kubernetes-specialist
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Kubernetes Specialist
Use this skill for Kubernetes design, architecture, implementation, optimization, and planned changes. For live incidents, unhealthy workloads, CrashLoopBackOff, stuck rollouts, ingress failures, or customer support triage, use k8s-sre-triage first.
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 design-level or planned-change cluster issues after incident triage has narrowed the fault domain
- 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 design-level or planned-change cluster issues after
k8s-sre-triagehas narrowed the fault domain - 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)
- Live Kubernetes incidents or customer support triage (use
k8s-sre-triagefirst) - Cloud infrastructure provisioning (use an installed cloud or infrastructure skill when available)
- Application code debugging (use an installed debugging or application skill when available)
- Database-specific issues (use a database-focused workflow or specialist when available)
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 Type | CPU Request | CPU Limit | Memory Request | Memory Limit |
|---|---|---|---|---|
| Web API | 100m-500m | 1000m | 256Mi-512Mi | 1Gi |
| Worker | 500m-1000m | 2000m | 512Mi-1Gi | 2Gi |
| Database | 1000m-2000m | 4000m | 2Gi-4Gi | 8Gi |
| Cache | 100m-250m | 500m | 1Gi-4Gi | 8Gi |
| Batch Job | 500m-2000m | 4000m | 1Gi-4Gi | 8Gi |
Node Pool Strategy
| Use Case | Instance Type | Scaling | Cost |
|---|---|---|---|
| System pods | t3.large (3 nodes) | Fixed | Low |
| Applications | m5.xlarge | Auto 3-20 | Medium |
| Batch/Spot | m5.large-2xlarge | Auto 0-50 | Very Low |
| GPU workloads | p3.2xlarge | Manual | High |
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
- Detailed Technical Reference: See REFERENCE.md
- Code Examples & Patterns: See EXAMPLES.md
<!-- tomevault:4.0:skill_md:2026-05-22 -->Source: Canepro/codex-skills — distributed by TomeVault.
- Fluxly category
- DevOps
- Author-declared agents
- No explicit declaration found; this is not inferred or tested compatibility
- Static check
- 88 / 100 · heuristic scan, not runtime safety proof
- Author / version / license
- @tomevault-io · no license declared
- Fluxly token estimate
- Lean
- Fluxly setup estimate
- Manual integration
- External API key
- No requirement detected
- Detected OS requirements
- Docker
- Runtime requirements
- Node.js · Docker
- Detected file/system behavior
-
- Read-only
- Write / modify
- Detected network behavior
- Local-only
- Install commands
- None (reference only)
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
The current SKILL.md does not define a fixed output example. 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…
Designing Kubernetes cluster architecture for production workloads Implementing Helm charts, operators, or GitOps workflows (ArgoCD, Flux) Troubleshooting design-level or planned-change cluster issues after incident triage has narrowed the fault domain
Invoke this skill when: Designing Kubernetes cluster architecture for production workloads Implementing Helm charts, operators, or GitOps workflows
Decision Framework
Deployment Strategy Selection
Workload Type · CPU Request · CPU Limit · Memory Request · Memory Limit Web API · 100m-500m · 1000m · 256Mi-512Mi · 1Gi Worker · 500m-1000m · 2000m · 512Mi-1Gi · 2Gi
# Kubernetes Specialist
Use this skill for Kubernetes design, architecture, implementation, optimization, and planned changes. For live incidents, unhealthy workloads, CrashLoopBackOff, stuck rollouts, ingress failures, or customer support triage, use `k8s-sre-triage` first.
## 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 design-level or planned-change cluster issues after incident triage has narrowed the fault domain
- 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 design-level or planned-change cluster issues after `k8s-sre-triage` has narrowed the fault domain
- 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)
- Live Kubernetes incidents or customer support triage (use `k8s-sre-triage` first)
… Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.
sections -> Purpose → When to Use → Quick Start → Decision Framework → Deployment Strategy Selection → Resource Configuration Matrix
terms -> Invoke this skill when · Do NOT invoke when · Web API · Worker · Database · Cache · Batch Job · System pods
files/cmd -> k8s-sre-triage · Batch/Spot · REFERENCE.md · EXAMPLES.md · Canepro/codex-skills · github.com/Canepro/codex-skills
body sha256 -> dce57a720fd7
Decide Fit First
Design Intent
How To Use It
Boundaries And Review