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/devops-automation

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

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rohitg00-claude-code-toolkit
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Install
$ npx -y skills add rohitg00/awesome-claude-code-toolkit --skill devops-automation --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/devops-automation

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

devops-automation.SKILL.md
name: devops-automation
description: CI/CD pipeline design with GitHub Actions, Docker, Kubernetes, Helm, and GitOps patterns

DevOps Automation

GitHub Actions Workflow Structure

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  • Use `concurrency` to cancel outdated runs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

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

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
# 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

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

# 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

Read more
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