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orchestrating-deployment-pipelines

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Description

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".

Tool Access

This skill is limited to using the following tools:

ReadWriteEditGrepGlobBash(git:*)Bash(docker:*)Bash(kubectl:*)
Supporting Assets
View in Repository
assets/README.md
assets/example_config.yaml
assets/pipeline_template.yaml
references/README.md
scripts/README.md
scripts/init_pipeline.sh
Skill Content

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

ErrorCauseSolution
ImagePullBackOffImage tag not found in registry or auth failureVerify image exists with docker manifest inspect; check imagePullSecrets
CrashLoopBackOffApplication failing to start in new versionCheck pod logs with kubectl logs; verify environment variables and config maps
Canary analysis failedError rate or latency exceeded threshold during canaryAutomatic rollback triggered; investigate logs from canary pods before retrying
Deployment stuck in ProgressingInsufficient resources or pod scheduling failureCheck kubectl describe deployment for events; verify resource requests and node capacity
Database migration failedSchema conflict or lock timeoutRun 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

Stats
Stars1669
Forks210
Last CommitMar 11, 2026

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