#DevOps.

You are a DevOps engineer specializing in containerization. For my [language/framework] application, create: 1) An optimized multi-stage Dockerfile — minimize final image size, use non-root user, leverage layer caching correctly, add health check, 2) A Docker Compose file for local development with hot-reload, 3) A GitHub Actions CI/CD pipeline YAML that: runs linting and tests on PR, builds and pushes image to [registry] on merge to main, deploys to [environment: ECS/K8s/Cloud Run], and notifies Slack on failure. App details: [describe app, dependencies, env vars needed]. Environment variables must use GitHub Secrets (list which ones are needed).

You are a DevOps engineer specializing in CI/CD automation. Create a complete GitHub Actions workflow for [project type: Node.js/Python/Go/Java]. The pipeline must: 1) Trigger on: push to main, pull requests, and manual workflow dispatch, 2) Jobs: a) lint-and-test (runs linting, unit tests, coverage report, uploads to Codecov), b) security-scan (runs SAST with appropriate tool for the language), c) build-and-push (builds Docker image, tags with commit SHA and "latest", pushes to [ECR/GCR/Docker Hub]), d) deploy-staging (deploys to staging on every merge to main), e) deploy-prod (deploys to production on tag push, requires manual approval). Include: matrix testing across [list versions], caching for dependencies, environment secrets usage, and a Slack notification on deploy success/failure. Stack specifics: [describe your stack]. Registry: [container registry]. Deploy target: [ECS/EKS/Cloud Run/etc.].

You are a Terraform and Infrastructure as Code expert. Write a production-grade Terraform module for: [infrastructure component, e.g., "a highly available RDS PostgreSQL cluster on AWS" or "a GKE autopilot cluster with VPC-native networking"]. Module requirements: 1) Complete main.tf with all necessary resources, 2) variables.tf with types, descriptions, defaults, and validation rules, 3) outputs.tf with useful outputs for downstream modules, 4) versions.tf with provider version constraints, 5) README.md with usage example and input/output documentation. Best practices to include: remote state backend configuration, tagging strategy, encryption at rest/in transit, least privilege IAM, cost optimization options. Provider: [AWS/GCP/Azure]. Environment parameterization: the module must work for dev/staging/prod via workspace or variable. Avoid: hardcoded credentials, overly permissive IAM, unencrypted resources.

You are a Kubernetes engineer with expertise in production-grade deployments. Generate complete, production-ready Kubernetes manifests for the following workload: Application: [name]. Type: [Deployment/StatefulSet/DaemonSet]. Container image: [image:tag]. Port(s): [list]. Environment variables: [list]. Resource requirements: CPU [Xm request / Xm limit], Memory [XMi request / XMi limit]. Replicas: [X]. Required manifests: 1) Deployment/StatefulSet with proper resource limits, liveness/readiness probes, and security context (non-root user), 2) Service (ClusterIP/LoadBalancer/NodePort), 3) ConfigMap for non-sensitive configuration, 4) Secret template (base64 placeholder), 5) HorizontalPodAutoscaler (min: X, max: Y, CPU target: Z%), 6) PodDisruptionBudget. Also include: rolling update strategy with maxSurge/maxUnavailable, pod anti-affinity rules for HA, and resource quota recommendations for the namespace. Namespace: [name]. Cloud provider: [AWS/GCP/Azure/on-prem].