Guide · Agent skill
How to use the DevOps Engineer agent skill (devops-engineer)
Updated · written with AI from the project's own documentation and checked against our catalogue data
The devops-engineer skill gives an AI coding agent the working method of a senior DevOps engineer. Once installed, the agent can help you write Dockerfiles, CI/CD pipelines, Kubernetes manifests and Terraform or Pulumi templates, and draft rollback and incident runbooks. This guide is for developers and small teams who may never have used an agent skill before, and it explains how to install the skill, trigger it, review its output and stay in control of production changes.
DevOps EngineerJeffallan/claude-skills · MIT Download the skill Quick start
- 1Download the ZIP. Download the devops-engineer ZIP from the skill's page in this directory.
- 2Install the skill. Unzip it into ~/.claude/skills/ for all projects, or into a project's .claude/skills/ folder for one project. In the Claude apps, you can upload the ZIP under Settings → Capabilities → Skills instead.
- 3Describe your task. Ask in plain language, naming the task, your language or framework, your cloud provider and your environments.
- 4Review the draft files. Read the pipeline, Dockerfile or infrastructure files and check image names, secrets handling and resource limits against your own setup.
- 5Validate in staging. Run the validation steps the skill describes, such as terraform plan and tests, and deploy to staging before production.
- 6Approve production changes. When the agent presents the deployment summary and rollback plan, review it and give explicit approval before any production command runs.
What does the DevOps Engineer skill make the agent do?
A skill is a folder of instructions that an AI agent loads when a task matches its description. This one covers building, testing, packaging and deploying software. When you ask for help in those areas, the agent works through a fixed sequence: assess the application and environments, design the pipeline and deployment strategy, implement the configuration, validate it, plan the rollout, get approval before deploying to production, and then set up monitoring.
The skill also loads reference notes on GitHub Actions, GitLab CI, Docker, Kubernetes, Terraform, deployment strategies (rolling, blue-green, canary, recreate), platform engineering, release automation and incident response. The outputs are pipeline files, Dockerfiles, infrastructure templates and rollback procedures.
- Pipeline files for GitHub Actions or GitLab CI
- Dockerfiles, including multi-stage builds with health checks
- Kubernetes manifests and GitOps configuration (ArgoCD or Flux)
- Terraform or Pulumi infrastructure templates
- Rollback procedures and incident response runbooks
How do you install the DevOps Engineer skill?
Download the ZIP file from the skill's page in this directory. Unzip it into your user skills folder, ~/.claude/skills/, so it is available in every project. To limit it to one project, unzip it into that project's .claude/skills/ folder instead.
If you use the Claude apps rather than a local setup, you can upload the skill instead. Open Settings, go to Capabilities, then Skills, and upload the ZIP there.
- Personal (all projects): ~/.claude/skills/
- Single project: .claude/skills/ inside the project folder
- Claude apps: Settings → Capabilities → Skills
How do you trigger the skill with a natural request?
You do not need a special command. Describe the task in plain language and mention the kind of work involved. The skill's description lists the trigger words it responds to, including pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call and platform engineering.
Give the agent context it can use: the language and framework of your app, where it runs, which cloud provider you use and what environments exist (for example staging and production). The skill's first step is to assess these, so the more you provide, the fewer questions it has to ask.
- Set up a GitHub Actions pipeline that runs tests, builds a Docker image and pushes it to ghcr.io for my Node.js API.
- Containerize this Flask app with a multi-stage Dockerfile and a health check.
- Write Kubernetes manifests for this service with a rolling update strategy and resource limits.
- Draft a Terraform template for a small AWS setup with staging and production environments.
- Write an on-call runbook for when our API returns 500 errors after a deploy.
What does a typical pipeline example include?
The skill's example pipeline builds the image, runs tests inside the container, scans the image with Trivy (or a similar scanner), and pushes the tagged image to a registry. Tagging by commit SHA is used rather than the latest tag, which the skill says not to use in production.
Each generated file is a draft. Read it, check the image names, registry paths and secrets handling against your own setup, and run the validation steps the skill describes before you rely on it.
- The skill asks you to run terraform plan, lint configs and run tests before proceeding.
- It asks you to confirm there are no destructive changes before deploying.
- It recommends storing secrets in a secret manager, not in code or CI/CD variables.
docker build -t myapp:${{ github.sha }} .How does the skill handle production deployments and rollbacks?
For production or customer-facing targets, the skill is instructed to present a deployment summary and rollback plan and ask for your explicit approval. It runs deployment commands only after you confirm. If you withhold approval, it stops with a blocked verdict.
The skill also expects every change to document its rollback command and verification step in the pull request or change ticket. The example below shows a Kubernetes rollback followed by checks.
- Use infrastructure as code rather than manual changes.
- Confirm the rollback procedure is ready before going live.
- Run smoke tests after deployment.
kubectl rollout undo deployment/myapp -n production
kubectl rollout status deployment/myapp -n production
kubectl get pods -n production -l app=myapp
curl -f https://myapp.example.com/healthWhat do you need before using the skill?
The skill works on a project, so you need access to the Git repository being configured. The tools it relies on depend on what you ask for. Docker is needed to build and test images, kubectl for Kubernetes commands if you deploy to a cluster, and Terraform plus cloud provider credentials if you provision infrastructure. Trivy or a similar scanner is referenced in the example pipeline.
The skill does not run scripts and does not use the network by itself. Any commands it suggests are run by you or by your tools, so review them before running. Non-technical users without repository, cloud or terminal access will not be able to run and check the outputs.
- Git repository access for the project
- Docker for building and testing images
- kubectl, if deploying to a Kubernetes cluster
- Terraform and cloud credentials, if provisioning infrastructure
- Trivy or a similar container scanner
What should you check before trusting the output?
Treat generated configs as drafts written by an AI agent, which can make mistakes. Check every image name, registry path, port, resource limit and secret reference. Run the staging environment before production, because the skill's rules say not to skip staging testing.
The skill recommends container scanning but does not perform a security review. If you need a verified security audit, use a separate review process.
- Review all generated pipeline, Docker and infrastructure files line by line.
- Confirm resource limits and health checks are set.
- Test in staging first.
- Do not rely on the skill as a security audit.
What people use it for
- Setting up a CI/CD pipeline that tests, builds and scans a web or API project on every push to main.
- Writing a multi-stage Dockerfile with a health check and a non-root user for an existing application.
- Drafting Kubernetes manifests with rolling update settings and resource limits for a service.
- Producing a first-draft incident response runbook and rollback checklist for on-call engineers.
Frequently asked questions
How do I install the devops-engineer skill?
Download the ZIP from this skill's page and unzip it into ~/.claude/skills/ for all your projects, or into a project's .claude/skills/ folder for one project. In the Claude apps, upload the ZIP under Settings → Capabilities → Skills.
Is the DevOps Engineer skill free to use?
The skill is released under the MIT licence, as stated in its file. Check the licence file in the repository for the full terms before you reuse or redistribute it.
What do I need installed to use this skill?
You need access to the Git repository you are configuring. Docker is needed to build and test images, kubectl for Kubernetes deployments, and Terraform with cloud credentials for infrastructure. Trivy or a similar scanner is referenced in the example pipeline.
Does the skill deploy to production automatically?
No. For production or customer-facing targets, the skill is instructed to present a deployment summary and rollback plan and ask for explicit approval. It stops if approval is withheld.
Does the skill run scripts or use the network?
The skill's listed requirements say it runs no scripts and uses no network. Any commands it suggests still need to be run and checked by you.
How does it compare with the related skills?
The skill lists related skills for terraform-engineer, kubernetes-specialist, sre-engineer, monitoring-expert and security-reviewer. Use the DevOps Engineer skill for the overall pipeline and deployment workflow, and those for deeper work in a single area. It does not perform a security audit on its own.
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Our verdict
This skill suits developers setting up CI/CD for a web or API project, teams containerizing apps or writing Kubernetes and Terraform files, and on-call engineers who want a structured runbook draft. It is not suited to unattended production deployments, to people without repository, cloud or terminal access, or to teams that need a verified security audit.
Download the skill