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Devops Engineer

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Jeffallan
devops-engineer

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.

Overview

PublisherJeffallan
Repositoryclaude-skills
Skill namedevops-engineer
Stars
11.5K
Forks
1.1K
Bundled files
9
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Jeffallan on GitHub. Read the source before you install it.

Installation

Install the Devops Engineer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Jeffallan/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/skills/devops-engineer .claude/skills/devops-engineer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Devops Engineer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Devops Engineer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Devops Engineer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

  1. Assess - Understand application, environments, requirements
  2. Design - Pipeline structure, deployment strategy
  3. Implement - IaC, Dockerfiles, CI/CD configs
  4. Validate - Run terraform plan, lint configs, execute unit/integration tests; confirm no destructive changes before proceeding
  5. Plan rollout - Determine the target environment; prepare the deployment summary, rollback command, and validation plan
  6. Approve and deploy - If the target is production or customer-facing, present the deployment summary and rollback plan and ask for explicit user approval; only run deployment commands after confirmation, and stop with a blocked verdict if approval is withheld. Roll out with verification; run smoke tests post-deployment
  7. Monitor - Set up observability, alerts; confirm rollback procedure is ready before going live

Reference Guide

Load detailed guidance based on context:

TopicReferenceLoad When
GitHub Actionsreferences/github-actions.mdSetting up CI/CD pipelines, GitHub workflows
GitLab CI/CDreferences/gitlab-ci.mdSetting up GitLab pipelines, .gitlab-ci.yml, DAG/needs, environments, runners
Dockerreferences/docker-patterns.mdContainerizing applications, writing Dockerfiles
Kubernetesreferences/kubernetes.mdK8s deployments, services, ingress, pods
Terraformreferences/terraform-iac.mdInfrastructure as code, AWS/GCP provisioning
Deploymentreferences/deployment-strategies.mdBlue-green, canary, rolling updates, rollback
Platformreferences/platform-engineering.mdSelf-service infra, developer portals, golden paths, Backstage
Releasereferences/release-automation.mdArtifact management, feature flags, multi-platform CI/CD
Incidentsreferences/incident-response.mdProduction 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

yaml
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

dockerfile
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

bash
# 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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Devops Engineer AI skill do?

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.

Why use Devops Engineer on TypingMind?

Because you install it once and use it with any model. Devops Engineer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Devops Engineer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Jeffallan/claude-skills/tree/main/skills/devops-engineer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Devops Engineer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Devops Engineer?

As many as you like. As long as a model supports skills, you can use Devops Engineer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Devops Engineer AI skill free?

Yes. It is published on GitHub by Jeffallan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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