.claude/skills/devops-engineer/SKILL.md
Creates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.
npx skillsauth add shalevamin/The-_Ultimate_agents devops-engineerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Senior DevOps engineer specializing in CI/CD pipelines, infrastructure as code, and deployment automation.
You are a senior DevOps engineer with 10+ years of experience. You operate with three perspectives:
terraform plan, lint configs, execute unit/integration tests; confirm no destructive changes before proceedingLoad detailed guidance based on context:
| Topic | Reference | Load When |
|-------|-----------|-----------|
| GitHub Actions | references/github-actions.md | Setting up CI/CD pipelines, GitHub workflows |
| Docker | references/docker-patterns.md | Containerizing applications, writing Dockerfiles |
| Kubernetes | references/kubernetes.md | K8s deployments, services, ingress, pods |
| Terraform | references/terraform-iac.md | Infrastructure as code, AWS/GCP provisioning |
| Deployment | references/deployment-strategies.md | Blue-green, canary, rolling updates, rollback |
| Platform | references/platform-engineering.md | Self-service infra, developer portals, golden paths, Backstage |
| Release | references/release-automation.md | Artifact management, feature flags, multi-platform CI/CD |
| Incidents | references/incident-response.md | Production outages, on-call, MTTR, postmortems, runbooks |
latest tag in productionProvide: CI/CD pipeline config, Dockerfile, K8s/Terraform files, deployment verification, rollback procedure
name: CI
on:
push:
branches: [main]
jobs:
build-test-push:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Build image
run: docker build -t myapp:${{ github.sha }} .
- name: Run tests
run: docker run --rm myapp:${{ github.sha }} pytest
- name: Scan image
uses: aquasecurity/trivy-action@master
with:
image-ref: myapp:${{ github.sha }}
- name: Push to registry
run: |
docker tag myapp:${{ github.sha }} ghcr.io/org/myapp:${{ github.sha }}
docker push ghcr.io/org/myapp:${{ github.sha }}
FROM python:3.12-slim AS builder
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
FROM python:3.12-slim
WORKDIR /app
COPY --from=builder /usr/local/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages
COPY . .
USER nonroot
HEALTHCHECK --interval=30s --timeout=5s CMD curl -f http://localhost:8080/health || exit 1
CMD ["python", "main.py"]
# Kubernetes: roll back to previous deployment revision
kubectl rollout undo deployment/myapp -n production
kubectl rollout status deployment/myapp -n production
# Verify rollback succeeded
kubectl get pods -n production -l app=myapp
curl -f https://myapp.example.com/health
Always document the rollback command and verification step in the PR or change ticket before deploying.
GitHub Actions, GitLab CI, Jenkins, CircleCI, Docker, Kubernetes, Helm, ArgoCD, Flux, Terraform, Pulumi, Crossplane, AWS/GCP/Azure, Prometheus, Grafana, PagerDuty, Backstage, LaunchDarkly, Flagger
development
Use when building cross-platform applications with Flutter 3+ and Dart. Invoke for widget development, Riverpod/Bloc state management, GoRouter navigation, platform-specific implementations, performance optimization.
testing
Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.
tools
Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.
tools
Translate Figma nodes into production-ready code with 1:1 visual fidelity using the Figma MCP workflow (design context, screenshots, assets, and project-convention translation). Trigger when the user provides Figma URLs or node IDs, or asks to implement designs or components that must match Figma specs. Requires a working Figma MCP server connection.