Agent skill

CI CD

by seb1n in seb1n/awesome-ai-agent-skills

Set up a continuous integration and continuous delivery (CI/CD) pipeline for a software project, automating builds, tests, and deployments across environments.

MITAuto-check passedDevOps & Cloud

Install CI CD

skills CLI
$ npx skills add seb1n/awesome-ai-agent-skills --skill ci-cd -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install seb1n/awesome-ai-agent-skills ci-cd --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/devops-and-infrastructure/ci-cd .claude/skills/ci-cd && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ci-cd
GitHub stars
206
Token cost
~2.9k tokens
SKILL.md length
791 words
Files
1
Skills in repo
92
Repo updated
First seen
Licence
MIT

At a glance

Set up a continuous integration and continuous delivery (CI/CD) pipeline for a software project, automating builds, tests, and deployments across environments.

  • Works in 6 steps: Assess the Project and Choose a… → Define Pipeline Stages: The agent… → Configure Secrets and Environment… → …
  • The user requests ci cd
  • SKILL.md covers Workflow, Supported Technologies, Usage and Examples, plus 2 more sections
  • Needs POSTGRES_PASSWORD and AWS_ACCESS_KEY_ID

What it does

CI CD is an agent skill from seb1n/awesome-ai-agent-skills. 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.

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering CI/CD. It works with GitLab. The repository describes itself as: 103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf, and other Agent Skills-compatible tools. Complete SKILL.md… The licence is MIT.

When your agent uses it

  • The user requests ci cd
  • Provides relevant inputs for this workflow

Example prompts

  • “/ci-cd”

Requirements

  • Python 3
  • Node.js
  • Docker
  • A credential in GITHUB_TOKEN
  • A credential in AWS_SECRET_ACCESS_KEY

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Assess the Project and Choose a Platform: The agent analyzes the project's language, framework, hosting environment, and team preferences…
  2. Define Pipeline Stages: The agent structures the pipeline into discrete stages: lint (static analysis and code style), test (unit…
  3. Configure Secrets and Environment Variables: The agent sets up secure storage for API keys, database credentials, cloud provider tokens…
  4. Implement Caching and Optimization: The agent configures dependency caching (npm, pip, Maven) and build artifact caching to reduce…
  5. Configure Deployment Strategies: The agent implements the appropriate deployment strategy based on the project's risk tolerance and…
  6. Set Up Notifications and Monitoring: The agent configures post-pipeline notifications to inform the team of build status via Slack…

What it can do on your machine

Read from SKILL.md and the folder at commit 75865a5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • POSTGRES_PASSWORD
    • AWS_ACCESS_KEY_ID
    • AWS_SECRET_ACCESS_KEY
    • GITHUB_TOKEN
    • CI_REGISTRY_PASSWORD
    • SSH_PRIVATE_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

CI CD loads about 2.9k tokens when it runs. Until then it costs about 61 tokens; SKILL.md has 791 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 791 words, ~2,894 tokens.

Download SKILL.mdSave it as .claude/skills/ci-cd/SKILL.md (or your agent's skills folder).
name
ci-cd
description
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.
license
MIT
metadata.author
awesome-ai-agent-skills
metadata.version
1.0.0

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
    if: github.ref == 'refs/heads/develop'
    environment: staging
    steps:
      - uses: aws-actions/configure-aws-credentials@v4
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: us-east-1
      - run: |
          aws ecs update-service --cluster staging-cluster \
            --service my-app --force-new-deployment

  deploy-production:
    runs-on: ubuntu-latest
    needs: build-and-push
    if: github.ref == 'refs/heads/main'
    environment: production
    steps:
      - uses: aws-actions/configure-aws-credentials@v4
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: us-east-1
      - run: |
          aws ecs update-service --cluster production-cluster \
            --service my-app --force-new-deployment

  notify:
    runs-on: ubuntu-latest
    needs: [deploy-staging, deploy-production]
    if: always() && contains(needs.*.result, 'failure')
    steps:
      - uses: slackapi/slack-github-action@v1.25.0
        with:
          payload: |
            {"text": "Pipeline failed for ${{ github.repository }} on ${{ github.ref_name }}"}
        env:
          SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL }}
Show full SKILL.md (316 more words)Show less
Example 2: GitLab CI Pipeline for a Python Application with Docker
yaml
stages:
  - lint
  - test
  - build
  - deploy

variables:
  PIP_CACHE_DIR: "$CI_PROJECT_DIR/.pip-cache"
  DOCKER_IMAGE: $CI_REGISTRY_IMAGE:$CI_COMMIT_SHA

cache:
  paths:
    - .pip-cache/
    - .venv/

lint:
  stage: lint
  image: python:3.12-slim
  script:
    - pip install ruff mypy
    - ruff check src/
    - mypy src/ --ignore-missing-imports

test:
  stage: test
  image: python:3.12-slim
  services:
    - postgres:16
  variables:
    POSTGRES_DB: testdb
    POSTGRES_PASSWORD: testpass
    DATABASE_URL: "postgresql://postgres:testpass@postgres:5432/testdb"
  script:
    - python -m venv .venv
    - source .venv/bin/activate
    - pip install -r requirements.txt -r requirements-dev.txt
    - pytest tests/ --cov=src --cov-report=xml
  artifacts:
    reports:
      coverage_report:
        coverage_format: cobertura
        path: coverage.xml

build:
  stage: build
  image: docker:24
  services:
    - docker:24-dind
  script:
    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
    - docker build -t $DOCKER_IMAGE -t $CI_REGISTRY_IMAGE:latest .
    - docker push $DOCKER_IMAGE
    - docker push $CI_REGISTRY_IMAGE:latest

deploy_production:
  stage: deploy
  image: alpine:latest
  only:
    - main
  environment:
    name: production
    url: https://myapp.example.com
  before_script:
    - apk add --no-cache openssh-client
    - eval $(ssh-agent -s)
    - echo "$SSH_PRIVATE_KEY" | ssh-add -
  script:
    - ssh deploy@production-server "docker pull $DOCKER_IMAGE && docker-compose up -d"

Best Practices

  • Fail fast: Order pipeline stages so that quick checks (linting, formatting) run first, preventing wasted compute on code that will fail review anyway.
  • Pin action and image versions: Always use specific version tags for CI actions and Docker images (e.g., actions/checkout@v4, python:3.12-slim) to ensure reproducible builds and avoid supply-chain attacks.
  • Scope secrets tightly: Restrict secrets to the environments and branches that need them. Use environment-level protection rules to require manual approval before production deployments.
  • Cache aggressively: Cache dependency installations, Docker layers, and build artifacts between pipeline runs. This can reduce pipeline duration by 50% or more on typical projects.
  • Use matrix builds for compatibility: Test across multiple language versions and operating systems in parallel to catch compatibility issues early without increasing pipeline wall-clock time.
  • Implement deployment gates: Use manual approval steps, health check verifications, or canary analysis before promoting a release to production to reduce the blast radius of bad deployments.

Edge Cases

  • Flaky tests: Tests that pass intermittently can block pipelines. Implement retry logic for known flaky tests and track flakiness metrics to prioritize fixes. Most CI platforms support a retry directive for individual jobs.
  • Monorepo pipelines: In monorepos, changes to one service should not trigger pipelines for unrelated services. Use path-based filters (e.g., GitHub Actions paths: or GitLab changes:) to scope pipeline triggers.
  • Rate limits and quotas: Docker Hub, npm, and cloud APIs impose rate limits. Use authenticated pulls, private mirrors, or caching proxies to avoid pipeline failures due to throttling.
  • Long-running pipelines: Pipelines exceeding platform time limits (e.g., GitHub Actions' 6-hour job limit) need to be split into smaller jobs or use self-hosted runners with higher limits.
  • Branch protection conflicts: If the CI pipeline writes back to the repo (e.g., auto-formatting commits), it may conflict with branch protection rules. Use a dedicated bot token or app-level authentication to push changes.

© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in devops-and-infrastructure/ci-cd of seb1n/awesome-ai-agent-skills.

Open the folder on GitHubat commit 75865a5

Compare with similar skills

CI CD next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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CI CD this skillseb1n/awesome-ai-agent-skills206—~2.9kAutomated safety check: PassMIT
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Megatron-LM Base Image BumpNVIDIA/Megatron-LM18k—~2.8kAutomated safety check: PassApache-2.0
Megatron-LM CI/CD GuideNVIDIA/Megatron-LM18k—~1.8kAutomated safety check: PassApache-2.0
Megalinter Checknvuillam/npm-groovy-lint2481 repos~3.9kAutomated safety check: NotesMIT
Migrate To TeamcityJetBrains/teamcity-cli125—~1.3kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about CI CD

What does CI CD do?

Set up a continuous integration and continuous delivery (CI/CD) pipeline for a software project, automating builds, tests, and deployments across environments. CI CD is an agent skill from seb1n/awesome-ai-agent-skills. Set up a continuous integration and continuous delivery (CI/CD) pipeline for a software project, automating builds, tests, and deployments across environments.

When should I use CI CD?

CI CD fits situations like: the user requests ci cd; provides relevant inputs for this workflow.

How do I install CI CD in Claude Code?

Run `npx skills add seb1n/awesome-ai-agent-skills --skill ci-cd -a claude-code`. Or copy the skill folder (devops-and-infrastructure/ci-cd in seb1n/awesome-ai-agent-skills) into .claude/skills/ci-cd in your project. Claude Code loads it when a task matches its description.

How do I install CI CD in Codex?

Run `npx skills add seb1n/awesome-ai-agent-skills --skill ci-cd -a codex`. Or copy the skill folder (devops-and-infrastructure/ci-cd in seb1n/awesome-ai-agent-skills) into .agents/skills/ci-cd in your project. Codex loads it when a task matches its description.

Can I use CI CD in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add seb1n/awesome-ai-agent-skills --skill ci-cd -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ci-cd, .gemini/skills/ci-cd, .github/skills/ci-cd and .opencode/skills/ci-cd in your project.

What does CI CD need to run?

Going by SKILL.md and its folder, CI CD needs credentials named POSTGRES_PASSWORD, AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and GITHUB_TOKEN. Our summary lists: Python 3; Node.js; Docker; A credential in GITHUB_TOKEN; A credential in AWS_SECRET_ACCESS_KEY.

Does CI CD access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is CI CD safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does CI CD use?

CI CD is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does CI CD use?

About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to CI CD?

Skills that share tags, products or a category with CI CD: Fingerprint CI Gate (liarjsdev/liarjs-skills, 518 stars), Megatron-LM Base Image Bump (NVIDIA/Megatron-LM, 18k stars), Megatron-LM CI/CD Guide (NVIDIA/Megatron-LM, 18k stars) and Megalinter Check (nvuillam/npm-groovy-lint, 248 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains CI CD?

seb1n (a GitHub user) maintains it in seb1n/awesome-ai-agent-skills, which has 206 GitHub stars. The repository holds 92 skills in this directory. The repository was last updated on August 9, 2026.

Source: seb1n/awesome-ai-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.