Docker & Kubernetes Orchestrator

Expert guidance for Docker containerization and Kubernetes orchestration. Use when containerizing applications, managing multi-container setups with Docker Compose, or deploying to Kubernetes clusters.

16 stars

Best use case

Docker & Kubernetes Orchestrator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Expert guidance for Docker containerization and Kubernetes orchestration. Use when containerizing applications, managing multi-container setups with Docker Compose, or deploying to Kubernetes clusters.

Teams using Docker & Kubernetes Orchestrator should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/docker-kubernetes-orchestrator/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/devops/docker-kubernetes-orchestrator/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/docker-kubernetes-orchestrator/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Docker & Kubernetes Orchestrator Compares

Feature / AgentDocker & Kubernetes OrchestratorStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Expert guidance for Docker containerization and Kubernetes orchestration. Use when containerizing applications, managing multi-container setups with Docker Compose, or deploying to Kubernetes clusters.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Docker & Kubernetes Orchestrator

Production-grade containerization and orchestration patterns.

## Docker Patterns

### Multi-Stage Builds

```dockerfile
# Node.js optimized build
FROM node:18-alpine AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci
COPY . .
RUN npm run build

FROM node:18-alpine AS runner
WORKDIR /app
ENV NODE_ENV=production
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
COPY package*.json ./
USER node
EXPOSE 3000
CMD ["node", "dist/index.js"]
```

```dockerfile
# Python optimized build
FROM python:3.11-slim AS builder
WORKDIR /app
RUN python -m venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

FROM python:3.11-slim AS runner
WORKDIR /app
COPY --from=builder /opt/venv /opt/venv
ENV PATH="/opt/venv/bin:$PATH"
COPY . .
USER nobody
CMD ["uvicorn", "main:app", "--host", "0.0.0.0"]
```

### Docker Compose for Microservices

```yaml
version: '3.8'

services:
  api:
    build: ./api
    ports:
      - "8000:8000"
    environment:
      DATABASE_URL: postgresql://postgres:password@db:5432/app
      REDIS_URL: redis://redis:6379
    depends_on:
      - db
      - redis
    networks:
      - backend
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

  worker:
    build: ./worker
    environment:
      CELERY_BROKER_URL: redis://redis:6379/0
    depends_on:
      - redis
    networks:
      - backend
    deploy:
      replicas: 3

  frontend:
    build: ./frontend
    ports:
      - "3000:3000"
    environment:
      NEXT_PUBLIC_API_URL: http://localhost:8000
    networks:
      - frontend
      - backend

  db:
    image: postgres:15-alpine
    volumes:
      - postgres_data:/var/lib/postgresql/data
    environment:
      POSTGRES_PASSWORD: password
    networks:
      - backend

  redis:
    image: redis:7-alpine
    volumes:
      - redis_data:/data
    networks:
      - backend

  nginx:
    image: nginx:alpine
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./nginx.conf:/etc/nginx/nginx.conf:ro
      - ./ssl:/etc/nginx/ssl:ro
    depends_on:
      - api
      - frontend
    networks:
      - frontend

volumes:
  postgres_data:
  redis_data:

networks:
  frontend:
  backend:
```

## Kubernetes Patterns

### Deployment

```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: api
  labels:
    app: api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: api
  template:
    metadata:
      labels:
        app: api
    spec:
      containers:
      - name: api
        image: myregistry.com/api:latest
        ports:
        - containerPort: 8000
        env:
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: db-secret
              key: url
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 5
          periodSeconds: 5
```

### Service & Ingress

```yaml
apiVersion: v1
kind: Service
metadata:
  name: api
spec:
  selector:
    app: api
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8000
  type: ClusterIP
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: api
  annotations:
    cert-manager.io/cluster-issuer: letsencrypt-prod
    nginx.ingress.kubernetes.io/rate-limit: "100"
spec:
  ingressClassName: nginx
  tls:
  - hosts:
    - api.example.com
    secretName: api-tls
  rules:
  - host: api.example.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: api
            port:
              number: 80
```

### ConfigMap & Secrets

```yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: app-config
data:
  LOG_LEVEL: "info"
  MAX_CONNECTIONS: "100"
---
apiVersion: v1
kind: Secret
metadata:
  name: db-secret
type: Opaque
stringData:
  url: postgresql://user:pass@db:5432/app
  password: secret123
```

### StatefulSet for Databases

```yaml
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: postgres
spec:
  serviceName: postgres
  replicas: 3
  selector:
    matchLabels:
      app: postgres
  template:
    metadata:
      labels:
        app: postgres
    spec:
      containers:
      - name: postgres
        image: postgres:15
        ports:
        - containerPort: 5432
        env:
        - name: POSTGRES_PASSWORD
          valueFrom:
            secretKeyRef:
              name: db-secret
              key: password
        volumeMounts:
        - name: data
          mountPath: /var/lib/postgresql/data
  volumeClaimTemplates:
  - metadata:
      name: data
    spec:
      accessModes: ["ReadWriteOnce"]
      resources:
        requests:
          storage: 10Gi
```

### Horizontal Pod Autoscaler

```yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: api-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
```

## Helm Charts

```yaml
# Chart.yaml
apiVersion: v2
name: myapp
version: 1.0.0
appVersion: "1.0"

# values.yaml
replicaCount: 3
image:
  repository: myregistry.com/api
  tag: latest
  pullPolicy: IfNotPresent

service:
  type: ClusterIP
  port: 80

ingress:
  enabled: true
  className: nginx
  hosts:
    - host: api.example.com
      paths:
        - path: /
          pathType: Prefix

resources:
  limits:
    cpu: 500m
    memory: 512Mi
  requests:
    cpu: 250m
    memory: 256Mi

autoscaling:
  enabled: true
  minReplicas: 2
  maxReplicas: 10
  targetCPUUtilizationPercentage: 70
```

## Quick Commands

```bash
# Docker
docker build -t myapp:latest .
docker run -p 8000:8000 myapp:latest
docker compose up -d
docker compose logs -f api
docker exec -it container_name sh

# Kubernetes
kubectl apply -f deployment.yaml
kubectl get pods -w
kubectl logs -f pod-name
kubectl exec -it pod-name -- sh
kubectl port-forward svc/api 8000:80
kubectl scale deployment/api --replicas=5
kubectl rollout restart deployment/api
kubectl rollout undo deployment/api

# Helm
helm install myapp ./chart
helm upgrade myapp ./chart
helm rollback myapp 1
helm list
```

---

**When to Use:** Containerizing apps, Docker Compose orchestration, Kubernetes deployments, scaling microservices.

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