CI/CD Pipeline Setup with AI Assistance

Modern software development demands automated pipelines that build, test, and deploy code reliably. This lesson shows how Cursor IDE’s AI capabilities can help you design, implement, and maintain sophisticated CI/CD workflows that would typically require DevOps expertise.

Understanding CI/CD with AI Support

Cursor’s AI understands popular CI/CD platforms, their configuration languages, and best practices. It can generate pipeline configurations, debug failures, and suggest optimizations based on your project’s needs.

What Cursor Can Automate

Pipeline Generation

Create complete CI/CD configurations from descriptions

Test Automation

Generate comprehensive test suites and strategies

Deployment Scripts

Build deployment automation for any platform

Monitoring Setup

Configure pipeline monitoring and alerting

Setting Up Your First Pipeline

Choose Your Platform

GitHub Actions

# Agent prompt:
"Create a GitHub Actions workflow for a Node.js app with:
- Build and test on multiple Node versions
- Code coverage reporting
- Docker image building
- Deployment to AWS ECS
- Slack notifications"

GitLab CI

# Agent prompt:
"Generate GitLab CI pipeline with:
- Parallel test execution
- Docker layer caching
- Kubernetes deployment
- Environment-specific configs
- Manual approval for production"

Jenkins

// Agent prompt:
"Create Jenkinsfile with:
- Declarative pipeline syntax
- Docker agents for builds
- Blue Ocean compatible
- Shared libraries usage
- Post-build notifications"

Complete GitHub Actions Example

Let’s build a production-ready CI/CD pipeline for a full-stack application:

Step 1: Generate the Workflow

1. Create the workflow structure

   @.github/workflows
   Create a comprehensive GitHub Actions workflow:
   - Trigger on push to main and PRs
   - Run linting, testing, and security checks
   - Build Docker images with caching
   - Deploy to staging automatically
   - Deploy to production with approval

2. Cursor generates the complete workflow

   name: CI/CD Pipeline
   
   on:
     push:
       branches: [main, develop]
     pull_request:
       branches: [main]
   
   env:
     REGISTRY: ghcr.io
     IMAGE_NAME: ${{ github.repository }}
   
   jobs:
     lint-and-test:
       runs-on: ubuntu-latest
       strategy:
         matrix:
           node-version: [18.x, 20.x]
   
       steps:
       - uses: actions/checkout@v4
   
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
           node-version: ${{ matrix.node-version }}
           cache: 'npm'
   
       - name: Install dependencies
         run: npm ci
   
       - name: Run linting
         run: npm run lint
   
       - name: Run tests
         run: npm test -- --coverage
   
       - name: Upload coverage
         uses: codecov/codecov-action@v3
         with:
           token: ${{ secrets.CODECOV_TOKEN }}

3. Add security scanning

   security:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v4
   
     - name: Run security audit
       run: npm audit --production
   
     - name: Run SAST scan
       uses: github/super-linter@v5
       env:
         DEFAULT_BRANCH: main
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

Step 2: Container Building and Registry

build-and-push:
  needs: [lint-and-test, security]
  runs-on: ubuntu-latest
  permissions:
    contents: read
    packages: write

  steps:
  - uses: actions/checkout@v4

  - name: Set up Docker Buildx
    uses: docker/setup-buildx-action@v3

  - name: Log in to Container Registry
    uses: docker/login-action@v3
    with:
      registry: ${{ env.REGISTRY }}
      username: ${{ github.actor }}
      password: ${{ secrets.GITHUB_TOKEN }}

  - name: Extract metadata
    id: meta
    uses: docker/metadata-action@v5
    with:
      images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
      tags: |
        type=ref,event=branch
        type=ref,event=pr
        type=semver,pattern={{version}}
        type=sha,prefix={{branch}}-

  - name: Build and push Docker image
    uses: docker/build-push-action@v5
    with:
      context: .
      push: true
      tags: ${{ steps.meta.outputs.tags }}
      labels: ${{ steps.meta.outputs.labels }}
      cache-from: type=gha
      cache-to: type=gha,mode=max

Step 3: Deployment Configuration

AWS ECS

deploy-staging:
  needs: build-and-push
  runs-on: ubuntu-latest
  environment: staging

  steps:
  - name: Configure AWS credentials
    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

  - name: Deploy to ECS
    run: |
      aws ecs update-service \
        --cluster staging-cluster \
        --service my-app-service \
        --force-new-deployment

Kubernetes

deploy-k8s:
  needs: build-and-push
  runs-on: ubuntu-latest

  steps:
  - uses: actions/checkout@v4

  - name: Setup kubectl
    uses: azure/setup-kubectl@v3

  - name: Deploy to Kubernetes
    run: |
      kubectl set image deployment/my-app \
        my-app=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:${{ github.sha }}
      kubectl rollout status deployment/my-app

Advanced Pipeline Features

Matrix Builds

Ask Cursor to implement complex matrix strategies:

Create a matrix build that tests:
- Multiple OS (Ubuntu, Windows, macOS)
- Multiple Node versions (16, 18, 20)
- Multiple database versions (PostgreSQL 12, 13, 14)
- Skip incompatible combinations

Parallel Job Execution

test-suite:
  runs-on: ubuntu-latest
  strategy:
    matrix:
      test-group: [unit, integration, e2e]

  steps:
  - name: Run ${{ matrix.test-group }} tests
    run: npm run test:${{ matrix.test-group }}

Conditional Deployments

deploy-production:
  if: github.ref == 'refs/heads/main' && github.event_name == 'push'
  needs: [deploy-staging]
  environment:
    name: production
    url: https://app.example.com

  steps:
  - name: Wait for approval
    uses: trstringer/manual-approval@v1
    with:
      approvers: tech-lead,devops-team

Testing Strategies in CI/CD

Comprehensive Test Suite

Use Cursor to generate a complete testing strategy:

1. Unit Tests

   Generate unit tests with:
   - 80% code coverage minimum
   - Mock external dependencies
   - Test edge cases
   - Performance benchmarks

2. Integration Tests

   Create integration tests:
   - Test API endpoints
   - Database operations
   - External service integrations
   - Use test containers

3. End-to-End Tests

   Implement E2E tests:
   - Use Playwright/Cypress
   - Test critical user flows
   - Cross-browser testing
   - Visual regression tests

Test Optimization

Parallel Test Execution

Ask Cursor to split tests for parallel execution:

Optimize test suite for CI:
- Split tests into parallel groups
- Run unit tests first (fail fast)
- Cache test dependencies
- Only run affected tests on PRs

Deployment Strategies

Blue-Green Deployments

Implement blue-green deployment in GitHub Actions:
1. Deploy to green environment
2. Run smoke tests
3. Switch load balancer to green
4. Keep blue for rollback
5. Clean up old blue after success

Canary Deployments

canary-deploy:
  steps:
  - name: Deploy canary
    run: |
      kubectl set image deployment/my-app-canary \
        my-app=${{ env.NEW_IMAGE }}

  - name: Monitor metrics
    run: |
      ./scripts/check-canary-metrics.sh

  - name: Gradual rollout
    run: |
      for percent in 10 25 50 100; do
        kubectl patch deployment my-app \
          -p '{"spec":{"strategy":{"rollingUpdate":{"maxSurge":"'$percent'%"}}}}'
        sleep 300
        ./scripts/check-health.sh || exit 1
      done

Feature Flag Integration

Integrate feature flags with CI/CD:
- Use LaunchDarkly/Unleash
- Deploy code with flags off
- Gradually enable features
- Monitor metrics per feature
- Automatic rollback on errors

Pipeline Optimization

Build Performance

1. Implement caching

   - name: Cache dependencies
     uses: actions/cache@v3
     with:
       path: ~/.npm
       key: ${{ runner.os }}-node-${{ hashFiles('**/package-lock.json') }}

2. Use Docker layer caching

   - name: Build with cache
     uses: docker/build-push-action@v5
     with:
       cache-from: type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache
       cache-to: type=registry,ref=${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}:buildcache,mode=max

3. Parallelize independent jobs

   jobs:
     test:
       # Runs immediately
     security-scan:
       # Runs in parallel with test
     build:
       needs: [test, security-scan]
       # Waits for both

Cost Optimization

Ask Cursor to optimize for CI/CD costs:

Optimize GitHub Actions for cost:
- Use larger runners only when needed
- Implement job timeouts
- Skip unchanged components
- Use conditional workflows
- Archive old artifacts

Monitoring and Notifications

Pipeline Monitoring

- name: Send metrics to DataDog
  if: always()
  run: |
    curl -X POST "https://api.datadoghq.com/api/v1/series" \
      -H "Content-Type: application/json" \
      -H "DD-API-KEY: ${{ secrets.DD_API_KEY }}" \
      -d @- <<EOF
    {
      "series": [{
        "metric": "ci.pipeline.duration",
        "points": [[${{ steps.timer.outputs.time }}, ${{ steps.timer.outputs.duration }}]],
        "tags": ["pipeline:${{ github.workflow }}", "status:${{ job.status }}"]
      }]
    }
    EOF

Notification Strategies

Slack

- name: Notify Slack
  if: failure()
  uses: slackapi/slack-github-action@v1
  with:
    payload: |
      {
        "text": "Deployment failed!",
        "blocks": [{
          "type": "section",
          "text": {
            "type": "mrkdwn",
            "text": "❌ *${{ github.workflow }}* failed\n${{ github.event.pull_request.html_url || github.event.head_commit.url }}"
          }
        }]
      }
  env:
    SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK }}

Email

- name: Send email notification
  if: failure()
  uses: dawidd6/action-send-mail@v3
  with:
    server_address: smtp.gmail.com
    server_port: 465
    username: ${{ secrets.EMAIL_USERNAME }}
    password: ${{ secrets.EMAIL_PASSWORD }}
    subject: Build Failed - ${{ github.repository }}
    body: |
      Build failed for ${{ github.workflow }}
      Commit: ${{ github.sha }}
      Author: ${{ github.actor }}

Security Best Practices

Secrets Management

Security First

Never hardcode secrets in your pipeline configurations. Use Cursor to implement proper secret management:

Implement secure secrets handling:
1. Use GitHub Secrets for sensitive data
2. Rotate secrets regularly
3. Use OIDC for cloud authentication
4. Implement least-privilege access
5. Audit secret usage

Supply Chain Security

- name: Generate SBOM
  uses: anchore/sbom-action@v0
  with:
    output-file: sbom.spdx.json

- name: Sign container image
  uses: sigstore/cosign-installer@v3
  run: |
    cosign sign --yes \
      ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}@${{ steps.build.outputs.digest }}

Debugging Failed Pipelines

Common Issues and Solutions

Use Cursor to diagnose pipeline failures:

My GitHub Actions workflow is failing with "Error: Process completed with exit code 1"
Help me debug by:
1. Adding detailed logging
2. Checking environment variables
3. Validating file permissions
4. Testing locally with act

Local Pipeline Testing

# Test GitHub Actions locally
act -j build --secret-file .env.secrets

# Debug specific job
act -j test --verbose

# Use specific Docker image
act -j deploy --platform ubuntu-latest=nektos/act-environments-ubuntu:18.04

Real-World Pipeline Examples

Monorepo CI/CD

Create CI/CD for a monorepo with:
- Detect changed packages
- Run tests only for affected code
- Build and deploy changed services
- Shared dependency caching
- Parallel execution per package

Mobile App Pipeline

ios-build:
  runs-on: macos-latest
  steps:
  - uses: actions/checkout@v4

  - name: Setup Xcode
    uses: maxim-lobanov/setup-xcode@v1
    with:
      xcode-version: latest-stable

  - name: Build and test
    run: |
      xcodebuild test \
        -scheme MyApp \
        -destination 'platform=iOS Simulator,name=iPhone 14'

  - name: Upload to TestFlight
    run: |
      xcrun altool --upload-app \
        -f MyApp.ipa \
        -u ${{ secrets.APPLE_ID }} \
        -p ${{ secrets.APP_PASSWORD }}

Pipeline as Code Best Practices

1. Version Control Everything

Keep all pipeline configurations in your repository for traceability and rollback capabilities.

2. DRY Principle

Use Cursor to create reusable workflow templates and composite actions.

3. Fail Fast

Order jobs to catch failures early and save compute resources.

4. Document Pipeline Behavior

Ask Cursor to generate documentation for complex pipeline logic.

5. Monitor Everything

Track pipeline metrics, costs, and performance trends.

Measuring Pipeline Success

Key metrics to track:

• Build Success Rate: Percentage of successful builds
• Mean Time to Deploy: From commit to production
• Test Execution Time: How long tests take
• Pipeline Cost: Monthly CI/CD expenses
• Deployment Frequency: How often you deploy

Next Steps

After mastering CI/CD with Cursor:

1. Explore GitOps workflows with ArgoCD or Flux
2. Implement progressive delivery with feature flags
3. Build multi-cloud deployment pipelines
4. Create disaster recovery automation

Remember: Cursor doesn’t just help you write pipeline configurations—it helps you understand CI/CD patterns and best practices that create reliable, efficient software delivery systems.