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Get Started Free →Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup, infrastructure as code, deployment automation, and monitoring. Use when setting up pipelines, deploying applications, managing infrastructure, implementing monitoring, or optimizing deployment processes.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 174% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 72% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 85% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 144% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 170% | 0% |
Complete toolkit for senior devops with modern tools and best practices.
This skill provides three core capabilities through automated scripts:
bash# Script 1: Pipeline Generator — scaffolds CI/CD pipelines for GitHub Actions or CircleCI python scripts/pipeline_generator.py ./app --platform=github --stages=build,test,deploy # Script 2: Terraform Scaffolder — generates and validates IaC modules for AWS/GCP/Azure python scripts/terraform_scaffolder.py ./infra --provider=aws --module=ecs-service --verbose # Script 3: Deployment Manager — generates deployment manifests + runbooks with rollback support python3 scripts/deployment_manager.py deploy --env=staging --image=app:1.2.3 --strategy=blue-green --verbose --json
Scaffolds CI/CD pipeline configurations for GitHub Actions or CircleCI, with stages for build, test, security scan, and deploy.
Example — GitHub Actions workflow:
yaml# .github/workflows/ci.yml name: CI/CD Pipeline on: push: branches: [main, develop] pull_request: branches: [main] jobs: build-and-test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Set up Node.js uses: actions/setup-node@v4 with: node-version: '20' cache: 'npm' - run: npm ci - run: npm run lint - run: npm test -- --coverage - name: Upload coverage uses: codecov/codecov-action@v4 build-docker: needs: build-and-test runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Build and push image uses: docker/build-push-action@v5 with: push: ${{ github.ref == 'refs/heads/main' }} tags: ghcr.io/${{ github.repository }}:${{ github.sha }} deploy: needs: build-docker if: github.ref == 'refs/heads/main' runs-on: ubuntu-latest steps: - name: Deploy to ECS run: | aws ecs update-service \ --cluster production \ --service app-service \ --force-new-deployment
Usage:
bashpython scripts/pipeline_generator.py <project-path> --platform=github|circleci --stages=build,test,deploy
Generates, validates, and plans Terraform modules. Enforces consistent module structure and runs terraform validate + terraform plan before any apply.
Example — AWS ECS service module:
hcl# modules/ecs-service/main.tf resource "aws_ecs_task_definition" "app" { family = var.service_name requires_compatibilities = ["FARGATE"] network_mode = "awsvpc" cpu = var.cpu memory = var.memory container_definitions = jsonencode([{ name = var.service_name image = var.container_image essential = true portMappings = [{ containerPort = var.container_port protocol = "tcp" }] environment = [for k, v in var.env_vars : { name = k, value = v }] logConfiguration = { logDriver = "awslogs" options = { awslogs-group = "/ecs/${var.service_name}" awslogs-region = var.aws_region awslogs-stream-prefix = "ecs" } } }]) } resource "aws_ecs_service" "app" { name = var.service_name cluster = var.cluster_id task_definition = aws_ecs_task_definition.app.arn desired_count = var.desired_count launch_type = "FARGATE" network_configuration { subnets = var.private_subnet_ids security_groups = [aws_security_group.app.id] assign_public_ip = false } load_balancer { target_group_arn = aws_lb_target_group.app.arn container_name = var.service_name container_port = var.container_port } }
Usage:
bashpython scripts/terraform_scaffolder.py <target-path> --provider=aws|gcp|azure --module=ecs-service|gke-deployment|aks-service [--verbose]
Generates Kubernetes deployment manifests and ordered kubectl runbooks for blue/green or rolling strategies, with health-check gates before traffic switches and rollback runbooks. The tool writes manifests and prints the commands — it never applies them to a cluster itself, so every change gets a human review.
Example — Kubernetes blue/green deployment (blue-slot specific elements):
yaml# k8s/deployment-blue.yaml apiVersion: apps/v1 kind: Deployment metadata: name: app-blue labels: app: myapp slot: blue # slot label distinguishes blue from green spec: replicas: 3 selector: matchLabels: app: myapp slot: blue template: metadata: labels: app: myapp slot: blue spec: containers: - name: app image: ghcr.io/org/app:1.2.3 readinessProbe: # gate: pod must pass before traffic switches httpGet: path: /healthz port: 8080 initialDelaySeconds: 10 periodSeconds: 5 resources: requests: cpu: "250m" memory: "256Mi" limits: cpu: "500m" memory: "512Mi"
Usage:
bashpython scripts/deployment_manager.py deploy \ --env=staging|production \ --image=app:1.2.3 \ --strategy=blue-green|rolling \ --health-check-url=https://app.example.com/healthz python scripts/deployment_manager.py rollback --env=production --to-version=1.2.2 python scripts/deployment_manager.py --analyze --env=production # audit current state
references/cicd_pipeline_guide.md — detailed CI/CD patterns, best practices, anti-patternsreferences/infrastructure_as_code.md — IaC step-by-step processes, optimization, troubleshootingreferences/deployment_strategies.md — deployment strategy configs, security considerations, scalabilityscripts/ directorybash# Scaffold or update module python scripts/terraform_scaffolder.py ./infra --provider=aws --module=ecs-service --verbose # Validate and plan — review diff before applying terraform -chdir=infra init terraform -chdir=infra validate terraform -chdir=infra plan -out=tfplan # Apply only after plan review terraform -chdir=infra apply tfplan # Verify resources are healthy aws ecs describe-services --cluster production --services app-service \ --query 'services[0].{Status:status,Running:runningCount,Desired:desiredCount}'
bash# Generate or update pipeline config python scripts/pipeline_generator.py . --platform=github --stages=build,test,security,deploy # Build and tag image docker build -t ghcr.io/org/app:$(git rev-parse --short HEAD) . docker push ghcr.io/org/app:$(git rev-parse --short HEAD) # Deploy with health-check gate python scripts/deployment_manager.py deploy \ --env=production \ --image=app:$(git rev-parse --short HEAD) \ --strategy=blue-green \ --health-check-url=https://app.example.com/healthz # Verify pods are running kubectl get pods -n production -l app=myapp kubectl rollout status deployment/app-blue -n production # Switch traffic after verification kubectl patch service app-svc -n production \ -p '{"spec":{"selector":{"slot":"blue"}}}'
bash# Immediate rollback via deployment manager python scripts/deployment_manager.py rollback --env=production --to-version=1.2.2 # Or via kubectl kubectl rollout undo deployment/app -n production kubectl rollout status deployment/app -n production # Verify rollback succeeded kubectl get pods -n production -l app=myapp curl -sf https://app.example.com/healthz || echo "ROLLBACK FAILED — escalate"
Use these companion skills for cloud-specific deep dives:
| Skill | Cloud | Use When | |-------|-------|----------| | aws-solution-architect | AWS | ECS/EKS, Lambda, VPC design, cost optimization | | azure-cloud-architect | Azure | AKS, App Service, Virtual Networks, Azure DevOps | | gcp-cloud-architect | GCP | GKE, Cloud Run, VPC, Cloud Build (coming soon) |
Multi-cloud vs single-cloud decision:
> Start single-cloud. Add a second cloud only when there is a concrete business or compliance driver — not for theoretical redundancy.
Terraform (or its open-source fork OpenTofu) is the recommended IaC tool for most teams:
Choose Pulumi when the team strongly prefers TypeScript, Python, Go, or C# over HCL:
| Tool | Use When | |------|----------| | CloudFormation | AWS-only shop; need native AWS support (StackSets, Service Catalog) | | Bicep | Azure-only shop; simpler syntax than ARM templates | | Cloud Deployment Manager | GCP-only; rare — most GCP teams prefer Terraform |
> Rule of thumb: Use Terraform/OpenTofu unless you are 100% committed to a single cloud AND the cloud-native tool offers a feature Terraform cannot replicate (e.g., AWS Service Catalog integration).
Check the comprehensive troubleshooting section in references/deployment_strategies.md.
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