cd-pipeline-generator
Scannednpx machina-cli add skill ArabelaTso/Skills-4-SE/cd-pipeline-generator --openclawCD Pipeline Generator
Overview
Generate production-ready GitHub Actions deployment workflows that automate deployments to staging and production environments with environment protection rules, approval gates, and secrets management.
Workflow
1. Identify Deployment Target
Determine the cloud platform and deployment method:
- AWS: ECS, Elastic Beanstalk, EC2, Lambda
- GCP: Cloud Run, App Engine, Compute Engine, Cloud Functions
- Azure: App Service, Container Instances, Functions
2. Select Template
Use the appropriate template from assets/ based on cloud platform:
deploy-aws.yml- AWS deployments (ECS, Elastic Beanstalk, Lambda)deploy-gcp.yml- GCP deployments (Cloud Run, App Engine)deploy-azure.yml- Azure deployments (App Service, Container Instances)
3. Configure Environments
Set up GitHub environment protection rules for staging and production:
Staging environment:
- Auto-deploy on merge to main/master
- No approval required
- Use staging secrets and variables
Production environment:
- Manual approval required
- Deploy on workflow_dispatch or tag push
- Use production secrets and variables
- Optional: Restrict to specific branches
4. Configure Secrets
Add required secrets to GitHub repository settings (Settings → Secrets and variables → Actions):
AWS:
AWS_ACCESS_KEY_IDAWS_SECRET_ACCESS_KEYAWS_REGION
GCP:
GCP_PROJECT_IDGCP_SERVICE_ACCOUNT_KEY
Azure:
AZURE_CREDENTIALSAZURE_SUBSCRIPTION_ID
5. Customize Deployment Steps
Adapt the template to project-specific deployment needs:
Build artifacts: Add build steps before deployment
- name: Build application
run: npm run build # or: python -m build, go build, cargo build
Docker images: Build and push container images
- name: Build Docker image
run: docker build -t $IMAGE_NAME:$TAG .
- name: Push to registry
run: docker push $IMAGE_NAME:$TAG
Database migrations: Run migrations before deployment
- name: Run migrations
run: npm run migrate # or: alembic upgrade head, rails db:migrate
Health checks: Verify deployment success
- name: Health check
run: curl -f https://$DEPLOYMENT_URL/health || exit 1
6. Set Deployment Triggers
Configure when deployments run:
Staging: Auto-deploy on push to main
on:
push:
branches: [main]
Production: Manual trigger or tag-based
on:
workflow_dispatch:
push:
tags:
- 'v*'
7. Place Workflow File
Create deployment workflow at .github/workflows/deploy.yml. If multiple deployment workflows are needed (e.g., separate staging and production), use descriptive names:
.github/workflows/deploy-staging.yml.github/workflows/deploy-production.yml
Template Features
All templates include:
- Environment separation: Distinct staging and production deployments
- Approval gates: Production deployments require manual approval
- Secrets management: Secure credential handling via GitHub secrets
- Deployment status: Clear success/failure reporting
- Rollback support: Easy revert to previous versions
- Conditional execution: Deploy only when tests pass
Security Best Practices
- Never commit credentials or API keys to the repository
- Use GitHub environments to scope secrets appropriately
- Enable required reviewers for production deployments
- Use OIDC authentication instead of long-lived credentials when possible
- Implement deployment windows for production (e.g., business hours only)
- Add deployment notifications to Slack/email
Customization Examples
Add deployment notification:
- name: Notify deployment
if: always()
uses: 8398a7/action-slack@v3
with:
status: ${{ job.status }}
text: 'Deployment to ${{ github.event.inputs.environment }} ${{ job.status }}'
env:
SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK }}
Add rollback capability:
- name: Rollback on failure
if: failure()
run: |
echo "Deployment failed, rolling back..."
# Platform-specific rollback commands
Restrict production deployment time:
- name: Check deployment window
run: |
HOUR=$(date +%H)
if [ $HOUR -lt 9 ] || [ $HOUR -gt 17 ]; then
echo "Deployments only allowed 9 AM - 5 PM"
exit 1
fi
Tips
- Start with staging deployments to validate the workflow
- Use environment-specific configuration files (e.g.,
config.staging.json,config.production.json) - Implement blue-green or canary deployments for zero-downtime updates
- Add deployment metrics and monitoring
- Document rollback procedures in the repository
- Test deployment workflows in a separate test environment first
Source
git clone https://github.com/ArabelaTso/Skills-4-SE/blob/main/skills/cd-pipeline-generator/SKILL.mdView on GitHub Overview
CD Pipeline Generator creates production-ready GitHub Actions workflows that automate deployments to staging and production across AWS, GCP, and Azure. It includes environment-specific templates with approval gates, secrets management, and rollback capabilities to support reliable, risk-controlled releases. Templates are selected from assets and tailored per cloud.
How This Skill Works
Identify the cloud platform and service to deploy (e.g., AWS ECS, GCP Cloud Run, Azure App Service). Then pick the matching template from assets (deploy-aws.yml, deploy-gcp.yml, deploy-azure.yml) and configure environment protections, secrets, and triggers. Finally, tailor build, containerization, migrations, and health checks before saving the workflow under .github/workflows.
When to Use It
- When starting a continuous deployment (CD) pipeline for staging and production across AWS, GCP, or Azure.
- When you need environment protection rules and manual approvals for production deployments.
- When you want to standardize deployment templates with asset-based templates (deploy-aws.yml, deploy-gcp.yml, deploy-azure.yml).
- When you need secrets management integrated via GitHub Secrets.
- When you want easy rollback support and clear deployment status reporting.
Quick Start
- Step 1: Identify deployment target (cloud platform and service).
- Step 2: Select the matching template from assets (deploy-aws.yml, deploy-gcp.yml, deploy-azure.yml).
- Step 3: Configure environments, add secrets, customize steps, and place the workflow under .github/workflows.
Best Practices
- Use separate GitHub environments for staging and production with appropriate protection rules.
- Never commit credentials; store them as GitHub Secrets and reference them in workflows.
- Require reviewers or approvals for production deployments to enforce governance.
- Prefer OIDC-based authentication where possible to minimize long-lived credentials.
- Incorporate pre-deployment checks (build, migrations, health checks) and maintain rollback steps.
Example Use Cases
- Deploy to AWS ECS using deploy-aws.yml with auto-deploy to staging and a manual-approval prod flow.
- Deploy to GCP Cloud Run via deploy-gcp.yml with a production approval gate and secrets for the service.
- Deploy to Azure App Service using deploy-azure.yml with a workflow_dispatch trigger for production.
- Include database migrations in the deployment steps to ensure schema updates precede user traffic.
- Maintain separate workflows (.github/workflows/deploy-staging.yml and deploy-production.yml) for clarity and control.