Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/creating-kubernetes-deployments/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill creating-kubernetes-deployments. Copyright stays with the author (MIT).

Creating Kubernetes Deployments

Generate production-ready Kubernetes manifests with health checks, resource limits, and security best practices.

Quick Start

Basic Deployment + Service

# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-api
  labels:
    app: my-api
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-api
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 25%
  template:
    metadata:
      labels:
        app: my-api
    spec:
      containers:
      - name: my-api
        image: my-registry/my-api:v1.0.0
        ports:
        - containerPort: 8080  # 8080: HTTP proxy port
        resources:
          requests:
            cpu: 100m
            memory: 256Mi
          limits:
            cpu: 500m
            memory: 512Mi
        livenessProbe:
          httpGet:
            path: /healthz
            port: 8080  # HTTP proxy port
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /readyz
            port: 8080  # HTTP proxy port
          initialDelaySeconds: 5
          periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
  name: my-api
spec:
  type: ClusterIP
  selector:
    app: my-api
  ports:
  - port: 80
    targetPort: 8080  # HTTP proxy port

Deployment Strategies

Blue-Green Deployment

# Blue deployment (current production)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-api-blue
  labels:
    app: my-api
    version: blue
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-api
      version: blue
  template:
    metadata:
      labels:
        app: my-api
        version: blue
    spec:
      containers:
      - name: my-api
        image: my-registry/my-api:v1.0.0
---
# Service points to blue
apiVersion: v1
kind: Service
metadata:
  name: my-api
spec:
  selector:
    app: my-api
    version: blue  # Switch to 'green' for deployment
  ports:
  - port: 80
    targetPort: 8080  # 8080: HTTP proxy port

Service Types

Ingress with TLS

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: my-api-ingress
  annotations:
    cert-manager.io/cluster-issuer: letsencrypt-prod
    nginx.ingress.kubernetes.io/ssl-redirect: "true"
spec:
  ingressClassName: nginx
  tls:
  - hosts:
    - api.example.com
    secretName: api-tls-secret
  rules:
  - host: api.example.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: my-api
            port:
              number: 80

Resource Limits

Always set resource requests and limits:

resources:
  requests:    # Guaranteed resources
    cpu: 100m  # 0.1 CPU core
    memory: 256Mi
  limits:      # Maximum allowed
    cpu: 500m  # 0.5 CPU core
    memory: 512Mi

Health Checks

Liveness Probe (Is container running?)

livenessProbe:
  httpGet:
    path: /healthz
    port: 8080  # 8080: HTTP proxy port
  initialDelaySeconds: 30  # Wait for app startup
  periodSeconds: 10         # Check every 10s
  timeoutSeconds: 5         # Timeout per check
  failureThreshold: 3       # Restart after 3 failures

Readiness Probe (Ready for traffic?)

readinessProbe:
  httpGet:
    path: /readyz
    port: 8080  # 8080: HTTP proxy port
  initialDelaySeconds: 5    # Quick check after start
  periodSeconds: 5          # Check every 5s
  successThreshold: 1       # 1 success = ready
  failureThreshold: 3       # Remove from LB after 3 failures

Startup Probe (Slow-starting apps)

startupProbe:
  httpGet:
    path: /healthz
    port: 8080  # 8080: HTTP proxy port
  initialDelaySeconds: 0
  periodSeconds: 10
  failureThreshold: 30      # Allow 5 minutes to start (30 * 10s)

Horizontal Pod Autoscaler

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: my-api-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-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
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300  # 300: Wait 5min before scale down

ConfigMaps and Secrets

ConfigMap (Non-sensitive config)

apiVersion: v1
kind: ConfigMap
metadata:
  name: my-api-config
data:
  LOG_LEVEL: "info"
  API_ENDPOINT: "https://api.example.com"
  config.yaml: |
    server:
      port: 8080  # 8080: HTTP proxy port
    features:
      enabled: true

Secret (Sensitive data - base64 encoded)

apiVersion: v1
kind: Secret
metadata:
  name: my-api-secrets
type: Opaque
data:
  API_KEY: YXBpLWtleS1oZXJl          # echo -n "api-key-here" | base64
  DATABASE_URL: cG9zdGdyZXM6Ly8uLi4=  # echo -n "postgres://..." | base64

Using in Deployment

spec:
  containers:
  - name: my-api
    envFrom:
    - configMapRef:
        name: my-api-config
    - secretRef:
        name: my-api-secrets
    volumeMounts:
    - name: config-volume
      mountPath: /app/config
  volumes:
  - name: config-volume
    configMap:
      name: my-api-config

Instructions

1. Gather Requirements

   • Application name, container image, port
   • Replica count and resource requirements
   • Health check endpoints
   • External access requirements (Ingress/LoadBalancer)

2. Generate Base Manifests

   • Create Deployment with resource limits and probes
   • Create Service (ClusterIP for internal, LoadBalancer for external)
   • Add ConfigMap for configuration
   • Add Secret for sensitive data

3. Add Production Features

   • Configure Ingress with TLS if external access needed
   • Add HPA for auto-scaling
   • Add NetworkPolicy for security
   • Add PodDisruptionBudget for availability

4. Validate and Apply

   # Validate manifests
   kubectl apply -f manifests/ --dry-run=server
   
   # Apply to cluster
   kubectl apply -f manifests/
   
   # Watch rollout
   kubectl rollout status deployment/my-api

Error Handling

See ${CLAUDE_SKILL_DIR}/references/errors.md for comprehensive troubleshooting.

Examples

See ${CLAUDE_SKILL_DIR}/references/examples.md for detailed walkthroughs.

Resources

• Kubernetes documentation: https://kubernetes.io/docs/
• kubectl reference: https://kubernetes.io/docs/reference/kubectl/
• Templates in ${CLAUDE_SKILL_DIR}/assets/
• Scripts in ${CLAUDE_SKILL_DIR}/scripts/

Overview

Deploy applications to Kubernetes with production-ready manifests.

Prerequisites

• Access to the Kubernetes environment or API
• Required CLI tools installed and authenticated
• Familiarity with Kubernetes concepts and terminology

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale

See Kubernetes implementation details for output format specifications.