Deployment Pipeline Design Architecture patterns for multi-stage CI/CD pipelines with approval gates and deployment strategies. Do not use this skill when The task is unrelated to deployment pipeline design You need a different domain or tool outside this scope Instructions Clarify goals, constraints, and required inputs. Apply relevant best practices and validate outcomes. Provide actionable steps and verification. If detailed examples are required, open resources/implementation-playbook.md . Purpose Design robust, secure deployment pipelines that balance speed with safety through proper stage organization and approval workflows. Use this skill when Design CI/CD architecture Implement deployment gates Configure multi-environment pipelines Establish deployment best practices Implement progressive delivery Pipeline Stages Standard Pipeline Flow ┌─────────┐ ┌──────┐ ┌─────────┐ ┌────────┐ ┌──────────┐ │ Build │ → │ Test │ → │ Staging │ → │ Approve│ → │Production│ └─────────┘ └──────┘ └─────────┘ └────────┘ └──────────┘ Detailed Stage Breakdown Source - Code checkout Build - Compile, package, containerize Test - Unit, integration, security scans Staging Deploy - Deploy to staging environment Integration Tests - E2E, smoke tests Approval Gate - Manual approval required Production Deploy - Canary, blue-green, rolling Verification - Health checks, monitoring Rollback - Automated rollback on failure Approval Gate Patterns Pattern 1: Manual Approval

GitHub Actions

production-deploy : needs : staging - deploy environment : name : production url : https : //app.example.com runs-on : ubuntu - latest steps : - name : Deploy to production run : |

Deployment commands

Pattern 2: Time-Based Approval

GitLab CI

deploy:production : stage : deploy script : - deploy.sh production environment : name : production when : delayed start_in : 30 minutes only : - main Pattern 3: Multi-Approver

Azure Pipelines

stages : - stage : Production dependsOn : Staging jobs : - deployment : Deploy environment : name : production resourceType : Kubernetes strategy : runOnce : preDeploy : steps : - task : ManualValidation@0 inputs : notifyUsers : 'team-leads@example.com' instructions : 'Review staging metrics before approving' Reference: See assets/approval-gate-template.yml Deployment Strategies 1. Rolling Deployment apiVersion : apps/v1 kind : Deployment metadata : name : my - app spec : replicas : 10 strategy : type : RollingUpdate rollingUpdate : maxSurge : 2 maxUnavailable : 1 Characteristics: Gradual rollout Zero downtime Easy rollback Best for most applications 2. Blue-Green Deployment

Blue (current)

kubectl apply

f blue

deployment.yaml kubectl label service my - app version=blue

Green (new)

kubectl apply

f green

deployment.yaml

Test green environment

kubectl label service my

app version=green

Rollback if needed

kubectl label service my

app version=blue Characteristics: Instant switchover Easy rollback Doubles infrastructure cost temporarily Good for high-risk deployments 3. Canary Deployment apiVersion : argoproj.io/v1alpha1 kind : Rollout metadata : name : my - app spec : replicas : 10 strategy : canary : steps : - setWeight : 10 - pause : { duration : 5m } - setWeight : 25 - pause : { duration : 5m } - setWeight : 50 - pause : { duration : 5m } - setWeight : 100 Characteristics: Gradual traffic shift Risk mitigation Real user testing Requires service mesh or similar 4. Feature Flags from flagsmith import Flagsmith flagsmith = Flagsmith ( environment_key = "API_KEY" ) if flagsmith . has_feature ( "new_checkout_flow" ) :

New code path

process_checkout_v2 ( ) else :

Existing code path

process_checkout_v1 ( ) Characteristics: Deploy without releasing A/B testing Instant rollback Granular control Pipeline Orchestration Multi-Stage Pipeline Example name : Production Pipeline on : push : branches : [ main ] jobs : build : runs-on : ubuntu - latest steps : - uses : actions/checkout@v4 - name : Build application run : make build - name : Build Docker image run : docker build - t myapp : $ { { github.sha } } . - name : Push to registry run : docker push myapp : $ { { github.sha } } test : needs : build runs-on : ubuntu - latest steps : - name : Unit tests run : make test - name : Security scan run : trivy image myapp : $ { { github.sha } } deploy-staging : needs : test runs-on : ubuntu - latest environment : name : staging steps : - name : Deploy to staging run : kubectl apply - f k8s/staging/ integration-test : needs : deploy - staging runs-on : ubuntu - latest steps : - name : Run E2E tests run : npm run test : e2e deploy-production : needs : integration - test runs-on : ubuntu - latest environment : name : production steps : - name : Canary deployment run : | kubectl apply -f k8s/production/ kubectl argo rollouts promote my-app verify : needs : deploy - production runs-on : ubuntu - latest steps : - name : Health check run : curl - f https : //app.example.com/health - name : Notify team run : | curl -X POST ${{ secrets.SLACK_WEBHOOK }} \ -d '{"text":"Production deployment successful!"}' Pipeline Best Practices Fail fast - Run quick tests first Parallel execution - Run independent jobs concurrently Caching - Cache dependencies between runs Artifact management - Store build artifacts Environment parity - Keep environments consistent Secrets management - Use secret stores (Vault, etc.) Deployment windows - Schedule deployments appropriately Monitoring integration - Track deployment metrics Rollback automation - Auto-rollback on failures Documentation - Document pipeline stages Rollback Strategies Automated Rollback deploy-and-verify : steps : - name : Deploy new version run : kubectl apply - f k8s/ - name : Wait for rollout run : kubectl rollout status deployment/my - app - name : Health check id : health run : | for i in {1..10}; do if curl -sf https://app.example.com/health; then exit 0 fi sleep 10 done exit 1 - name : Rollback on failure if : failure() run : kubectl rollout undo deployment/my - app Manual Rollback

List revision history

kubectl rollout history deployment/my-app

Rollback to previous version

kubectl rollout undo deployment/my-app

Rollback to specific revision

kubectl rollout undo deployment/my-app --to-revision

3 Monitoring and Metrics Key Pipeline Metrics Deployment Frequency - How often deployments occur Lead Time - Time from commit to production Change Failure Rate - Percentage of failed deployments Mean Time to Recovery (MTTR) - Time to recover from failure Pipeline Success Rate - Percentage of successful runs Average Pipeline Duration - Time to complete pipeline Integration with Monitoring - name : Post - deployment verification run : |

Wait for metrics stabilization

sleep 60

Check error rate

ERROR_RATE=$(curl

s "$PROMETHEUS_URL/api/v1/query ? query=rate(http_errors_total [ 5m ] )" | jq '.data.result [ 0 ] .value [ 1 ] ') if (( $(echo "$ERROR_RATE

0.01" | bc - l) )); then echo "Error rate too high : $ERROR_RATE" exit 1 fi Reference Files references/pipeline-orchestration.md - Complex pipeline patterns assets/approval-gate-template.yml - Approval workflow templates