Components to package, ship and keep software running in production.
The code works on the machine of whoever wrote it, and the trouble starts right after that. The Dockerfile grew without anyone reviewing it, the workflow file was copied from another project and nobody can explain half its lines anymore, shipping is still a manual Friday ritual and, when something breaks in production, the logs are scattered across three different places. This is the work that separates an application that exists from an application that keeps running.
The components on this page tackle that specific part. There are agents that read the repository and write the pipeline, skills that explain how to shrink an image or when to roll back a release, commands that set up monitoring in one go, MCP connectors that link Claude Code to the Terraform registry and to Grafana dashboards, and an automation that watches the status of deployments. The practical effect is less configuration written from memory and more verified decisions.
Choose by the problem you have today, not the whole list. If you still deploy by hand, you gain most from a workflow generator and safe deployment rules. If your infrastructure is already described in code, you gain most from a review before the apply. If you have already spent a night fighting fires, you gain most from observability and SLOs. Four to six well-chosen components are enough to cover a whole project.
22 hand-picked components, from 4 different angles.
Everything that happens between the commit and the version ready to ship: workflows, automated tests, environments and the decision about when to go ahead.
A good starting point when nothing is set up yet, because it covers pipelines, infrastructure automation, containerization and cloud platforms (AWS, GCP, Azure) in a single set of instructions.
Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, inf
For teams that already ship releases but with too much friction in the process, this agent designs and optimizes CI/CD pipelines and deployment automation strategies.
Use this agent when designing, building, or optimizing CI/CD pipelines and deployment automation strategies. Specifically:
Generates GitHub Actions workflows from a description of what you need: tests, linting, security analysis, Docker builds, release automation and scheduled jobs.
Use when the user wants to create, generate, or set up a GitHub Actions workflow. Handles CI/CD pipelines, testing, deployment, linting, security scan
Useful when the pipeline already exists and what is missing is maintaining it, with multi-environment support, deployment strategies and a readout of the status of recent runs.
Manage and automate CI/CD pipeline configuration with GitHub Actions, multi-environment support, and deployment strategies
Built for GitOps workflows and for debugging failed deployments, with a stated goal that sums up the intent well: make every release boring and reliable.
DevOps specialist for CI/CD pipelines, deployment debugging, and GitOps workflows focused on making deployments boring and reliable
Teaches decision criteria instead of scripts to copy, including verification and rollback strategies. It helps when everyone on the team ships in a different way.
Production deployment principles and decision-making. Safe deployment workflows, rollback strategies, and verification. Teaches thinking, not scripts.
Packaging the application and running it repeatably, from the Dockerfile to the cluster.
Handles multi-stage builds, image optimization and security, and orchestration with Docker Compose, in other words the usual source of complaints about heavy images and slow builds.
Docker containerization expert with deep knowledge of multi-stage builds, image optimization, container security, Docker Compose orchestration, and pr
Makes sense when the project has already moved to Kubernetes and you need to design, configure or troubleshoot clusters and workloads in a production environment.
Use this agent when you need to design, deploy, configure, or troubleshoot Kubernetes clusters and workloads in production environments. Specifically:
The choice for anyone who ships nervously: it prioritizes safe rollouts and rollbacks, secure defaults and operational verification after every change.
SRE-focused Kubernetes specialist prioritizing reliability, safe rollouts/rollbacks, security defaults, and operational verification for production-gr
When the same application runs in several environments, it guides the creation and organization of Helm charts with reusable templates instead of duplicated manifests.
Comprehensive guidance for creating, organizing, and managing Helm charts for packaging and deploying Kubernetes applications.
Describing servers, networks and managed services in versioned files, and deciding the architecture behind them.
Covers the parts of Terraform where things usually go wrong: modules, state management, provider configuration, workspaces and drift detection.
Terraform and Infrastructure as Code specialist. Use PROACTIVELY for Terraform modules, state management, IaC best practices, provider configurations,
Useful for reviewing changes before the apply, with attention to state security, least privilege and plan/apply discipline. It pairs with the previous agent in a two-step flow.
Terraform-focused agent that reviews and creates safer IaC changes with emphasis on state safety, least privilege, module patterns, drift detection, a
A connector that links the assistant to the Terraform Registry APIs, which saves you writing provider and module names and versions from memory.
The Terraform MCP Server is a Model Context Protocol (MCP) server that provides seamless integration with Terraform Registry APIs, enabling advanced a
For the decisions that sit above the configuration file: multi-cloud strategies, migration planning, disaster recovery and cost optimization.
Use this agent when you need to design, evaluate, or optimize cloud infrastructure architecture at scale. Invoke when designing multi-cloud strategies
If the project lives on Vercel, it covers platform configuration, edge functions, middleware and the deployment strategies specific to that environment.
Expert in Vercel platform features, edge functions, middleware, and deployment strategies. Use PROACTIVELY for Vercel deployments, performance optimiz
What you need after the deploy: releases without downtime, metrics and alerts, and a method for the hours when something breaks.
Sets up switching between two versions with health checks and automatic rollback, a good fit for services that cannot accept downtime windows.
Implement blue-green deployment strategy with zero-downtime switching, health validation, and automatic rollback
Treats monitoring as infrastructure: metrics collection, log aggregation, distributed tracing, alerting systems and performance dashboards.
Monitoring and observability infrastructure specialist. Use PROACTIVELY for metrics collection, alerting systems, log aggregation, distributed tracing
A good start for projects with nothing installed, because it detects what already exists in the repository and configures metrics, logs, tracing and alerts in one go.
Setup comprehensive monitoring and observability with metrics, logging, tracing, and alerting
Connects Claude Code to Grafana dashboards and monitoring, on your own instance or on Grafana Cloud, so you can check status without switching tools.
A Model Context Protocol server for interacting with Grafana dashboards and monitoring. Supports both self-hosted Grafana instances and Grafana Cloud.
Designed for the moment production breaks: log analysis, investigation of deployment failures and root cause analysis.
Production troubleshooting and incident response specialist. Use PROACTIVELY for debugging issues, log analysis, deployment failures, monitoring setup
When the problem is no longer an isolated incident but a lack of objectives, it helps define SLIs and SLOs, manage error budgets and reduce repetitive manual work.
Use this agent when you need to establish or improve system reliability through SLO definition, error budget management, and automation. Invoke when i
An automation that reacts to deploy commands and watches the status of releases on Vercel, warning you about build failures or performance degradation.
Monitor deployment status, error rates, and performance metrics, sending notifications for failed deployments or performance degradation. Tracks Verce
Each category lives in its own place. Agents and skills are markdown files in .claude/agents/ and .claude/skills/, inside the repository or in the personal ~/.claude folder. Commands go in .claude/commands/ and are invoked with a slash and the name, for example /ci-pipeline. MCP connectors, such as the Terraform and Grafana ones, are configuration entries with the command to run and the environment variables, including the URL and access token. Automations are hook blocks in the settings, tied to tool use events. For team work, it is worth installing at project level, so the configuration travels with the repository, and keeping credentials out of versioned files.
A sequence that works in practice: the Docker skill prepares the image, the GitHub Actions generator writes the workflow that builds and tests it, the Terraform specialist describes the infrastructure and the IaC reviewer validates the plan before the apply, the blue-green command handles the version switch without downtime and the observability setup installs metrics and alerts. When an alert fires, the troubleshooting agent steps in for log and root cause analysis, and the SRE agent closes the loop, turning the incident into SLOs and less manual work. Start with two or three steps of this chain and add more as real needs arise.
These components write configuration and suggest commands, but they do not replace human review. Read the generated YAML and HCL before applying them, always run the Terraform plan and confirm what will be destroyed, and do the first test in a staging environment. MCP connectors ask for tokens: create service accounts with minimal permissions and store the values in environment variables, never in the configuration file. Automations run commands on their own, so understand exactly what they trigger before you turn them on. And keep the same simple principle as always: ship early in the day, with someone available to roll back.
No. Many of the picks suit projects that run on a single server or on a managed platform: the Docker skill, the GitHub Actions generator, the safe deployment rules and the observability setup do not depend on any cluster. The Kubernetes and Helm components only pay off when the project already uses orchestration; otherwise they add complexity with no return.
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