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.
npx skills add https://github.com/ailabs-393/ai-labs-claude-skills --skill cicd-pipeline-generator
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:
Refer to references/platform-comparison.md for detailed platform comparisons, pros/cons, and use case recommendations.
Generate pipeline configs following these principles:
Structure pipelines with these standard stages:
npm ciImplement effective caching to speed up builds:
# Cache node_modules based on package-lock.json
cache:
key: ${{ hashFiles('package-lock.json') }}
paths:
- node_modules/
- .npm/
Configure necessary environment variables:
NODE_ENV: Set to production for buildsUse provided templates from assets/ directory:
GitHub Actions Template (assets/github-actions-nodejs.yml):
GitLab CI Template (assets/gitlab-ci-nodejs.yml):
To use a template:
.github/workflows/ci.yml.gitlab-ci.ymlFor GitHub Actions:
- 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 settingsVERCEL_ORG_ID: From Vercel project settingsVERCEL_PROJECT_ID: From Vercel project settings- 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 }}
- 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 "/*"
Configure test execution with proper reporting:
Jest Configuration:
- 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:
# 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]
Implement different behaviors per branch:
Feature Branches / PRs:
Develop Branch:
Main Branch:
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:
assets/github-actions-nodejs.ymlassets/gitlab-ci-nodejs.ymlreferences/platform-comparison.md*** masking)18.x not just 18)package-lock.json)continue-on-error for non-critical stepsdeploy_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]
- name: Deploy
if: github.event_name == 'push' && github.ref == 'refs/heads/main'
run: npm run deploy
- 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
assets/)github-actions-nodejs.yml: Complete GitHub Actions workflowgitlab-ci-nodejs.yml: Complete GitLab CI pipelinereferences/)platform-comparison.md: Detailed comparison of CI/CD platforms, deployment targets, best practices, and common patternsUser Request: "Create a GitHub Actions workflow that runs tests and deploys to Vercel"
Steps:
assets/github-actions-nodejs.yml template.github/workflows/ directory if it doesn't exist.github/workflows/ci.ymlVERCEL_TOKENVERCEL_ORG_IDVERCEL_PROJECT_IDUser Request: "Set up GitLab CI with staging and production environments"
Steps:
assets/gitlab-ci-nodejs.yml template.gitlab-ci.yml in repository rootVERCEL_TOKENpaths:
- 'apps/frontend/**'
- 'packages/**'
on:
schedule:
- cron: '0 2 * * *' # Daily at 2 AM
- name: Notify Slack
uses: 8398a7/action-slack@v3
with:
status: ${{ job.status }}
webhook_url: ${{ secrets.SLACK_WEBHOOK }}
- 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 }}
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take ailabs-393/cicd-pipeline-generator from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference npm.
Without those the skill loads but fails at the first command.