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Cicd Pipeline Generator

Community
ailabs-393
cicd-pipeline-generator

This skill should be used when creating or configuring CI/CD pipeline files for automated testing, building, and deployment. Use this for generating GitHub Actions workflows, GitLab CI configs, CircleCI configs, or other CI/CD platform configurations. Ideal for setting up automated pipelines for Node.js/Next.js applications, including linting, testing, building, and deploying to platforms like Vercel, Netlify, or AWS.

Overview

Publisherailabs-393
Repositoryai-labs-claude-skills
Skill namecicd-pipeline-generator
Stars
447
Forks
115
Bundled files
5
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ailabs-393 on GitHub. Read the source before you install it.

Installation

Install the Cicd Pipeline Generator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ailabs-393/ai-labs-claude-skills.git /tmp/ai-labs-claude-skills
mkdir -p .claude/skills
cp -r /tmp/ai-labs-claude-skills/packages/skills/cicd-pipeline-generator .claude/skills/cicd-pipeline-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cicd Pipeline Generator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cicd Pipeline Generator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cicd Pipeline Generator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CI/CD Pipeline Generator

Overview

Generate production-ready CI/CD pipeline configuration files for various platforms (GitHub Actions, GitLab CI, CircleCI, Jenkins). This skill provides templates and guidance for setting up automated workflows that handle linting, testing, building, and deployment for modern web applications, particularly Node.js/Next.js projects.

Core Capabilities

1. Platform Selection

Choose the appropriate CI/CD platform based on project requirements:

  • GitHub Actions: Best for GitHub-hosted projects with native integration
  • GitLab CI/CD: Ideal for GitLab repositories with complex pipeline needs
  • CircleCI: Optimized for Docker workflows and fast build times
  • Jenkins: Suitable for self-hosted, highly customizable environments

Refer to references/platform-comparison.md for detailed platform comparisons, pros/cons, and use case recommendations.

2. Pipeline Configuration Generation

Generate pipeline configs following these principles:

Pipeline Stages

Structure pipelines with these standard stages:

  1. Install Dependencies

    • Checkout code from repository
    • Setup runtime environment (Node.js version)
    • Restore cached dependencies
    • Install dependencies with npm ci
    • Cache dependencies for future runs
  2. Lint

    • Run ESLint for code quality
    • Run TypeScript type checking
    • Fail fast on linting errors
  3. Test

    • Execute unit tests
    • Execute integration tests
    • Generate code coverage reports
    • Upload coverage to reporting services (Codecov, Coveralls)
  4. Build

    • Create production build
    • Verify build succeeds
    • Store build artifacts
  5. Deploy

    • Deploy to staging (develop branch)
    • Deploy to production (main branch)
    • Run post-deployment smoke tests
Caching Strategy

Implement effective caching to speed up builds:

yaml
# Cache node_modules based on package-lock.json
cache:
  key: ${{ hashFiles('package-lock.json') }}
  paths:
    - node_modules/
    - .npm/
Environment Variables

Configure necessary environment variables:

  • NODE_ENV: Set to production for builds
  • Platform-specific tokens: Store as secrets
  • Build-time variables: Pass to build process

3. Template Usage

Use provided templates from assets/ directory:

GitHub Actions Template (assets/github-actions-nodejs.yml):

  • Multi-job workflow with lint, test, build, deploy
  • Matrix builds for multiple Node.js versions (optional)
  • Vercel deployment integration
  • Artifact uploading
  • Code coverage reporting

GitLab CI Template (assets/gitlab-ci-nodejs.yml):

  • Multi-stage pipeline
  • Dependency caching
  • Manual production deployment
  • Automatic staging deployment
  • Coverage reporting

To use a template:

  1. Copy the appropriate template file
  2. Place in the correct location:
    • GitHub Actions: .github/workflows/ci.yml
    • GitLab CI: .gitlab-ci.yml
  3. Customize deployment targets, environment variables, and branch names
  4. Add required secrets to platform settings

4. Deployment Configuration

Vercel Deployment

For GitHub Actions:

yaml
- uses: amondnet/vercel-action@v25
  with:
    vercel-token: ${{ secrets.VERCEL_TOKEN }}
    vercel-org-id: ${{ secrets.VERCEL_ORG_ID }}
    vercel-project-id: ${{ secrets.VERCEL_PROJECT_ID }}
    vercel-args: '--prod'

Required Secrets:

  • VERCEL_TOKEN: Get from Vercel account settings
  • VERCEL_ORG_ID: From Vercel project settings
  • VERCEL_PROJECT_ID: From Vercel project settings
Netlify Deployment
yaml
- run: |
    npm install -g netlify-cli
    netlify deploy --prod --dir=.next
  env:
    NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }}
    NETLIFY_SITE_ID: ${{ secrets.NETLIFY_SITE_ID }}
AWS S3 + CloudFront
yaml
- 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 s3 sync .next/static s3://${{ secrets.S3_BUCKET }}/static
    aws cloudfront create-invalidation --distribution-id ${{ secrets.CF_DIST_ID }} --paths "/*"

5. Testing Integration

Configure test execution with proper reporting:

Jest Configuration:

yaml
- name: Run tests with coverage
  run: npm test -- --coverage --coverageReporters=text --coverageReporters=lcov

- name: Upload coverage
  uses: codecov/codecov-action@v4
  with:
    files: ./coverage/lcov.info
    flags: unittests

Fail Fast Strategy:

yaml
# Run quick tests first
jobs:
  lint:  # Fails in ~30 seconds
  test:  # Fails in ~2 minutes
  build: # Fails in ~5 minutes
    needs: [lint, test]
  deploy:
    needs: [build]

6. Branch-Based Workflows

Implement different behaviors per branch:

Feature Branches / PRs:

  • Run lint + test only
  • No deployment
  • Add PR comments with test results

Develop Branch:

  • Run lint + test + build
  • Deploy to staging environment
  • Automatic deployment

Main Branch:

  • Run lint + test + build
  • Deploy to production
  • Manual approval (optional)
  • Create release tags

Example:

yaml
deploy_staging:
  if: github.ref == 'refs/heads/develop'
  # Deploy to staging

deploy_production:
  if: github.ref == 'refs/heads/main'
  environment: production  # Requires manual approval
  # Deploy to production

Workflow Decision Tree

Follow this decision tree to generate the appropriate pipeline:

  1. Which platform?

    • GitHub → Use assets/github-actions-nodejs.yml
    • GitLab → Use assets/gitlab-ci-nodejs.yml
    • CircleCI/Jenkins → Adapt GitHub Actions template
    • Unsure → Consult references/platform-comparison.md
  2. What stages are needed?

    • Always include: Lint, Test, Build
    • Optional: Security scanning, E2E tests, performance tests
    • Add deployment stage if deploying from CI
  3. Which deployment platform?

    • Vercel → Use Vercel deployment examples
    • Netlify → Use Netlify CLI approach
    • AWS → Use AWS Actions/CLI
    • Custom → Implement custom deployment script
  4. What triggers?

    • On push to main/develop
    • On pull request
    • On tag creation
    • Manual workflow dispatch
  5. What environment variables needed?

    • Platform tokens (Vercel, Netlify, AWS)
    • API keys for external services
    • Build-time environment variables
    • Feature flags

Best Practices

Security

  • Store all secrets in platform secret management (never in code)
  • Use least-privilege tokens (read-only when possible)
  • Rotate secrets regularly
  • Audit secret access permissions
  • Never log secrets (use *** masking)

Performance

  • Cache dependencies aggressively
  • Parallelize independent jobs
  • Use matrix builds for multi-version testing
  • Fail fast: Run quick checks before slow ones
  • Optimize Docker layer caching

Reliability

  • Pin exact Node.js versions (18.x not just 18)
  • Commit lockfiles (package-lock.json)
  • Add retry logic for flaky external services
  • Set reasonable timeouts (10-15 minutes max)
  • Use continue-on-error for non-critical steps

Maintainability

  • Add comments explaining complex logic
  • Use reusable workflows/templates
  • Keep configs DRY (Don't Repeat Yourself)
  • Version control all pipeline changes
  • Document required secrets in README

Common Patterns

Multi-Environment Deployment

yaml
deploy_staging:
  environment: staging
  if: github.ref == 'refs/heads/develop'

deploy_production:
  environment: production
  if: github.ref == 'refs/heads/main'
  needs: [deploy_staging]

Matrix Testing

yaml
strategy:
  matrix:
    node-version: [16.x, 18.x, 20.x]
    os: [ubuntu-latest, windows-latest]

Conditional Steps

yaml
- name: Deploy
  if: github.event_name == 'push' && github.ref == 'refs/heads/main'
  run: npm run deploy

Artifact Management

yaml
- name: Upload build
  uses: actions/upload-artifact@v4
  with:
    name: build-output
    path: .next/
    retention-days: 7

- name: Download build
  uses: actions/download-artifact@v4
  with:
    name: build-output

Troubleshooting

Pipeline Failures

  1. Check action/job logs for error messages
  2. Verify environment variables and secrets are set
  3. Test commands locally before adding to pipeline
  4. Check for platform-specific issues in documentation

Slow Builds

  1. Verify cache is working (check cache hit/miss logs)
  2. Parallelize independent jobs
  3. Use faster runners if available
  4. Optimize dependency installation

Deployment Failures

  1. Verify deployment tokens are valid
  2. Check platform status pages
  3. Review deployment logs
  4. Test deployment commands locally

Resources

Templates (assets/)

  • github-actions-nodejs.yml: Complete GitHub Actions workflow
  • gitlab-ci-nodejs.yml: Complete GitLab CI pipeline

Reference Documentation (references/)

  • platform-comparison.md: Detailed comparison of CI/CD platforms, deployment targets, best practices, and common patterns

Example Usage

User Request: "Create a GitHub Actions workflow that runs tests and deploys to Vercel"

Steps:

  1. Copy assets/github-actions-nodejs.yml template
  2. Create .github/workflows/ directory if it doesn't exist
  3. Save as .github/workflows/ci.yml
  4. Update deployment section with Vercel credentials
  5. Add secrets to GitHub repository settings:
    • VERCEL_TOKEN
    • VERCEL_ORG_ID
    • VERCEL_PROJECT_ID
  6. Commit and push to trigger workflow

User Request: "Set up GitLab CI with staging and production environments"

Steps:

  1. Copy assets/gitlab-ci-nodejs.yml template
  2. Save as .gitlab-ci.yml in repository root
  3. Configure GitLab CI/CD variables:
    • VERCEL_TOKEN
    • Other deployment credentials
  4. Review manual approval settings for production
  5. Commit to trigger pipeline

Advanced Configuration

Monorepo Support

yaml
paths:
  - 'apps/frontend/**'
  - 'packages/**'

Scheduled Runs

yaml
on:
  schedule:
    - cron: '0 2 * * *'  # Daily at 2 AM

External Service Integration

yaml
- name: Notify Slack
  uses: 8398a7/action-slack@v3
  with:
    status: ${{ job.status }}
    webhook_url: ${{ secrets.SLACK_WEBHOOK }}

Security Scanning

yaml
- name: Run security audit
  run: npm audit --audit-level=moderate

- name: Check for vulnerabilities
  uses: snyk/actions/node@master
  env:
    SNYK_TOKEN: ${{ secrets.SNYK_TOKEN }}

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cicd Pipeline Generator AI skill do?

This skill should be used when creating or configuring CI/CD pipeline files for automated testing, building, and deployment. Use this for generating GitHub Actions workflows, GitLab CI configs, CircleCI configs, or other CI/CD platform configurations. Ideal for setting up automated pipelines for Node.js/Next.js applications, including linting, testing, building, and deploying to platforms like Vercel, Netlify, or AWS.

Why use Cicd Pipeline Generator on TypingMind?

Because you install it once and use it with any model. Cicd Pipeline Generator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cicd Pipeline Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ailabs-393/ai-labs-claude-skills/tree/main/packages/skills/cicd-pipeline-generator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cicd Pipeline Generator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cicd Pipeline Generator?

As many as you like. As long as a model supports skills, you can use Cicd Pipeline Generator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cicd Pipeline Generator AI skill free?

Yes. It is published on GitHub by ailabs-393 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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