deployment-patterns_skill

Overview

This skill codifies production deployment workflows, CI/CD pipeline patterns, Docker containerization guidance, health checks, rollback strategies, and a production readiness checklist for web applications. It focuses on practical, battle-tested patterns that reduce downtime, speed rollout, and make rollbacks reliable. The content is platform-agnostic and includes concrete examples for Node.js, Go, and Python images plus CI/CD pipeline stages.

How this skill works

The skill inspects deployment requirements and recommends an appropriate rollout strategy (rolling, blue-green, or canary) based on risk and traffic. It provides multi-stage Dockerfile templates, health check implementations, Kubernetes probe configurations, CI/CD job patterns, environment validation, and a rollback checklist. It also supplies a production readiness checklist covering application, infrastructure, monitoring, security, and operations.

When to use it

• Setting up or improving CI/CD pipelines for web services
• Dockerizing apps and optimizing container image builds
• Choosing a deployment strategy for a release (rolling, blue-green, canary)
• Implementing health checks, readiness/liveness probes, and startup probes
• Preparing for or validating a production release

Best practices

• Use multi-stage builds, pinned base image tags, and run containers as non-root users
• Validate configuration at startup (schema validation) and inject secrets via env or secret manager
• Include HEALTHCHECK in images and expose both simple and detailed health endpoints
• Enforce CI stages: lint → typecheck → tests → build → staging deploy → smoke tests → production
• Set resource limits, horizontal autoscaling, and structured JSON logging without PII

Example use cases

• Implement a GitHub Actions pipeline that builds, tests, pushes a container image, and triggers a staged deploy
• Switch a critical service to blue-green deploys for instant rollback capability
• Run a canary rollout to route 5% of traffic to a new version and monitor error/latency metrics
• Use a multi-stage Dockerfile for Node.js to produce small production images and include a healthcheck
• Validate environment variables with a schema library and fail fast on startup for misconfiguration