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".

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Repositório: ComeOnOliver/skillshub
Última atividade na origem: 23 de março de 2026 às 17:43
Idioma detectado do SKILL.md: inglês
Estrelas: 58
Forks: 20

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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

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