Full-stack-skills kubernetes

Provides comprehensive guidance for Kubernetes including pods, services, deployments, ingress, ConfigMaps, and cluster management. Use when the user asks about Kubernetes, needs to deploy applications, configure resources, or troubleshoot cluster issues.

install

source · Clone the upstream repo

git clone https://github.com/partme-ai/full-stack-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/partme-ai/full-stack-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/devops-skills/kubernetes" ~/.claude/skills/partme-ai-full-stack-skills-kubernetes && rm -rf "$T"

manifest: skills/devops-skills/kubernetes/SKILL.md

When to use this skill

Use this skill whenever the user wants to:

Write Deployment, Service, ConfigMap, Secret, or Ingress manifests
Deploy, scale, or troubleshoot pods and clusters with kubectl
Design resource limits, health probes, rolling updates, and operational workflows
Set up local development clusters with minikube, kind, or k3d

How to use this skill

Workflow

Write manifests — define workloads and services in YAML
Apply to cluster — use
```
kubectl apply -f
```
to deploy
Verify status — check rollout, pod health, and service endpoints
Debug issues — inspect logs, describe resources, exec into pods

Quick Start Example

# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
spec:
  replicas: 3
  selector:
    matchLabels:
      app: myapp
  template:
    metadata:
      labels:
        app: myapp
    spec:
      containers:
        - name: myapp
          image: myapp:1.0.0
          ports:
            - containerPort: 8080
          resources:
            requests:
              cpu: 100m
              memory: 128Mi
            limits:
              cpu: 500m
              memory: 256Mi
          livenessProbe:
            httpGet:
              path: /healthz
              port: 8080
            initialDelaySeconds: 10
          readinessProbe:
            httpGet:
              path: /ready
              port: 8080
---
apiVersion: v1
kind: Service
metadata:
  name: myapp
spec:
  selector:
    app: myapp
  ports:
    - port: 80
      targetPort: 8080
  type: ClusterIP

# Apply manifests
kubectl apply -f deployment.yaml

# Check rollout status
kubectl rollout status deployment/myapp

# View pod logs
kubectl logs -l app=myapp --tail=50

# Exec into a pod for debugging
kubectl exec -it deployment/myapp -- /bin/sh

Essential kubectl Commands

Command	Purpose
`kubectl apply -f <file>`	Create or update resources
`kubectl get pods -w`	Watch pod status
`kubectl describe pod <name>`	Inspect pod details and events
`kubectl logs <pod> -f`	Stream container logs
`kubectl rollout undo deployment/<name>`	Roll back a deployment
`kubectl scale deployment/<name> --replicas=5`	Scale replicas

Best Practices

Always set
```
requests
```
and
```
limits
```
for CPU and memory
Configure
```
livenessProbe
```
and
```
readinessProbe
```
for every container
Use Secrets for sensitive data and ConfigMaps for configuration
Define rolling update strategy with
```
maxSurge
```
and
```
maxUnavailable
```
Collect logs and metrics centrally; use RBAC and NetworkPolicies in production

Troubleshooting

CrashLoopBackOff: Run
```
kubectl logs <pod> --previous
```
to see crash output; check resource limits and probe configuration
ImagePullBackOff: Verify image name/tag exists and imagePullSecrets are configured
Pending pods: Run
```
kubectl describe pod <name>
```
— look for insufficient resources or unschedulable nodes
Service not reachable: Verify selector labels match pod labels; check endpoints with
```
kubectl get endpoints <svc>
```

Keywords

kubernetes, k8s, kubectl, deployment, pod, service, ingress, configmap, secret, container orchestration