知识不触发。
- [fxmacrodata](https://doutorai.pt/en/components/skills__davepoon-subagents__fxmacrodata): Look up official macroeconomic releases, central-bank policy rates, release calendars and FX rates for 22 currencies through the FXMacroData MCP server. Use for
- [equibles-research](https://doutorai.pt/en/components/skills__davepoon-subagents__equibles-research): Use when the user asks about a US-listed company or the US market and wants sourced numbers, such as SEC filings and what a 10-K or 10-Q says, income statements
- [toolkit-research](https://doutorai.pt/en/components/skills__davepoon-subagents__toolkit-research): Use when the user explicitly requests research with Baizhi Agent Toolkit. Discover the connected MCP tools and their current schemas, agree a request budget, an
- [datacircle](https://doutorai.pt/en/components/skills__davepoon-subagents__datacircle): Look up people's LinkedIn profiles by URL with Datacircle, no markup, and manage the Datacircle balance and files. Use when the user gives LinkedIn profile URLs
- [browser-automation](https://doutorai.pt/en/components/skills__davepoon-subagents__browser-automation): Use when the user asks to do something on a real website, such as open a page, click through a flow, fill or submit a form, log in, extract data, take screensho
- [ad-angle-research](https://doutorai.pt/en/components/skills__davepoon-subagents__ad-angle-research): Pull the ads a competitor is running right now on Meta (Facebook/Instagram) and Google, then extract the hooks, offers, CTAs and landing pages worth modelling. 
- [dating-profile-coach](https://doutorai.pt/en/components/skills__davepoon-subagents__dating-profile-coach): Review and improve a dating profile on Tinder, Hinge or Bumble - rank the user''s photos and pick the lead photo, check them against the app''s official photo a
- [linkedin-profile-refresh](https://doutorai.pt/en/components/skills__davepoon-subagents__linkedin-profile-refresh): Refresh a LinkedIn profile for a job search from the user''s own resume - pick and check the profile photo, crop it locally to LinkedIn, Slack and GitHub sizes,
- [team-headshot-consistency](https://doutorai.pt/en/components/skills__davepoon-subagents__team-headshot-consistency): Make a website''s team or about page photos look like one set - scan the headshot folder locally, flag photos whose aspect ratio, brightness, colour temperature
- [ugc-ad-remix](https://doutorai.pt/en/components/skills__davepoon-subagents__ugc-ad-remix): Turn a proven UGC video ad into a shot-by-shot script for your own product. Use when the user shares a TikTok, Reels or Shorts ad (link or transcript) and wants
- [agentwhy-session-audit](https://doutorai.pt/en/components/skills__davepoon-subagents__agentwhy-session-audit): Checks which private files (.env, keys, secrets) Claude Code and its subagents reached in this project, whether a value came into an agent's context, and which 
- [dispatch-authoring](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__dispatch-authoring): Write the structured handoff from a planning session to a coding agent: a dispatch with a header, Gate 0 checks, scope, specification, verification, delivery ru
- [oss-contribution-research](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__oss-contribution-research): Research and prepare an outside contribution to an open-source repo, then stop at a queue item for a human to approve: policy check, intake, duplicate check, re
- [sprint-operations](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__sprint-operations): Run the measurement and ceremony layer of a delivery team: the metric set, estimation and capacity, the five sprint ceremonies, and the sprint report and quarte
- [ticket-authoring](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__ticket-authoring): Write complete, ready-to-pull work items for an issue tracker: bugs, tasks, stories, spikes, and epics, each with the fields, attachments, and done test its typ
- [dispatch-authoring](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__dispatch-authoring-4cffed): Write the structured handoff from a planning session to a coding agent: a dispatch with a header, Gate 0 checks, scope, specification, verification, delivery ru
- [oss-contribution-research](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__oss-contribution-research-48316e): Research and prepare an outside contribution to an open-source repo, then stop at a queue item for a human to approve: policy check, intake, duplicate check, re
- [sprint-operations](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__sprint-operations-0ec7c2): Run the measurement and ceremony layer of a delivery team: the metric set, estimation and capacity, the five sprint ceremonies, and the sprint report and quarte
- [ticket-authoring](https://doutorai.pt/en/components/skills__rampstackco-claude-skills__ticket-authoring-ab681d): Write complete, ready-to-pull work items for an issue tracker: bugs, tasks, stories, spikes, and epics, each with the fields, attachments, and done test its typ
- [wan27-media](https://doutorai.pt/en/components/skills__davepoon-subagents__wan27-media): Generate images and videos with Wan27, discover available models, estimate credit costs, and retrieve generation results through its MCP tools or CLI. Use when 
- [bro](https://doutorai.pt/en/components/skills__davepoon-subagents__bro): Re-explain the previous assistant message, or any text typed after the command, in a much simpler way. For when a reply made you go 'bro what'. Use /bro for a p
- [feedback](https://doutorai.pt/en/components/skills__davepoon-subagents__feedback): Turn post-task feedback - even blunt or angry feedback - into an evidence-based, approval-gated improvement plan for the agent/user workflow. Use after agent wo
- [guided-goal](https://doutorai.pt/en/components/skills__davepoon-subagents__guided-goal): Turn a rough objective into a bounded, verifiable completion contract, confirm it, then execute it with a finite attempt cap. Use when the user wants autonomous
- [launch-check](https://doutorai.pt/en/components/skills__davepoon-subagents__launch-check): Passive pre-launch report card for a live URL of an app or site built with AI (Lovable, Bolt, Replit, Cursor, v0, Claude). Scores security (secret keys visible 
- [second-deposit-igaming](https://doutorai.pt/en/components/skills__davepoon-subagents__second-deposit-igaming): Finds why casino first-time depositors (FTD) do not make a second deposit, from the operator's back-office exports (deposits, and if available withdrawals, bonu
- [cooper-email](https://doutorai.pt/en/components/skills__davepoon-subagents__cooper-email): Give Claude its own email inbox with Cooper Email to send, receive, reply, and wait for replies or verification codes over a hosted MCP server or HTTP. Use when
- [veida-image](https://doutorai.pt/en/components/skills__davepoon-subagents__veida-image): Generate an image from a text prompt with no API key, no account and no cost, using the free anonymous tier of Veida (veida.ai). Use when the user asks for a pi

## How to install skills for Claude Code

To install skills in Claude Code, start by opening the Doutor AI Library. From there, you can explore the large collection of available components and select the skills that suit your project. Once you have chosen, follow the quick, simple installation instructions to make sure your application has the capabilities it needs.

## Common use cases for skills in Claude Code

Skills for Claude Code can be applied in many contexts, from smart chatbots to recommendation systems. For example, a natural language processing skill can improve interaction with users, while a data analysis skill can provide valuable business insights. Their versatility means skills can be adapted to different industries and needs.

## The difference between skills and other Claude Code components

Skills differ from other Claude Code components, such as libraries and frameworks, because they focus on specific capabilities. While a library may offer many functions, a skill is designed to solve one concrete problem or provide one particular feature. This specialization makes skills an ideal choice for anyone looking for targeted solutions.

## By use case

If you already know the task you want to solve, start from a hand-picked selection: we chose the components that are really worth it and explain why. [Code review](https://doutorai.pt/en/for/code-review) · [Automated testing](https://doutorai.pt/en/for/automated-testing) · [Security and auditing](https://doutorai.pt/en/for/security-audit) · [Databases](https://doutorai.pt/en/for/databases) · [Technical documentation](https://doutorai.pt/en/for/technical-documentation) · [Frontend and interfaces](https://doutorai.pt/en/for/frontend-and-ui) · [DevOps and deployment](https://doutorai.pt/en/for/devops-and-deployment) · [Debugging](https://doutorai.pt/en/for/debugging). [See all use cases](https://doutorai.pt/en/for).

## Frequently asked questions

### What are skills in Claude Code?

Skills are packaged capabilities or knowledge that let Claude Code perform specific functions, improving its performance on different tasks.

### How can I find skills that are relevant to my project?

In the Doutor AI Library, you can use filters and categories to explore the available skills, making it easier to find the ones that match your project's needs.

### Are skills free?

Most skills in the Doutor AI Library are free, but some may have associated costs, depending on their complexity and the support offered.

### Can I create my own skills?

Yes, Claude Code lets developers create custom skills. Doutor AI offers resources and documentation to help with that process.

### Why are skills important when developing with Claude Code?

Skills let developers add advanced functionality quickly, saving time and resources while improving the user experience.

## Save hours every week with AI, no hassle

**Doutor AI** gives you 1,859 ready-to-copy AI prompts, by industry and in plain English - plus a complete technical library for those who want to go further. [See plans →](https://doutorai.pt/en/pricing)

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Content by Doutor AI (https://doutorai.pt/en). When quoting, please cite the source: https://doutorai.pt/en/claude-code/skills