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kubernetes-specialist

kubernetes-specialist

github.com/404kidwizCloud & DevOps113 installs
Repository

About kubernetes-specialist

A Kubernetes-focused skill for production cluster design, deployment, operations, and security. It is intended for teams planning or troubleshooting Kubernetes environments, with checklists and decision guidance across these tasks.

Key features

  1. 01Production cluster architecture planning
  2. 02Helm, operators, and GitOps workflows
  3. 03Networking, storage, and performance troubleshooting
  4. 04Upgrade and multi-cluster strategies
  5. 05Resource, security, and observability checks

Use cases

  1. 01Plan cluster architecture for production workloads
  2. 02Develop Kubernetes upgrade or multi-cluster plans
  3. 03Troubleshoot cluster networking, storage, or performance

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/404kidwiz/claude-supercode-skills --skill kubernetes-specialist

After installing, type/kubernetes-specialistin Claude Code to confirm it loaded.

Bundled filesEXAMPLES.mdREFERENCE.md

Details

From the project README

Kubernetes Specialist

Purpose

Provides expert Kubernetes orchestration and cloud-native application expertise with deep knowledge of container orchestration, cluster management, and production-grade deployments. Specializes in Kubernetes architecture, Helm charts, operators, multi-cluster management, and GitOps workflows across EKS, AKS, GKE, and on-premises deployments.

When to Use

  • Designing Kubernetes cluster architecture for production workloads
  • Implementing Helm charts, operators, or GitOps workflows (ArgoCD, Flux)
  • Troubleshooting cluster issues (networking, storage, performance)
  • Planning Kubernetes upgrades or multi-cluster strategies
  • Optimizing resource utilization and cost in Kubernetes environments
  • Setting up service mesh (Istio, Linkerd) and observability
  • Implementing Kubernetes security and RBAC policies

Quick Start

Invoke this skill when:

  • Designing Kubernetes cluster architecture for production workloads
  • Implementing Helm charts, operators, or GitOps workflows
  • Troubleshooting cluster issues (networking, storage, performance)
  • Planning Kubernetes upgrades or multi-cluster strategies
  • Optimizing resource utilization and cost in Kubernetes environments

Do NOT invoke when:

  • Simple Docker container needs (use docker commands directly)
  • Cloud infrastructure provisioning (use cloud-architect instead)
  • Application code debugging (use backend-developer/frontend-developer)
  • Database-specific issues (use database-administrator instead)

Decision Framework

Deployment Strategy Selection
├─ Zero downtime required?
│   ├─ Instant rollback needed → Blue-Green Deployment
│   │   Pros: Instant switch, easy rollback
│   │   Cons: 2x resources during deployment
│   │
│   ├─ Gradual rollout → Canary Deployment
│   │   Pros: Test with subset of traffic
│   │   Cons: Complex routing setup
│   │
│   └─ Simple updates → Rolling Update (default)
│       Pros: Built-in, no extra resources
│       Cons: Rollback takes time
│
├─ Stateful application?
│   ├─ Database → StatefulSet + PVC
│   │   Pros: Stable network IDs, ordered deployment
│   │   Cons: Complex scaling
│   │
│   └─ Stateless → Deployment
│       Pros: Easy scaling, self-healing
│
└─ Batch processing?
    ├─ One-time → Job
    ├─ Scheduled → CronJob
    └─ Parallel processing → Job with parallelism
Resource Configuration Matrix
Workload TypeCPU RequestCPU LimitMemory RequestMemory Limit
Web API100m-500m1000m256Mi-512Mi1Gi
Worker500m-1000m2000m512Mi-1Gi2Gi
Database1000m-2000m4000m2Gi-4Gi8Gi
Cache100m-250m500m1Gi-4Gi8Gi
Batch Job500m-2000m4000m1Gi-4Gi8Gi
Node Pool Strategy
Use CaseInstance TypeScalingCost
System podst3.large (3 nodes)FixedLow
Applicationsm5.xlargeAuto 3-20Medium
Batch/Spotm5.large-2xlargeAuto 0-50Very Low
GPU workloadsp3.2xlargeManualHigh
Red Flags → Escalate

STOP and escalate if:

  • Cluster upgrade with breaking API changes (deprecated versions)
  • Multi-region active-active requirements
  • Compliance requirements (PCI-DSS, HIPAA) need validation
  • Custom scheduler or controller development needed
  • etcd corruption or cluster state issues

Quality Checklist

Cluster Configuration
  • Multi-AZ deployment (nodes spread across availability zones)
  • Node autoscaling configured (Cluster Autoscaler or Karpenter)
  • System node pool with taints (separate critical addons from apps)
  • Encryption enabled (secrets at rest with KMS)
  • Audit logging enabled (API server logs)
Security
  • Pod Security Standards enforced (restricted or baseline)
  • Network policies configured (default deny + explicit allow)
  • RBAC configured (least privilege for all service accounts)
  • Image scanning enabled (scan for vulnerabilities)
  • Private container registry configured
Resource Management
  • All pods have resource requests and limits
  • HorizontalPodAutoscalers configured for scalable workloads
  • PodDisruptionBudgets defined (prevent too many pods down)
  • ResourceQuotas set per namespace
  • LimitRanges defined (default limits for pods)
High Availability
  • Deployments have ≥2 replicas
  • Anti-affinity rules prevent pod co-location
  • Readiness and liveness probes configured
  • PodDisruptionBudgets allow for rolling updates
  • Multi-region cluster (if global scale required)
Observability
  • Metrics server installed (kubectl top works)
  • Prometheus monitoring application metrics
  • Centralized logging (CloudWatch, Elasticsearch, Loki)
  • Distributed tracing (Jaeger, Tempo)
  • Dashboards for cluster and application health
Disaster Recovery
  • Velero installed for cluster backups
  • Backup schedule configured (daily minimum)
  • Restore tested (annual drill)
  • etcd backups automated (cloud-managed clusters)

Additional Resources

Comments

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