READMEs, API docs, ADRs, diagrams and changelogs that do not go stale
Technical documentation is usually the first thing to fall behind. The code changes every week, the README still describes last year's installation and the API spec lives in a document nobody has opened since the last migration. The cost shows up late: every new person loses days working out what had already been decided, and nobody remembers why.
The components on this page treat documentation as the output of a process rather than a manual task that keeps getting postponed. They generate API references from the code, write Architecture Decision Records (ADR) with context and alternatives, draw diagrams in Mermaid or with the C4 model, build the changelog from the commit history and check whether the existing files still match what is implemented.
Start with what hurts most in your project. If nobody can get the local environment running, the README and the onboarding guide are worth more than any diagram. If the frontend team keeps asking about the response format, start with the API documentation. Agents are for open-ended, ongoing work, commands for a concrete, repeated task, and skills for fixing formats and writing rules.
21 hand-picked components, from 4 different angles.
The starting point for any repository: explaining what the project does, how to get it running and how to contribute. It also includes the less visible work of keeping those files alive as the code changes.
For when there is too much code and too little text: it creates and improves the project documentation from the code itself, applying consistent technical writing standards.
Use this agent to create, improve, and maintain project documentation. Specializes in technical writing, documentation standards, and generating docum
Designed for inherited codebases. It analyzes the existing architecture and implementation patterns and produces long technical manuals, instead of loose pages.
Creates comprehensive technical documentation from existing codebases. Analyzes architecture, design patterns, and implementation details to produce l
A single command for the sync pass, which updates the implementation status, the API changes and any content that has drifted after a round of development.
Systematically update project documentation with implementation status, API changes, and synchronized content
Useful when the documentation is already too big to reread by hand, because it sets up validation and automatic updates to catch pages that no longer match.
Use PROACTIVELY to implement comprehensive documentation maintenance systems with quality assurance, validation, and automated updates
Not every README serves the same audience. This skill provides templates based on the type of project and who will read it, from an internal library to an open source project.
Use when writing or improving README files. Not all READMEs are the same - provides templates and guidance matched to your audience and project type.
For teams that want to stop arguing about structure. It brings together templates for READMEs, API documentation and code comments, including formats designed to be read by AI.
Documentation templates and structure guidelines. README, API docs, code comments, and AI-friendly documentation.
Documentation that other people read to integrate with your system. It covers OpenAPI specs, complete parameter listings and the handoff of context between backend and frontend.
Covers the full lifecycle of an API exposed to third parties: writing the OpenAPI spec, interactive documentation portals and ready-to-use code samples for integrators.
Use this agent when creating or improving API documentation, writing OpenAPI specifications, building interactive documentation portals, or generating
When the API is already stable and all that is missing is the reference, it generates the documentation in several output formats and handles automatic publishing.
Auto-generate API reference documentation with multiple output formats and automated deployment
A good fit for anyone working design-first or who needs to generate SDKs, because it creates and maintains OpenAPI 3.1 specs and checks contract compliance.
Generate and maintain OpenAPI 3.1 specifications from code, design-first specs, and validation patterns. Use when creating API documentation, generati
Solves the moment when the backend is done and the frontend is left waiting to find out what exists, producing a handoff document focused on whoever consumes the endpoints.
Create API handoff documentation for frontend developers. Use when backend work is complete and needs to be documented for frontend integration, or us
For the cases where the complete listings are missing. It produces exhaustive parameter references and configuration guides, instead of a handful of illustrative examples.
Creates exhaustive technical references and API documentation. Generates comprehensive parameter listings, configuration guides, and searchable refere
Documentation for the newcomer who asks why the system is built this way. It brings together the overall design, the decisions recorded in writing and the visual representations.
A complete kickoff when nothing has been written about the system's design, since it produces the diagrams, the ADRs and a browsable visualization in one go.
Generate comprehensive architecture documentation with diagrams, ADRs, and interactive visualization
For pinning down decisions before they get lost in chat history. It writes structured Architecture Decision Records, with the context and the alternatives considered.
Expert agent for creating comprehensive Architectural Decision Records (ADRs) with structured formatting optimized for AI consumption and human readab
Follows the C4 model and generates the context, container and component levels in Mermaid, which helps when you need to explain the same system to audiences who need different levels of detail.
Generate architecture documentation using C4 model Mermaid diagrams. Use when asked to create architecture diagrams, document system architecture, vis
When the diagram has to fit a specific format, it produces ASCII, Mermaid, PlantUML or Draw.io, including ERDs, state machines and dependency graphs.
Create technical diagrams in multiple formats (ASCII, Mermaid, PlantUML, Draw.io). Use PROACTIVELY for architecture visualization, ERD generation, flo
A syntax reference for anyone who writes diagrams by hand inside Markdown, covering class diagrams, sequence diagrams and domain modeling.
Comprehensive guide for creating software diagrams using Mermaid syntax. Use when users need to create, visualize, or document software through diagra
What has changed since the last version and what someone needs to know on day one. They are two problems with the same root: context that only exists in the heads of the people who are already there.
Works from the git history to produce changelogs and release notes, and keeps the version documentation aligned from one release to the next.
Changelog and release notes specialist. Use PROACTIVELY for generating changelogs from git history, creating release notes, and maintaining version do
For anyone who just wants the CHANGELOG.md file in order. It generates and maintains entries in the Keep a Changelog format, without inventing a new structure for each version.
Generate and maintain project changelog with Keep a Changelog format
Separates two audiences that usually get mixed up: from the recent commits it produces a customer-facing note and a separate technical note for the team.
Generate comprehensive release documentation from recent commits, producing two distinct outputs: a customer-facing release note and a technical engin
Generates the onboarding guide with environment setup, workflows and tutorials, which saves you repeating the same explanations to every new person.
Create comprehensive developer onboarding guide with environment setup, workflows, and interactive tutorials
Turns existing code into step-by-step tutorials with practical examples, useful when the reference is already written but nobody knows where to start.
Creates step-by-step tutorials and educational content from code. Transforms complex concepts into progressive learning experiences with hands-on exam
Every item in the Doutor AI technical library is a text file. Agents go in .claude/agents/, commands in .claude/commands/ and skills in .claude/skills/, inside the repository where you want to use them. If you would rather have them available in every project, put them in the equivalent folder inside ~/.claude/. After saving the file, restart the Claude Code session so the new component is read. Agents are chosen by their description, so you just ask for the task in plain words; commands are called by name, preceded by a slash; skills load when the request matches what their description says. Install two or three at a time, not twenty.
These components do more when chained than when used alone. A sequence that works well on a project already underway: first an agent that reads the codebase and produces the overall design, then the ADRs to record the decisions that only exist in the team's memory and, at the end, the README and the onboarding guide, which build on that foundation. API documentation follows a different rhythm, tied to the development cycle: generate the OpenAPI spec from the code and regenerate it whenever an endpoint changes. The changelog closes the loop, because it comes from the commits and serves as a record of what was delivered in each version.
None of these components knows what is not written down anywhere. An agent reads the code, the commits and the existing files; it does not know why the team rejected the obvious alternative two years ago, or that a given service is going to be switched off next quarter. That context has to come from you. There is also a known risk: documentation generated in large volumes gives a sense of work done and starts being accepted without being read. Review the output before saving it, especially the ADRs and the API documentation, where one wrong detail leads someone else to build on a false premise.
It depends on what you ask for. Reference documentation, meaning lists of endpoints, parameters and configuration options, comes out well because the information is already in the code. Text that explains intent and tradeoffs needs context only the team has. In practice, generate the mechanical part and write by hand the three or four pages that explain decisions.
Review diffs, catch bugs before the merge and reduce technical debt.
React, Vue, Angular, design systems, accessibility and performance in the browser.
Components to write, run and evaluate tests with Claude Code.
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