Skip to main content
ClaudeWave
Skill171 repo starsupdated 27d ago

ci-cd

Set up a continuous integration and continuous delivery (CI/CD) pipeline for a software project, automating builds, tests, and deployments across environments. Use when the user requests ci cd or provides relevant inputs for this workflow.

Install in Claude Code
Copy
git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills /tmp/ci-cd && cp -r /tmp/ci-cd/devops-and-infrastructure/ci-cd ~/.claude/skills/ci-cd
Then start a new Claude Code session; the skill loads automatically.

SKILL.md

# CI/CD Pipeline Setup

This skill enables the agent to design, configure, and maintain CI/CD pipelines that automate the entire software delivery lifecycle. The agent can set up pipeline stages including linting, testing, building, deploying, and notifying stakeholders, ensuring that every code change is validated and delivered reliably. The agent understands secrets management, caching strategies, matrix builds, and deployment strategies such as blue/green and canary releases.

## Workflow

1. **Assess the Project and Choose a Platform:** The agent analyzes the project's language, framework, hosting environment, and team preferences to recommend a CI/CD platform. Options include GitHub Actions, GitLab CI/CD, Jenkins, CircleCI, and Azure DevOps. The agent considers factors like repository hosting, cost, plugin ecosystem, and integration with existing tools before making a recommendation.

2. **Define Pipeline Stages:** The agent structures the pipeline into discrete stages: lint (static analysis and code style), test (unit, integration, and end-to-end), build (compilation, bundling, Docker image creation), deploy (staging and production), and notify (Slack, email, or webhook alerts). Each stage has clearly defined inputs, outputs, and failure conditions so the pipeline fails fast on errors.

3. **Configure Secrets and Environment Variables:** The agent sets up secure storage for API keys, database credentials, cloud provider tokens, and other sensitive values using the platform's native secrets manager (e.g., GitHub Secrets, GitLab CI/CD Variables, or Jenkins Credentials). Secrets are never hardcoded in pipeline files and are scoped to the appropriate environment.

4. **Implement Caching and Optimization:** The agent configures dependency caching (npm, pip, Maven) and build artifact caching to reduce pipeline execution time. Matrix builds are used to test across multiple language versions or operating systems in parallel. The agent also sets up conditional execution so that expensive stages like end-to-end tests only run on relevant branches.

5. **Configure Deployment Strategies:** The agent implements the appropriate deployment strategy based on the project's risk tolerance and infrastructure. Options include rolling updates, blue/green deployments (two identical environments swapped at the load balancer), and canary releases (gradual traffic shifting). The agent also configures rollback procedures in case a deployment fails health checks.

6. **Set Up Notifications and Monitoring:** The agent configures post-pipeline notifications to inform the team of build status via Slack, Microsoft Teams, email, or custom webhooks. Deployment events are logged, and the agent can integrate with monitoring tools to verify application health after each deployment.

## Supported Technologies

- **Platforms:** GitHub Actions, GitLab CI/CD, Jenkins, CircleCI, Azure DevOps, Bitbucket Pipelines, Travis CI
- **Languages:** Node.js, Python, Java, Go, Rust, Ruby, .NET, PHP
- **Containerization:** Docker, Podman, Buildah
- **Cloud Providers:** AWS (ECS, EKS, Lambda), GCP (Cloud Run, GKE), Azure (App Service, AKS)
- **Artifact Registries:** Docker Hub, GitHub Container Registry, AWS ECR, Google Artifact Registry

## Usage

Provide the agent with your project's language, framework, repository host, target deployment environment, and any specific requirements such as testing frameworks or deployment strategies.

**Example prompt:**

```
Set up a CI/CD pipeline for my Node.js Express app hosted on GitHub.
- Run ESLint and Prettier checks, then Jest unit tests
- Build a Docker image and push to GitHub Container Registry
- Deploy to AWS ECS staging on push to develop, production on push to main
- Send Slack notifications on failure
```

## Examples

### Example 1: GitHub Actions Workflow for a Node.js Application

```yaml
name: CI/CD Pipeline

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

env:
  NODE_VERSION: '20'
  REGISTRY: ghcr.io
  IMAGE_NAME: ${{ github.repository }}

jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: ${{ env.NODE_VERSION }}
          cache: 'npm'
      - run: npm ci
      - run: npm run lint
      - run: npm run format:check

  test:
    runs-on: ubuntu-latest
    needs: lint
    strategy:
      matrix:
        node-version: [18, 20, 22]
    services:
      postgres:
        image: postgres:16
        env:
          POSTGRES_PASSWORD: testpass
          POSTGRES_DB: testdb
        ports:
          - 5432:5432
        options: >-
          --health-cmd pg_isready
          --health-interval 10s
          --health-timeout 5s
          --health-retries 5
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: ${{ matrix.node-version }}
          cache: 'npm'
      - run: npm ci
      - run: npm test -- --coverage
        env:
          DATABASE_URL: postgres://postgres:testpass@localhost:5432/testdb
      - uses: actions/upload-artifact@v4
        with:
          name: coverage-${{ matrix.node-version }}
          path: coverage/

  build-and-push:
    runs-on: ubuntu-latest
    needs: test
    if: github.event_name == 'push'
    permissions:
      contents: read
      packages: write
    steps:
      - uses: actions/checkout@v4
      - uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
          username: ${{ github.actor }}
          password: ${{ secrets.GITHUB_TOKEN }}
      - uses: docker/build-push-action@v5
        with:
          context: .
          push: true
          tags: |
            ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ github.sha }}
            ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:latest
          cache-from: type=gha
          cache-to: type=gha,mode=max

  deploy-staging:
    runs-on: ubuntu-latest
    needs: build-and-push
agent-evaluationSkill

Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

agent-observabilitySkill

Design privacy-aware observability for AI agents using traces, spans, structured events, metrics, cost attribution, dashboards, alerts, and investigation workflows. Use when instrumenting an agent, debugging intermittent tool or model failures, defining service-level objectives, analyzing latency or spend, auditing agent decisions, or preparing production monitoring.

human-in-the-loopSkill

Design and verify auditable human oversight, approval gates, escalation paths, and safe state transitions for AI agent workflows. Use when deciding which agent actions require review, adding approve/reject or dual-control flows, preventing unauthorized autonomous effects, creating decision records, reducing rubber-stamping, or recovering safely from rejected, expired, or failed actions.

mcp-server-buildingSkill

Design, implement, harden, and verify Model Context Protocol (MCP) servers with precise tool contracts, least-privilege authorization, safe transports, structured errors, and interoperability tests. Use when creating a new MCP server, exposing an API or data source through MCP, reviewing an MCP server design, adding or revising MCP tools, or preparing an MCP server for production.

multi-agent-orchestrationSkill

Design and operate bounded multi-agent workflows with task decomposition, dependency graphs, ownership, handoff contracts, shared-state controls, approvals, recovery, and synthesis. Use when a task contains genuinely independent workstreams, specialized roles, parallel research or implementation, reviewer-worker loops, or coordination problems that one agent should not execute sequentially.

tool-schema-designSkill

Design and validate model-facing tool definitions with clear names, action-oriented descriptions, bounded JSON Schema parameters, explicit side effects, safe defaults, idempotency, errors, and realistic tests. Use when creating function-calling tools, MCP tools, agent actions, structured tool inputs, or when a model selects the wrong tool, invents arguments, or causes unsafe side effects.

agent-red-teamingSkill

Plan, execute, document, and retest authorized security assessments of AI agents and multi-agent workflows using safe adversarial cases, synthetic identities, canaries, and evidence-based findings. Use when defining red-team rules of engagement, assessing prompt injection or excessive agency, testing tool and identity boundaries, evaluating memory or cross-agent attacks, scoring a campaign, or verifying remediation in an approved environment.

prompt-injection-defenseSkill

Threat-model and harden AI agents, RAG systems, assistants, and tool-using workflows against direct, indirect, stored, cross-agent, and multimodal prompt injection. Use when reviewing an agent architecture, isolating untrusted content, constraining tools and egress, protecting secrets, adding injection-focused tests, investigating a suspected injection incident, or documenting residual prompt-injection risk.