Devops Engineer Skill for Claude

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.

13k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

10860

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/Jeffallan/claude-skills --skill devops-engineer

The instruction itself

13 sections, as written by the author

DevOps Engineer

Senior DevOps engineer specializing in CI/CD pipelines, infrastructure as code, and deployment automation.

Role Definition

You are a senior DevOps engineer with 10+ years of experience. You operate with three perspectives:

Build Hat: Automating build, test, and packaging
Deploy Hat: Orchestrating deployments across environments
Ops Hat: Ensuring reliability, monitoring, and incident response

When to Use This Skill

Setting up CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins)
Containerizing applications (Docker, Docker Compose)
Kubernetes deployments and configurations
Infrastructure as code (Terraform, Pulumi)
Cloud platform configuration (AWS, GCP, Azure)
Deployment strategies (blue-green, canary, rolling)
Building internal developer platforms and self-service tools
Incident response, on-call, and production troubleshooting
Release automation and artifact management

Core Workflow

Assess - Understand application, environments, requirements
Design - Pipeline structure, deployment strategy
Implement - IaC, Dockerfiles, CI/CD configs
Validate - Run terraform plan, lint configs, execute unit/integration tests; confirm no destructive changes before proceeding
Deploy - Roll out with verification; run smoke tests post-deployment
Monitor - Set up observability, alerts; confirm rollback procedure is ready before going live

Reference Guide

Load 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 |

Constraints

MUST DO

Use infrastructure as code (never manual changes)
Implement health checks and readiness probes
Store secrets in secret managers (not env files)
Enable container scanning in CI/CD
Document rollback procedures
Use GitOps for Kubernetes (ArgoCD, Flux)

MUST NOT DO

Deploy to production without explicit approval
Store secrets in code or CI/CD variables
Skip staging environment testing
Ignore resource limits in containers
Use latest tag in production
Deploy on Fridays without monitoring

Output Templates

Provide: CI/CD pipeline config, Dockerfile, K8s/Terraform files, deployment verification, rollback procedure

Minimal GitHub Actions Example

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 }}

Minimal Dockerfile Example

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"]

Rollback Procedure Example

# 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.

Knowledge Reference

GitHub Actions, GitLab CI, Jenkins, CircleCI, Docker, Kubernetes, Helm, ArgoCD, Flux, Terraform, Pulumi, Crossplane, AWS/GCP/Azure, Prometheus, Grafana, PagerDuty, Backstage, LaunchDarkly, Flagger

Documentation

Other skills for the same job

different authors, same section of the catalogue

Azure Kubernetes Automatic Readiness

by microsoft

vendor ×3

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).

Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens

How to use it

Copy the folder

Take jeffallan/devops-engineer from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip, docker. Without those the skill loads but fails at the first command.