# Kubernetes Patterns

> Pods, deployments, services, ingress, RBAC, autoscaling, and production cluster best practices

- Skill: `gabrielmoreira/kubernetes-patterns` (Agent Skill)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/kubernetes-patterns`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/kubernetes-patterns/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/kubernetes-patterns

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# Kubernetes Patterns

Deploy and manage production Kubernetes clusters.

## Core Objects

### Workloads
| Object | Use Case | Scaling |
|--------|----------|---------|
| Deployment | Stateless apps | Replicas, HPA |
| StatefulSet | Stateful apps (DBs) | Stable network IDs |
| DaemonSet | Per-node agents (logging, monitoring) | Node count |
| Job/CronJob | Batch tasks, scheduled jobs | Completion |

### Networking
- **Service**: Stable endpoint for pods (ClusterIP, NodePort, LoadBalancer)
- **Ingress**: HTTP routing, TLS termination, path-based routing
- **Network Policies**: Pod-level firewall rules

## Production Best Practices

### Resource Management
```yaml
resources:
  requests:
    cpu: 100m
    memory: 128Mi
  limits:
    cpu: 500m
    memory: 256Mi
```
- Always set requests AND limits
- Use LimitRange for namespace defaults
- Use ResourceQuota for namespace caps

### Pod Anti-Affinity
Spread pods across nodes for HA.

### Readiness & Liveness Probes
- Readiness: traffic starts flowing
- Liveness: pod gets restarted
- Startup: for slow-starting containers

## RBAC
- Least-privilege service accounts per app
- Namespace-scoped roles (not cluster-wide)
- Regularly audit permissions
- Use groups, not individual users

## Autoscaling
- HPA: scale by CPU/memory or custom metrics
- VPA: adjust resource requests automatically
- Cluster Autoscaler: add/remove nodes
- KEDA: event-driven scaling (SQS queue depth, etc.)