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Devops Automation

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rohitg00
devops-automation

CI/CD pipeline design with GitHub Actions, Docker, Kubernetes, Helm, and GitOps patterns

Overview

Publisherrohitg00
Repositoryawesome-claude-code-toolkit
Skill namedevops-automation
Stars
2.6K
Forks
963
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Devops Automation 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/rohitg00/awesome-claude-code-toolkit.git /tmp/awesome-claude-code-toolkit
mkdir -p .claude/skills
cp -r /tmp/awesome-claude-code-toolkit/skills/devops-automation .claude/skills/devops-automation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Devops Automation 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 Automation 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 Automation 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 Automation

GitHub Actions Workflow Structure

yaml
name: CI/CD
on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true

jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 22
          cache: 'npm'
      - run: npm ci
      - run: npm run lint

  test:
    runs-on: ubuntu-latest
    needs: lint
    strategy:
      matrix:
        node-version: [20, 22]
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: ${{ matrix.node-version }}
          cache: 'npm'
      - run: npm ci
      - run: npm test -- --coverage
      - uses: actions/upload-artifact@v4
        with:
          name: coverage-${{ matrix.node-version }}
          path: coverage/

  deploy:
    runs-on: ubuntu-latest
    needs: test
    if: github.ref == 'refs/heads/main'
    environment: production
    steps:
      - uses: actions/checkout@v4
      - run: ./deploy.sh

Key patterns:

  • Use concurrency to cancel outdated runs
  • Cache dependencies with setup action's cache option
  • Use needs for job dependencies
  • Gate deploys with environment protection rules
  • Use matrix for cross-version testing

Docker Multi-Stage Builds

dockerfile
FROM node:22-alpine AS deps
WORKDIR /app
COPY package.json package-lock.json ./
RUN npm ci --production

FROM node:22-alpine AS builder
WORKDIR /app
COPY package.json package-lock.json ./
RUN npm ci
COPY . .
RUN npm run build

FROM node:22-alpine AS runner
WORKDIR /app
RUN addgroup -g 1001 appgroup && adduser -u 1001 -G appgroup -S appuser
COPY --from=deps /app/node_modules ./node_modules
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/package.json ./
USER appuser
EXPOSE 3000
HEALTHCHECK --interval=30s --timeout=3s CMD wget -qO- http://localhost:3000/health || exit 1
CMD ["node", "dist/server.js"]

Rules:

  • Use specific image tags, never latest
  • Run as non-root user
  • Copy only necessary files into final stage
  • Add HEALTHCHECK for orchestrator integration
  • Use .dockerignore to exclude node_modules, .git, tests

Kubernetes Deployment Manifest

yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: api-server
  labels:
    app: api-server
spec:
  replicas: 3
  selector:
    matchLabels:
      app: api-server
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  template:
    metadata:
      labels:
        app: api-server
    spec:
      containers:
        - name: api
          image: registry.example.com/api:v1.2.3
          ports:
            - containerPort: 3000
          resources:
            requests:
              cpu: 100m
              memory: 128Mi
            limits:
              cpu: 500m
              memory: 512Mi
          readinessProbe:
            httpGet:
              path: /health
              port: 3000
            initialDelaySeconds: 5
            periodSeconds: 10
          livenessProbe:
            httpGet:
              path: /health
              port: 3000
            initialDelaySeconds: 15
            periodSeconds: 20
          env:
            - name: DATABASE_URL
              valueFrom:
                secretKeyRef:
                  name: api-secrets
                  key: database-url

Always set resource requests and limits. Always define readiness and liveness probes. Use maxUnavailable: 0 for zero-downtime deploys.

Helm Chart Structure

chart/
  Chart.yaml
  values.yaml
  values-staging.yaml
  values-production.yaml
  templates/
    deployment.yaml
    service.yaml
    ingress.yaml
    hpa.yaml
    _helpers.tpl
yaml
# values.yaml
replicaCount: 2
image:
  repository: registry.example.com/api
  tag: latest
  pullPolicy: IfNotPresent
resources:
  requests:
    cpu: 100m
    memory: 128Mi
  limits:
    cpu: 500m
    memory: 512Mi
ingress:
  enabled: true
  host: api.example.com
autoscaling:
  enabled: true
  minReplicas: 2
  maxReplicas: 10
  targetCPUUtilization: 70

Use values-{env}.yaml overrides per environment. Lint charts with helm lint. Test with helm template before deploying.

ArgoCD GitOps Pattern

yaml
apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
  name: api-server
  namespace: argocd
spec:
  project: default
  source:
    repoURL: https://github.com/org/k8s-manifests
    targetRevision: main
    path: apps/api-server
  destination:
    server: https://kubernetes.default.svc
    namespace: production
  syncPolicy:
    automated:
      prune: true
      selfHeal: true
    syncOptions:
      - CreateNamespace=true

GitOps principles:

  • Git is the single source of truth for cluster state
  • All changes go through PRs (no kubectl apply in production)
  • ArgoCD auto-syncs from Git to cluster
  • Enable selfHeal to revert manual cluster changes
  • Separate app code repos from deployment manifest repos

Monitoring Stack

yaml
# Prometheus ServiceMonitor
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: api-server
spec:
  selector:
    matchLabels:
      app: api-server
  endpoints:
    - port: metrics
      interval: 15s
      path: /metrics

Key metrics to expose:

  • http_request_duration_seconds (histogram) - request latency by route and status
  • http_requests_total (counter) - request count by route and status
  • process_resident_memory_bytes (gauge) - memory usage
  • db_query_duration_seconds (histogram) - database query latency

Alert on: error rate >1%, P99 latency >2s, memory >80% of limit, pod restarts >3 in 10 minutes.

Pipeline Best Practices

  1. Keep CI under 10 minutes (parallelize jobs, cache aggressively)
  2. Run linting and type checking before tests
  3. Use ephemeral environments for PR previews
  4. Pin all action versions to SHA, not tags
  5. Store secrets in GitHub Secrets, never in workflow files
  6. Use OIDC for cloud provider authentication (no long-lived keys)
  7. Tag images with git SHA, not latest
  8. Run security scans (Trivy, Snyk) on container images in CI

Frequently asked questions

What does the Devops Automation AI skill do?

CI/CD pipeline design with GitHub Actions, Docker, Kubernetes, Helm, and GitOps patterns

Why use Devops Automation on TypingMind?

Because you install it once and use it with any model. Devops Automation 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 Automation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/awesome-claude-code-toolkit/tree/main/skills/devops-automation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Devops Automation?

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 Automation?

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

Is the Devops Automation AI skill free?

Yes. It is published on GitHub by rohitg00 under the Apache-2.0 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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