Kubernetes Patterns

SkillCloud & infra

kubernetes-patterns is a skill that guides an AI agent in writing and debugging Kubernetes configurations. It covers workload patterns, resource management, RBAC, probes, autoscaling, ConfigMap and Secret handling, and kubectl debugging for production-grade deployments. Use it when writing or reviewing Kubernetes manifests, or when debugging probes, RBAC, autoscaling, or resource limits.

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Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Have an AI agent that can load skills and access to a Kubernetes cluster or manifest files.

Kubernetes PatternsStart free

What your AI can do with it

  • Write and review Kubernetes manifests for production workloads
  • Debug probe failures, RBAC issues, autoscaling, and resource limits
  • Handle ConfigMap and Secret configuration correctly
  • Apply workload patterns and resource management best practices
  • Use kubectl debugging commands to diagnose cluster problems

Getting started

  1. Have an AI agent that can load skills and access to a Kubernetes cluster or manifest files.
  2. Add the kubernetes-patterns skill to the agent's available skills.
  3. Ask the agent to write or review a Kubernetes manifest, or to debug a specific issue such as a failing probe or RBAC error.
  4. Provide the relevant manifest, error message, or kubectl output so the agent can apply the skill.

What this skill tells your AI

The instructions your AI receives, as published by affaan-m/ecc in skills/kubernetes-patterns/SKILL.md and read by ahel’s review.

Production-grade Kubernetes patterns for deploying, managing, and debugging workloads reliably.

When to Activate

  • Writing Kubernetes manifests (Deployments, Services, Ingress, Jobs)
  • Configuring resource requests/limits, liveness/readiness probes
  • Setting up RBAC, namespaces, or ServiceAccounts
  • Managing configuration and secrets in K8s
  • Debugging CrashLoopBackOff, OOMKilled, pending pods, or image pull errors
  • Configuring HPA (Horizontal Pod Autoscaler) or PodDisruptionBudgets
  • Reviewing K8s YAML for security or correctness

When to Use

Same as When to Activate above. This alias satisfies repo skill-format conventions. Use this skill any time you are writing, reviewing, or debugging Kubernetes YAML and workloads.

How It Works

This skill provides copy-pasteable, production-grade YAML patterns and kubectl debugging commands organized by task:

  1. Deployment template — A fully configured production Deployment with security context, rolling update strategy, all three probe types, resource limits, and environment injection from ConfigMap/Secret.
  2. Probes — Decision table for startup vs liveness vs readiness, with correct failureThreshold × periodSeconds math.
  3. Services & Ingress — ClusterIP, LoadBalancer, and TLS Ingress patterns with cert-manager annotations.
  4. ConfigMaps & Secrets — envFrom, file-mount, and external secrets guidance.
  5. Resource management — Requests vs limits rules of thumb by workload type (web API, JVM, worker, sidecar).
  6. RBAC — Least-privilege ServiceAccount → Role → RoleBinding chain.
  7. HPA & PDB — Autoscaling and node-drain safety configurations.
  8. Jobs & CronJobs — One-off and scheduled workload patterns with correct restartPolicy.
  9. kubectl cheatsheet — Logs, exec, rollback, port-forward, dry-run, and common error diagnosis commands.
  10. Anti-patterns & checklist — What NOT to do, and a security/reliability/observability checklist.

Examples

See the sections below for complete, runnable examples. Quick references:

TaskJump to
Full production Deployment YAMLCore Workload Patterns
Probe configurationProbes
RBAC least-privilege setupRBAC
Debug a CrashLoopBackOffkubectl Debugging Cheatsheet
AutoscalingHPA

Core Workload Patterns

Deployment — Production Template

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
  namespace: my-namespace
  labels:
    app: my-app
    version: "1.0.0"
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1          # Allow 1 extra pod during update
      maxUnavailable: 0    # Never reduce below desired count
  template:
    metadata:
      labels:
        app: my-app
        version: "1.0.0"
    spec:
      # Security context at pod level
      securityContext:
        runAsNonRoot: true
        runAsUser: 1001
        fsGroup: 1001

      # Graceful shutdown
      terminationGracePeriodSeconds: 30

      containers:
        - name: my-app
          image: ghcr.io/org/my-app:1.0.0   # Never use :latest
          imagePullPolicy: IfNotPresent

          ports:
            - containerPort: 8080
              protocol: TCP

          # Resource requests AND limits are both required
          resources:
            requests:
              cpu: "100m"
              memory: "128Mi"
            limits:
              cpu: "500m"
              memory: "256Mi"

          # Container security context
          securityContext:
            allowPrivilegeEscalation: false
            readOnlyRootFilesystem: true
            capabilities:
              drop:
                - ALL

          # Probes (see Probes section below)
          startupProbe:
            httpGet:
              path: /health
              port: 8080
            failureThreshold: 30
            periodSeconds: 5
          livenessProbe:
            httpGet:
              path: /health
              port: 8080
            initialDelaySeconds: 0
            periodSeconds: 30
            failureThreshold: 3
          readinessProbe:
            httpGet:
              path: /ready
              port: 8080
            initialDelaySeconds: 5
            periodSeconds: 10
            failureThreshold: 2

          # Environment from ConfigMap and Secret
          envFrom:
            - configMapRef:
                name: my-app-config
          env:
            - name: DB_PASSWORD
              valueFrom:
                secretKeyRef:
                  name: my-app-secrets
                  key: db-password

          # Writable tmp directory when readOnlyRootFilesystem: true
          volumeMounts:
            - name: tmp
              mountPath: /tmp

      volumes:
        - name: tmp
          emptyDir: {}

Probes — Liveness, Readiness, Startup

Understanding when to use each probe is critical:

ProbeFailure ActionUse For
startupProbeKills container if slow to startSlow-starting apps (JVM, Python)
livenessProbeRestarts containerDeadlock / hung process detection
readinessProbeRemoves from Service endpointsTemporary unavailability (DB reconnect)
# Correct pattern: startupProbe covers slow startup,
# then liveness/readiness take over
startupProbe:
  httpGet:
    path: /health
    port: 8080
  failureThreshold: 30  # 30 * 5s = 150s max startup time
  periodSeconds: 5

livenessProbe:
  httpGet:
    path: /health
    port: 8080
  periodSeconds: 30
  failureThreshold: 3   # 3 * 30s = 90s before restart

readinessProbe:
  httpGet:
    path: /ready         # Separate endpoint: checks DB, cache, etc.
    port: 8080
  periodSeconds: 10
  failureThreshold: 2
# WRONG: initialDelaySeconds without startupProbe
# If the app takes 60s to start, set a startupProbe instead
livenessProbe:
  httpGet:
    path: /health
    port: 8080
  initialDelaySeconds: 60   # BAD: Arbitrary wait, race condition

Services and Ingress

Service Types

# ClusterIP (default) — internal-only
apiVersion: v1
kind: Service
metadata:
  name: my-app
  namespace: my-namespace
spec:
  selector:
    app: my-app
  ports:
    - port: 80
      targetPort: 8080
      protocol: TCP
  type: ClusterIP
# LoadBalancer — external traffic (cloud providers)
spec:
  type: LoadBalancer
  ports:
    - port: 443
      targetPort: 8080

Ingress with TLS

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

ConfigMaps and Secrets

ConfigMap — Non-sensitive configuration

apiVersion: v1
kind: ConfigMap
metadata:
  name: my-app-config
  namespace: my-namespace
data:
  LOG_LEVEL: "info"
  APP_ENV: "production"
  MAX_CONNECTIONS: "100"
  # Mount as a file for complex config
  app.yaml: |
    server:
      port: 8080
      timeout: 30s
# Mount ConfigMap as a file
volumes:
  - name: config
    configMap:
      name: my-app-config
      items:
        - key: app.yaml
          path: app.yaml
volumeMounts:
  - name: config
    mountPath: /etc/app
    readOnly: true

Secrets — Sensitive data

# Create secret from literal (CLI, then store in Vault/SOPS)
kubectl create secret generic my-app-secrets \
  --from-literal=db-password='s3cr3t' \
  --namespace=my-namespace \
  --dry-run=client -o yaml | kubectl apply -f -
apiVersion: v1
kind: Secret
metadata:
  name: my-app-secrets
  namespace: my-namespace
type: Opaque
# Values are base64-encoded (NOT encrypted — use Sealed Secrets or ESO for real encryption)
data:
  db-password: czNjcjN0  # base64 of 's3cr3t'

Important: Raw Kubernetes Secrets are only base64-encoded, not encrypted at rest unless your cluster has encryption configured. Use Sealed Secrets or External Secrets Operator for production.


Resource Requests and Limits

resources:
  requests:       # Scheduler uses this to place the pod
    cpu: "100m"   # 100 millicores = 0.1 CPU
    memory: "128Mi"
  limits:         # Container is killed/throttled above this
    cpu: "500m"
    memory: "256Mi"

Rules of thumb:

Workload TypeCPU RequestMemory RequestNotes
Web API100–250m128–256MiSet limits 2-4x requests
Worker/consumer250–500m256–512MiMemory limit = request for predictability
JVM app500m–1512Mi–2GiAllow headroom above -Xmx for JVM overhead
Sidecar10–50m32–64MiKeep minimal
# WRONG: No requests or limits — unpredictable scheduling, OOM evictions
containers:
  - name: app
    image: myapp:latest
    # Missing resources: {} — this is dangerous in production

# WRONG: Limits without requests — requests default to limits, over-reserves capacity
resources:
  limits:
    cpu: "2"
    memory: "1Gi"
  # requests missing — will default to limits values

RBAC — Roles and ServiceAccounts

Principle of Least Privilege

Two patterns depending on whether the app calls the Kubernetes API:

Pattern A — App does NOT need the Kubernetes API (most apps)

Disable token automounting on the ServiceAccount. The Role/RoleBinding are not needed.

# ServiceAccount with token disabled — safest default
apiVersion: v1
kind: ServiceAccount
metadata:
  name: my-app-sa
  namespace: my-namespace
automountServiceAccountToken: false   # No K8s API token injected into pods
# Reference in Deployment — no token, no API access
spec:
  template:
    spec:
      serviceAccountName: my-app-sa
      automountServiceAccountToken: false   # Belt-and-suspenders: also set at pod level
Pattern B — App DOES need the Kubernetes API (operators, controllers, config watchers)

Enable the token and grant only the permissions actually required.

# 1. ServiceAccount — enable token for this SA
apiVersion: v1
kind: ServiceAccount
metadata:
  name: my-app-sa
  namespace: my-namespace
automountServiceAccountToken: true    # Token required: app calls K8s API
# 2. Role — grant only what the app needs (namespace-scoped)
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: my-app-role
  namespace: my-namespace
rules:
  - apiGroups: [""]
    resources: ["configmaps"]
    verbs: ["get", "list", "watch"]    # Read-only, specific resource
  - apiGroups: [""]
    resources: ["secrets"]
    resourceNames: ["my-app-secrets"]  # Restrict to specific secret by name
    verbs: ["get"]
# 3. Bind Role to ServiceAccount
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: my-app-rolebinding
  namespace: my-namespace
subjects:
  - kind: ServiceAccount
    name: my-app-sa
    namespace: my-namespace
roleRef:
  kind: Role
  apiGroup: rbac.authorization.k8s.io
  name: my-app-role
# 4. Reference SA in Deployment
spec:
  template:
    spec:
      serviceAccountName: my-app-sa
      # automountServiceAccountToken defaults to true from SA — token is injected

Horizontal Pod Autoscaler (HPA)

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: my-app-hpa
  namespace: my-namespace
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  minReplicas: 2      # Always at least 2 for HA
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70    # Scale up when avg CPU > 70%
    - type: Resource
      resource:
        name: memory
        target:
          type: Utilization
          averageUtilization: 80

HPA requires resources.requests to be set on all containers — it calculates utilization as current / request.


PodDisruptionBudget (PDB)

Prevent too many pods going down during node drains or rolling updates:

apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: my-app-pdb
  namespace: my-namespace
spec:
  minAvailable: 2           # OR use maxUnavailable: 1
  selector:
    matchLabels:
      app: my-app

Namespaces and Multi-Tenancy

# Create namespace with resource quotas
kubectl create namespace my-namespace

# Apply ResourceQuota to limit namespace consumption
kubectl apply -f - <<EOF
apiVersion: v1
kind: ResourceQuota
metadata:
  name: my-namespace-quota
  namespace: my-namespace
spec:
  hard:
    requests.cpu: "4"
    requests.memory: 4Gi
    limits.cpu: "8"
    limits.memory: 8Gi
    pods: "20"
EOF

Jobs and CronJobs

# One-off Job (DB migration, data processing)
apiVersion: batch/v1
kind: Job
metadata:
  name: db-migrate
  namespace: my-namespace
spec:
  backoffLimit: 3          # Retry up to 3 times on failure
  ttlSecondsAfterFinished: 3600   # Auto-delete after 1h
  template:
    spec:
      restartPolicy: OnFailure    # Never for Jobs (not Always)
      containers:
        - name: migrate
          image: ghcr.io/org/my-app:1.0.0
          command: ["python", "manage.py", "migrate"]
          resources:
            requests:
              cpu: "100m"
              memory: "256Mi"
# CronJob
apiVersion: batch/v1
kind: CronJob
metadata:
  name: cleanup-job
  namespace: my-namespace
spec:
  schedule: "0 2 * * *"         # 2am daily
  concurrencyPolicy: Forbid      # Don't run if previous still running
  successfulJobsHistoryLimit: 3
  failedJobsHistoryLimit: 1
  jobTemplate:
    spec:
      template:
        spec:
          restartPolicy: OnFailure
          containers:
            - name: cleanup
              image: ghcr.io/org/cleanup:1.0.0
              resources:
                requests:
                  cpu: "50m"
                  memory: "64Mi"

kubectl Debugging Cheatsheet

# --- Pod status and logs ---
kubectl get pods -n my-namespace
kubectl get pods -n my-namespace -o wide          # Show node assignment
kubectl describe pod <pod-name> -n my-namespace   # Events and state details
kubectl logs <pod-name> -n my-namespace           # Current logs
kubectl logs <pod-name> -n my-namespace --previous  # Logs from crashed container
kubectl logs <pod-name> -n my-namespace -c <container>  # Multi-container pod

# --- Execute into a running container ---
kubectl exec -it <pod-name> -n my-namespace -- sh
kubectl exec -it <pod-name> -n my-namespace -- bash

# --- Check resource usage ---
kubectl top pods -n my-namespace
kubectl top nodes

# --- Deployment operations ---
kubectl rollout status deployment/my-app -n my-namespace
kubectl rollout history deployment/my-app -n my-namespace
kubectl rollout undo deployment/my-app -n my-namespace      # Rollback
kubectl rollout undo deployment/my-app --to-revision=2 -n my-namespace

# --- Scale manually ---
kubectl scale deployment my-app --replicas=5 -n my-namespace

# --- Inspect events (cluster-wide issues) ---
kubectl get events -n my-namespace --sort-by='.lastTimestamp'

# --- Port-forward for local debugging ---
kubectl port-forward pod/<pod-name> 8080:8080 -n my-namespace
kubectl port-forward svc/my-app 8080:80 -n my-namespace

# --- Dry-run to validate YAML ---
kubectl apply -f deployment.yaml --dry-run=client
kubectl apply -f deployment.yaml --dry-run=server   # Validates against live cluster

Diagnosing Common Errors

# CrashLoopBackOff: container keeps crashing
kubectl logs <pod-name> --previous -n my-namespace  # Check crash logs
kubectl describe pod <pod-name> -n my-namespace     # Check exit code & OOMKilled

# ImagePullBackOff: can't pull image
kubectl describe pod <pod-name> -n my-namespace     # Check Events section
# Causes: wrong image tag, missing imagePullSecret, private registry

# Pending pod: not scheduled
kubectl describe pod <pod-name> -n my-namespace
# Causes: insufficient resources, no matching node selector, taint/toleration mismatch

# OOMKilled: out of memory
# Increase memory limits, check for memory leaks
kubectl describe pod <pod-name> -n my-namespace | grep -A5 "Last State"

Anti-Patterns

# BAD: Using :latest tag — non-deterministic deployments
image: myapp:latest

# GOOD: Pin to a specific immutable tag (SHA or semver)
image: ghcr.io/org/myapp:1.4.2
# or
image: ghcr.io/org/myapp@sha256:abc123...

# ---

# BAD: Running as root
securityContext: {}    # Defaults to root

# GOOD: Non-root with explicit UID
securityContext:
  runAsNonRoot: true
  runAsUser: 1001

# ---

# BAD: No resource limits — one pod can starve the entire node
containers:
  - name: app
    image: myapp:1.0.0
    # No resources defined

# GOOD: Always set requests and limits
resources:
  requests:
    cpu: "100m"
    memory: "128Mi"
  limits:
    cpu: "500m"
    memory: "256Mi"

# ---

# BAD: Storing plaintext secrets in ConfigMaps
apiVersion: v1
kind: ConfigMap
data:
  DB_PASSWORD: "mysecretpassword"   # NEVER — use Secret or external secrets manager

# ---

# BAD: ClusterAdmin for application service accounts
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
roleRef:
  kind: ClusterRole
  name: cluster-admin    # Grants god-mode to your app

# ---

# BAD: minAvailable: 0 in PDB — defeats the purpose
spec:
  minAvailable: 0

# ---

# BAD: restartPolicy: Always in a Job (causes infinite restart loop)
spec:
  restartPolicy: Always   # Use OnFailure or Never for Jobs

Best Practices Checklist

Security

  • Container runs as non-root (runAsNonRoot: true, runAsUser set)
  • readOnlyRootFilesystem: true with emptyDir for writable paths
  • allowPrivilegeEscalation: false
  • All capabilities dropped (capabilities.drop: [ALL])
  • Dedicated ServiceAccount per app, not default
  • automountServiceAccountToken: false unless needed
  • RBAC follows least privilege (use Role, not ClusterRole unless needed)
  • Secrets managed via Sealed Secrets or External Secrets Operator

Reliability

  • All 3 probe types configured (startup + liveness + readiness)
  • Resource requests AND limits set on every container
  • minReplicas: 2+ for any production workload
  • PodDisruptionBudget defined for stateful or critical services
  • RollingUpdate strategy with maxUnavailable: 0
  • HPA configured for variable-load services

Observability

  • App exposes /health (liveness) and /ready (readiness) endpoints
  • Structured JSON logging (no PII in logs)
  • Resource labels: app, version, environment

Related Skills

  • docker-patterns — Multi-stage Dockerfiles and image security
  • deployment-patterns — CI/CD pipelines, rollback strategy, health check endpoints
  • security-review — Broader security hardening context
  • git-workflow — GitOps integration with K8s (ArgoCD / Flux patterns)

Signals

GitHub stars
270k
Forks
40k
Last commit
Sep 2026

ahel review

  • S4info
    community integration, published by affaan-m, not kubernetes

Automated review, not a security audit. Ruleset v1+k2.

Others that do the same job

Questions

What does kubernetes-patterns cover?
It covers workload patterns, resource management, RBAC, probes, autoscaling, ConfigMap and Secret handling, and kubectl debugging for production-grade deployments.
When should I use this skill?
Use it when writing or reviewing Kubernetes manifests, or when debugging probes, RBAC, autoscaling, or resource limits.
Does it support Helm charts?
The skill description does not mention Helm, so it focuses on Kubernetes manifests and kubectl debugging.
Can it help with cluster provisioning?
No, it is for writing and debugging Kubernetes configurations, not for provisioning clusters.
Advanced
Item type
skill
Key
kubernetes-patterns
Source
github.com/affaan-m/ecc