orchestrating-deployment-pipelines
Deploy use when you need to work with deployment and CI/CD. This skill provides deployment automation and orchestration with comprehensive guidance and automation. Trigger with phrases like "deploy application", "create pipeline", or "automate deployment".
What this skill does
# Orchestrating Deployment Pipelines ## Overview Orchestrate multi-stage deployment pipelines that coordinate builds, tests, approvals, and releases across environments (dev, staging, production). Implement deployment strategies including blue-green, canary, rolling updates, and feature flags using Kubernetes, cloud-native services, and CI/CD platforms. ## Prerequisites - CI/CD platform configured (GitHub Actions, GitLab CI, Jenkins, ArgoCD) - Kubernetes cluster with `kubectl` access or cloud deployment target (ECS, Cloud Run, App Engine) - Container registry with built and tagged images ready for deployment - Environment-specific configuration (secrets, environment variables) stored securely - Monitoring and alerting configured to detect deployment failures ## Instructions 1. Define the deployment topology: target environments, promotion flow (dev -> staging -> production), and approval gates 2. Select deployment strategy per environment: rolling update for staging, canary or blue-green for production 3. Generate deployment manifests (Kubernetes Deployments, Services, Ingress) or cloud service configurations 4. Implement pre-deployment checks: database migration status, dependency health, configuration validation 5. Configure canary analysis: route 5-10% of traffic to new version, monitor error rate and latency for 15 minutes before full rollout 6. Add post-deployment verification: smoke tests, health check endpoints, synthetic monitoring 7. Implement automated rollback triggers: revert if error rate exceeds 1% or P99 latency doubles during canary phase 8. Set up deployment notifications: Slack messages with deployment status, version, environment, and commit link 9. Document the deployment runbook with manual intervention procedures for edge cases ## Output - Deployment pipeline configurations (GitHub Actions workflows, ArgoCD Applications) - Kubernetes manifests with deployment strategy annotations - Canary analysis configuration (Flagger, Argo Rollouts) - Pre/post-deployment hook scripts - Deployment runbook with rollback procedures ## Error Handling | Error | Cause | Solution | |-------|-------|---------| | `ImagePullBackOff` | Image tag not found in registry or auth failure | Verify image exists with `docker manifest inspect`; check `imagePullSecrets` | | `CrashLoopBackOff` | Application failing to start in new version | Check pod logs with `kubectl logs`; verify environment variables and config maps | | `Canary analysis failed` | Error rate or latency exceeded threshold during canary | Automatic rollback triggered; investigate logs from canary pods before retrying | | `Deployment stuck in Progressing` | Insufficient resources or pod scheduling failure | Check `kubectl describe deployment` for events; verify resource requests and node capacity | | `Database migration failed` | Schema conflict or lock timeout | Run migrations independently before deployment; add retry logic and connection timeout | ## Examples - "Create a deployment pipeline that builds on PR merge, deploys to staging automatically, runs integration tests, then requires manual approval for production with canary rollout." - "Set up Argo Rollouts for a Kubernetes deployment with 10% canary traffic, Prometheus-based analysis, and automatic rollback on error rate > 0.5%." - "Generate a blue-green deployment for an ECS service with ALB target group switching and automatic rollback on health check failure." ## Resources - Kubernetes deployment strategies: https://kubernetes.io/docs/concepts/workloads/controllers/deployment/ - Argo Rollouts: https://argoproj.github.io/argo-rollouts/ - Flagger (progressive delivery): https://flagger.app/ - AWS ECS blue-green: https://docs.aws.amazon.com/AmazonECS/latest/developerguide/deployment-type-bluegreen.html
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