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
- Define the deployment topology: target environments, promotion flow (dev -> staging -> production), and approval gates
- Select deployment strategy per environment: rolling update for staging, canary or blue-green for production
- Generate deployment manifests (Kubernetes Deployments, Services, Ingress) or cloud service configurations
- Implement pre-deployment checks: database migration status, dependency health, configuration validation
- Configure canary analysis: route 5-10% of traffic to new version, monitor error rate and latency for 15 minutes before full rollout
- Add post-deployment verification: smoke tests, health check endpoints, synthetic monitoring
- Implement automated rollback triggers: revert if error rate exceeds 1% or P99 latency doubles during canary phase
- Set up deployment notifications: Slack messages with deployment status, version, environment, and commit link
- 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
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1---2name: jeremylongshore-claude-code-plugins-plus-skills-orchestr-33description: Orchestrating Deployment Pipelines4---5# Orchestrating Deployment Pipelines67## Overview89Orchestrate 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.1011## Prerequisites1213- CI/CD platform configured (GitHub Actions, GitLab CI, Jenkins, ArgoCD)14- Kubernetes cluster with `kubectl` access or cloud deployment target (ECS, Cloud Run, App Engine)15- Container registry with built and tagged images ready for deployment16- Environment-specific configuration (secrets, environment variables) stored securely17- Monitoring and alerting configured to detect deployment failures1819## Instructions20211. Define the deployment topology: target environments, promotion flow (dev -> staging -> production), and approval gates222. Select deployment strategy per environment: rolling update for staging, canary or blue-green for production233. Generate deployment manifests (Kubernetes Deployments, Services, Ingress) or cloud service configurations244. Implement pre-deployment checks: database migration status, dependency health, configuration validation255. Configure canary analysis: route 5-10% of traffic to new version, monitor error rate and latency for 15 minutes before full rollout266. Add post-deployment verification: smoke tests, health check endpoints, synthetic monitoring277. Implement automated rollback triggers: revert if error rate exceeds 1% or P99 latency doubles during canary phase288. Set up deployment notifications: Slack messages with deployment status, version, environment, and commit link299. Document the deployment runbook with manual intervention procedures for edge cases3031## Output3233- Deployment pipeline configurations (GitHub Actions workflows, ArgoCD Applications)34- Kubernetes manifests with deployment strategy annotations35- Canary analysis configuration (Flagger, Argo Rollouts)36- Pre/post-deployment hook scripts37- Deployment runbook with rollback procedures3839## Error Handling4041| Error | Cause | Solution |42|-------|-------|---------|43| `ImagePullBackOff` | Image tag not found in registry or auth failure | Verify image exists with `docker manifest inspect`; check `imagePullSecrets` |44| `CrashLoopBackOff` | Application failing to start in new version | Check pod logs with `kubectl logs`; verify environment variables and config maps |45| `Canary analysis failed` | Error rate or latency exceeded threshold during canary | Automatic rollback triggered; investigate logs from canary pods before retrying |46| `Deployment stuck in Progressing` | Insufficient resources or pod scheduling failure | Check `kubectl describe deployment` for events; verify resource requests and node capacity |47| `Database migration failed` | Schema conflict or lock timeout | Run migrations independently before deployment; add retry logic and connection timeout |4849## Examples5051- "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."52- "Set up Argo Rollouts for a Kubernetes deployment with 10% canary traffic, Prometheus-based analysis, and automatic rollback on error rate > 0.5%."53- "Generate a blue-green deployment for an ECS service with ALB target group switching and automatic rollback on health check failure."5455## Resources5657- Kubernetes deployment strategies: https://kubernetes.io/docs/concepts/workloads/controllers/deployment/58- Argo Rollouts: https://argoproj.github.io/argo-rollouts/59- Flagger (progressive delivery): https://flagger.app/60- AWS ECS blue-green: https://docs.aws.amazon.com/AmazonECS/latest/developerguide/deployment-type-bluegreen.html6162---63> Converted and distributed by [TomeVault](https://tomevault.io/claim/jeremylongshore) — claim your Tome and manage your conversions.64<!-- tomevault:4.0:skill_md:2026-04-11 -->
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