deployment-automation

name	deployment-automation
description	CI/CD pipeline design, deployment strategies, and release automation. Use when implementing GitHub Actions, container deployments, release workflows, and infrastructure automation.

Deployment Automation Skill

✨ SKILL ACTIVATED: deployment-automation
   Purpose: CI/CD pipeline design and deployment strategies
   Functionality: Architecture planning, CI pipeline design, deployment patterns, monitoring setup
   Output: Complete CI/CD pipeline specifications and automation workflows
   Scope: Portable across VS Code, CLI, Claude, Cursor, and compatible agents

Purpose

Systematic methodology for designing and implementing CI/CD pipelines, deployment strategies, and release automation. Covers GitHub Actions workflows, container orchestration, infrastructure-as-code patterns, and automated release processes.

When to Use This Skill

• Designing CI/CD pipelines and workflows
• Implementing GitHub Actions automation
• Planning container deployments (Docker, Kubernetes)
• Creating release and versioning strategies
• Setting up infrastructure-as-code (Terraform, CloudFormation)
• Automating testing, building, and deployment phases
• Establishing monitoring and rollback strategies
• Planning multi-environment deployments

Deployment Framework

Phase 1: Architecture Planning

Objective: Define deployment strategy and infrastructure

Strategy Options:

1. Blue-Green Deployment

   • Run two identical production environments
   • Switch traffic between versions
   • Pros: Zero downtime, instant rollback
   • Cons: Double infrastructure cost, data consistency challenges
   • Best for: Critical applications, frequent releases

2. Canary Deployment

   • Release to small percentage of users first
   • Gradually increase traffic percentage
   • Monitor metrics and rollback if needed
   • Pros: Low risk, real-world testing
   • Cons: Complex monitoring, slower rollout
   • Best for: High-traffic applications, major changes

3. Rolling Deployment

   • Update instances/containers one at a time
   • Maintain availability during updates
   • Pros: No extra infrastructure, simple monitoring
   • Cons: Longer deployment time, brief capacity reduction
   • Best for: Microservices, stateless applications

4. Feature Flags

   • Deploy behind feature toggles
   • Control feature availability without redeployment
   • Pros: Instant enable/disable, A/B testing
   • Cons: Code complexity, configuration management
   • Best for: Rapid development, large teams

Selection Matrix:

Factor	Blue-Green	Canary	Rolling	Feature Flag
Complexity	Medium	High	Low	Medium
Rollback Speed	Instant	Minutes	Minutes	Instant
Cost	2x	1x	1x	1x
Risk	Low	Low	Medium	Low

Phase 2: CI Pipeline Design

Objective: Automate code validation and artifact creation

Pipeline Stages:

Push Code
  ↓ [Trigger]
1. Build
   - Compile/transpile code
   - Run linters
   - Generate artifacts
  ↓ [Success]
2. Test
   - Unit tests
   - Integration tests
   - Coverage reports
  ↓ [Success]
3. Security
   - SAST (static analysis)
   - Dependency scanning
   - Container scanning
  ↓ [Success]
4. Artifact
   - Build Docker image / packages
   - Push to registry
   - Create release artifacts
  ↓ [Success]
5. Deploy (Staging)
   - Deploy to staging environment
   - Run smoke tests
   - Verify functionality
  ↓ [Approval Gate]
6. Deploy (Production)
   - Execute deployment strategy
   - Monitor health metrics
   - Enable rollback if needed

GitHub Actions Example Structure:

name: CI/CD Pipeline
on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Build
        run: npm run build
  
  test:
    needs: build
    runs-on: ubuntu-latest
    steps:
      - name: Run tests
        run: npm test
  
  deploy:
    needs: [build, test]
    if: github.ref == 'refs/heads/main'
    runs-on: ubuntu-latest
    steps:
      - name: Deploy
        run: npm run deploy

Phase 3: Environment Configuration

Objective: Manage configuration across environments

Environment Levels:

Level	Purpose	Data	Secrets	Update Frequency
Dev	Developer testing	Fake/seeded	Non-prod	Per commit
Staging	Pre-production testing	Production-like	Non-prod	Per release candidate
Production	Live user traffic	Real data	Prod secrets	Controlled releases

Configuration Management:

1. Environment Variables

   • Use .env files for development (NOT committed)
   • Use GitHub Secrets for sensitive data
   • Use ConfigMaps for Kubernetes
   • Use Systems Manager Parameter Store (AWS)

2. Secrets Rotation

   • Automate secret rotation schedules
   • Implement graceful secret transitions
   • Audit secret access
   • Revoke compromised secrets immediately

3. Configuration-as-Code

   # Example: Environment config
   environments:
     development:
       database_url: postgresql://dev-db:5432/app_dev
       log_level: DEBUG
       cache_ttl: 300
     production:
       database_url: # from secret
       log_level: ERROR
       cache_ttl: 3600
   

Phase 4: Testing Strategy

Objective: Comprehensive validation before production

Test Layers:

1. Unit Tests

   • Fast execution (< 1 second per test)
   • 80%+ code coverage target
   • Test single functions/methods
   • Run on every commit

2. Integration Tests

   • 15-30 second execution
   • Test component interactions
   • Use test databases/services
   • Run on pull requests

3. End-to-End Tests

   • Full user workflows
   • 1-5 minute execution
   • Browser/API testing
   • Run before production deployment

4. Performance Tests

   • Load testing (concurrent users)
   • Stress testing (peak conditions)
   • Profiling and optimization
   • Establish performance baselines

5. Security Tests

   • SAST (SonarQube, CodeQL)
   • DAST (Burp Suite, OWASP ZAP)
   • Dependency scanning (Dependabot, Snyk)
   • Container scanning (Trivy)

Phase 5: Monitoring & Observability

Objective: Track health and performance in production

Key Metrics:

Availability:
  - Uptime percentage (target: 99.9%+)
  - Error rates (target: < 0.1%)
  - Successful request rate

Performance:
  - Response time (p50, p95, p99)
  - Throughput (requests/second)
  - Database query latency

Resources:
  - CPU usage
  - Memory usage
  - Disk usage
  - Network bandwidth

Business:
  - User signups
  - Transactions completed
  - Feature adoption

Observability Stack:

1. Logs

   • Structured logging (JSON format)
   • Log levels: ERROR, WARN, INFO, DEBUG
   • Centralized logging (ELK, Splunk, CloudWatch)
   • Searchable and filterable

2. Metrics

   • Prometheus/CloudWatch for metrics collection
   • Dashboards for visualization
   • Alerting on thresholds
   • Time-series retention

3. Traces

   • Distributed tracing (Jaeger, Datadog)
   • Request flow across services
   • Latency breakdown
   • Error context

4. Alerts

   • Alert on error rate spike
   • Alert on latency increase
   • Alert on resource exhaustion
   • Page on-call team

Phase 6: Rollback Strategy

Objective: Recover quickly from failed deployments

Rollback Approaches:

1. Instant Rollback (Blue-Green)

   • Switch load balancer to previous version
   • Execution time: < 1 minute
   • Data migration: Backward compatible required

2. Gradual Rollback (Canary)

   • Reduce traffic to new version
   • Route back to stable version
   • Execution time: 5-15 minutes

3. Database Rollback

   • Non-breaking schema changes (add columns, new tables)
   • Never delete data immediately
   • Use feature flags for data compatibility
   • Plan migration path

4. Rollback Checklist

   • Identify root cause
   • Notify on-call team
   • Initiate rollback procedure
   • Verify previous version health
   • Post-mortem after resolution

Phase 7: Release Management

Objective: Systematic version releases

Version Strategy (Semantic Versioning):

MAJOR.MINOR.PATCH (e.g., 1.5.3)

MAJOR: Breaking changes
MINOR: New features (backward compatible)
PATCH: Bug fixes

Examples:
- 1.0.0 → 2.0.0 (major breaking change)
- 1.0.0 → 1.1.0 (new feature)
- 1.0.0 → 1.0.1 (bug fix)

Release Workflow:

1. Planning

   • Define features/fixes for release
   • Set target release date
   • Plan migration path if needed

2. Development

   • Feature branches with pull requests
   • Automated testing on all PRs
   • Code review requirements

3. Staging

   • Deploy to staging environment
   • Run full test suite
   • Performance/load testing
   • Stakeholder approval

4. Release

   • Create version tag
   • Build and push artifacts
   • Update changelog
   • Deploy to production
   • Announce release

5. Post-Release

   • Monitor metrics
   • Gather user feedback
   • Plan hotfixes if needed
   • Document lessons learned

Common Patterns

GitHub Actions Workflow Template

name: Release
on:
  workflow_dispatch:  # Manual trigger
    inputs:
      version:
        description: 'Release version'
        required: true

jobs:
  release:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      
      - name: Create tag
        run: |
          git tag ${{ github.event.inputs.version }}
          git push origin ${{ github.event.inputs.version }}
      
      - name: Build artifacts
        run: npm run build
      
      - name: Create release
        uses: actions/create-release@v1
        with:
          tag_name: ${{ github.event.inputs.version }}
          release_name: Release ${{ github.event.inputs.version }}
      
      - name: Deploy to production
        run: npm run deploy:prod

Canary Deployment with Monitoring

name: Deploy with Canary
jobs:
  deploy:
    steps:
      - name: Deploy canary (10%)
        run: kubectl set image deploy/app app=myapp:${{ github.sha }} --record
      
      - name: Wait and monitor
        run: |
          for i in {1..5}; do
            ERROR_RATE=$(curl https://metrics/error_rate)
            if [ $ERROR_RATE -gt 1 ]; then
              echo "High error rate detected: $ERROR_RATE"
              kubectl rollout undo deploy/app
              exit 1
            fi
            sleep 60
          done
      
      - name: Full rollout (100%)
        run: kubectl set image deploy/app app=myapp:${{ github.sha }}

Quality Checklist

• CI/CD pipeline passes all builds
• Test coverage > 80%
• All security scans passing
• Deployment strategy documented
• Rollback procedure tested
• Monitoring alerts configured
• Team trained on deployment process
• Release notes prepared
• Stakeholder approval obtained
• On-call team notified

Best Practices

1. Automate Everything - Minimize manual steps
2. Test Before Deploy - Comprehensive automated testing
3. Environment Parity - Staging mirrors production
4. Fast Feedback - CI/CD completes in < 10 minutes
5. Observable - Logs, metrics, traces for troubleshooting
6. Documented - Playbooks for common scenarios
7. Gated - Approval gates for production changes
8. Measurable - DORA metrics (deployment frequency, lead time, MTTR, change failure rate)

Integration with Other Skills

Works well with:

• repo-analysis — Detect existing CI/CD infrastructure and deployment patterns
• planning — Create phased deployment implementation plans with risk mitigation
• orchestration — Design conductor/subagent systems for complex multi-stage deployments
• documentation — Generate runbooks, deployment guides, and incident response docs

Typical workflow: repo-analysis discovers current deployment state → this skill designs target CI/CD architecture → planning creates migration roadmap → orchestration coordinates multi-phase rollout → documentation generates operational guides

Success Criteria

• CI/CD pipeline successfully builds and deploys application
• All test phases pass (unit, integration, e2e)
• Security scans integrated and passing
• Deployment completes in acceptable time (< 15 minutes typical)
• Rollback procedure tested and documented
• Monitoring and alerting configured for deployment metrics
• Environment promotion strategy clearly defined
• Team trained on deployment process and runbooks available
• DORA metrics tracked and improving over time
• Infrastructure-as-code commits pass validation checks

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