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Kubernetes deployment patterns

Skill NickCrew/Claude-Cortex/skills/kubernetes-deployment-patterns

Claude Cortex

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npx -y skills add NickCrew/Claude-Cortex --skill kubernetes-deployment-patterns

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Kubernetes deployment strategies and workload patterns for production-grade applications. Use when deploying to Kubernetes, implementing rollout strategies, or designing cloud-native application architectures.

SKILL.md

5.5 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

Kubernetes Deployment Patterns

Expert guidance for production-grade Kubernetes deployments covering deployment strategies, workload types, configuration management, resource optimization, and autoscaling patterns for cloud-native applications.

When to Use This Skill

  • Implementing deployment strategies (rolling updates, blue-green, canary releases)
  • Choosing appropriate workload types (Deployment, StatefulSet, DaemonSet, Job)
  • Designing rollout strategies for zero-downtime deployments
  • Implementing configuration management with ConfigMaps and Secrets
  • Setting up resource management and autoscaling (HPA, VPA)
  • Configuring health checks and probe strategies
  • Designing highly available applications on Kubernetes
  • Implementing batch processing and scheduled jobs

Core Concepts

Deployment Strategies

Rolling Update: Gradually replace old pods with new ones (zero-downtime, default) Recreate: Terminate all old pods before creating new ones (brief downtime) Blue-Green: Run two environments, switch traffic instantly (2x resources) Canary: Gradually shift traffic to new version while monitoring (risk mitigation)

Workload Types

Deployment: Stateless applications (web servers, APIs, microservices) StatefulSet: Stateful applications (databases, message queues) DaemonSet: Node-level services (log collectors, monitoring agents) Job: One-time tasks (batch processing, migrations) CronJob: Scheduled tasks (backups, periodic reports)

Resource Management

Requests: Guaranteed resources for scheduling Limits: Maximum resources enforced by kubelet HPA: Horizontal Pod Autoscaler (scale replicas based on metrics) VPA: Vertical Pod Autoscaler (adjust resource requests/limits)

Quick Reference

TaskLoad reference
Deployment strategies (rolling, blue-green, canary)skills/kubernetes-deployment-patterns/references/deployment-strategies.md
Workload types (Deployment, StatefulSet, DaemonSet, Job)skills/kubernetes-deployment-patterns/references/workload-types.md
Configuration management (ConfigMaps, Secrets)skills/kubernetes-deployment-patterns/references/configuration-management.md
Resource management and autoscaling (HPA, VPA)skills/kubernetes-deployment-patterns/references/resource-management.md
Production best practices and securityskills/kubernetes-deployment-patterns/references/production-best-practices.md

Workflow

1. Choose Deployment Strategy

# Rolling update for standard deployments
strategy:
  type: RollingUpdate
  rollingUpdate:
    maxSurge: 1
    maxUnavailable: 0

# Recreate for incompatible versions
strategy:
  type: Recreate

2. Select Workload Type

  • Stateless? → Use Deployment
  • Stateful with persistent identity? → Use StatefulSet
  • One pod per node? → Use DaemonSet
  • Run to completion? → Use Job
  • Run on schedule? → Use CronJob

3. Configure Resources

resources:
  requests:
    memory: "256Mi"
    cpu: "250m"
  limits:
    memory: "512Mi"
    cpu: "1000m"

4. Add Configuration

# ConfigMap for non-sensitive config
envFrom:
- configMapRef:
    name: app-config

# Secret for sensitive data
env:
- name: DB_PASSWORD
  valueFrom:
    secretKeyRef:
      name: db-credentials
      key: password

5. Implement Health Checks

livenessProbe:
  httpGet:
    path: /healthz
    port: 8080
  initialDelaySeconds: 30
  periodSeconds: 10

readinessProbe:
  httpGet:
    path: /ready
    port: 8080
  initialDelaySeconds: 5
  periodSeconds: 5

6. Enable Autoscaling

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
spec:
  scaleTargetRef:
    kind: Deployment
    name: app
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

Common Mistakes

  1. Using latest tag: Always use specific version tags for reproducibility
  2. No resource limits: Can cause resource starvation and cluster instability
  3. Missing health checks: Kubernetes can't manage pod health without probes
  4. Single replica in production: No high availability or resilience
  5. Secrets in ConfigMaps: Use Secrets for sensitive data, not ConfigMaps
  6. No update strategy: Leads to unpredictable deployment behavior
  7. Running as root: Security vulnerability, violates least privilege
  8. No monitoring: Can't detect or debug issues in production

Resources

What ships with it: 5 files

24.5 KB alongside SKILL.md

references/

Gives 0 of the 12 instructions most containers cloud skills give in ~1.2k tokens

Counted across 607 of the 657 authors here whose files we hold, read 2026-08-07

  • Run containers as a non-root userin 66 of 607, across 46 files
  • Use multi-stage buildsin 53 of 607, across 44 files
  • Use Promise.all for independent operationsin 47 of 607, across 13 files
  • Import directly instead of barrel filesin 46 of 607, across 12 files
  • Use ternary instead of AND for conditionalsin 45 of 607, across 12 files
  • Use Set or Map for O(1) lookupsin 42 of 607, across 10 files
  • Create a .dockerignore filein 41 of 607, across 31 files
  • Read individual rule files for detailsin 39 of 607, across 9 files
  • Copy dependency files before source codein 36 of 607, across 23 files
  • Authenticate server actions like API routesin 35 of 607, across 7 files
  • Use next/dynamic for heavy componentsin 34 of 607, across 9 files
  • Use React.cache for per-request deduplicationin 34 of 607, across 10 files

Said here and by no other author read

  • select correct workload type for application
  • configure cpu and memory resources
  • enable autoscaling using metrics
  • use specific container image version tags

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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