Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
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.
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.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
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.
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.
Generate pipeline configs following these principles: Pipeline Stages Structure pipelines with these standard stages: 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 Lint Run ESLint for code quality Run TypeScript type checking Fail fast on linting errors Test Execute unit tests Execute integration tests Generate code coverage reports Upload coverage to reporting services (Codecov, Coveralls) Build Create production build Verify build succeeds Store build artifacts 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: # 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
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: Copy the appropriate template file Place in the correct location: GitHub Actions: .github/workflows/ci.yml GitLab CI: .gitlab-ci.yml Customize deployment targets, environment variables, and branch names Add required secrets to platform settings
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: 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
Follow this decision tree to generate the appropriate pipeline: Which platform? GitHub โ Use assets/github-actions-nodejs.yml GitLab โ Use assets/gitlab-ci-nodejs.yml CircleCI/Jenkins โ Adapt GitHub Actions template Unsure โ Consult references/platform-comparison.md What stages are needed? Always include: Lint, Test, Build Optional: Security scanning, E2E tests, performance tests Add deployment stage if deploying from CI Which deployment platform? Vercel โ Use Vercel deployment examples Netlify โ Use Netlify CLI approach AWS โ Use AWS Actions/CLI Custom โ Implement custom deployment script What triggers? On push to main/develop On pull request On tag creation Manual workflow dispatch What environment variables needed? Platform tokens (Vercel, Netlify, AWS) API keys for external services Build-time environment variables Feature flags
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)
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
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
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
deploy_staging: environment: staging if: github.ref == 'refs/heads/develop' deploy_production: environment: production if: github.ref == 'refs/heads/main' needs: [deploy_staging]
strategy: matrix: node-version: [16.x, 18.x, 20.x] os: [ubuntu-latest, windows-latest]
Check action/job logs for error messages Verify environment variables and secrets are set Test commands locally before adding to pipeline Check for platform-specific issues in documentation
Verify cache is working (check cache hit/miss logs) Parallelize independent jobs Use faster runners if available Optimize dependency installation
Verify deployment tokens are valid Check platform status pages Review deployment logs Test deployment commands locally
github-actions-nodejs.yml: Complete GitHub Actions workflow gitlab-ci-nodejs.yml: Complete GitLab CI pipeline
platform-comparison.md: Detailed comparison of CI/CD platforms, deployment targets, best practices, and common patterns
User Request: "Create a GitHub Actions workflow that runs tests and deploys to Vercel" Steps: Copy assets/github-actions-nodejs.yml template Create .github/workflows/ directory if it doesn't exist Save as .github/workflows/ci.yml Update deployment section with Vercel credentials Add secrets to GitHub repository settings: VERCEL_TOKEN VERCEL_ORG_ID VERCEL_PROJECT_ID Commit and push to trigger workflow User Request: "Set up GitLab CI with staging and production environments" Steps: Copy assets/gitlab-ci-nodejs.yml template Save as .gitlab-ci.yml in repository root Configure GitLab CI/CD variables: VERCEL_TOKEN Other deployment credentials Review manual approval settings for production Commit to trigger pipeline
paths: - 'apps/frontend/**' - 'packages/**'
on: schedule: - cron: '0 2 * * *' # Daily at 2 AM
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Largest current source with strong distribution and engagement signals.