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kubernetes-skill

kubernetes-skill

github.com/lukasniessenCloud & DevOps4671.4k installsUpdated 6w ago
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About kubernetes-skill

kubernetes-skill guides Agents through Kubernetes workload design and troubleshooting. It gathers cluster context, selects references by failure mode, and pairs remediation with validation and rollback steps.

Key features

  1. 01Capture cluster and deployment context
  2. 02Identify risks by failure mode
  3. 03Load relevant references selectively
  4. 04Generate manifests and policy artifacts
  5. 05Plan validation and rollback

Use cases

  1. 01Review workload security settings
  2. 02Diagnose resource or network issues
  3. 03Correct RBAC and rollout settings
  4. 04Address cloud or GitOps-specific patterns

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/lukasniessen/kubernetes-skill --skill kubernetes-skill

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

Bundled files

References and scripts installed along with SKILL.md.

.gitignore
CHANGELOG.md
CONTRIBUTING.md
PHILOSOPHY.md
LICENSE
CODE_OF_CONDUCT.md
README.md
.claude-plugin/
marketplace.json
.github/
CODEOWNERS
PULL_REQUEST_TEMPLATE.md
dependabot.yml
FUNDING.yml
.github/workflows/
deploy-docs.yml
stale.yml
validate.yml
docs/
SUMMARY.md
.gitignore
README.md
book.json
docs/advanced/
token-efficiency.md
docs/architecture/
multi-tenancy.md
storage-and-state.md
workload-patterns.md
docs/community/
changelog.md
contributing.md
docs/core-concepts/
workflow.md
philosophy.md
failure-modes.md
docs/examples/
bad-patterns.md
do-dont-checklist.md

Details

From the project README

KubeShark: Failure-Mode Workflow for Kubernetes

Run this workflow top to bottom.

1) Capture execution context

Record before writing manifests:

  • cluster version (e.g. 1.30, 1.31) and distribution (EKS, GKE, AKS, k3s, vanilla)
  • target namespace and environment criticality (dev/staging/prod)
  • workload type (Deployment, StatefulSet, Job, CronJob, DaemonSet)
  • deployment method (raw YAML, Helm, Kustomize, operator-managed)
  • policy enforcement (Pod Security Admission level, Kyverno, OPA/Gatekeeper)
  • cloud provider and CNI (affects networking, storage classes, load balancers)
  • platform controllers/add-ons (GitOps, observability, ingress, service mesh, autoscaling)

If unknown, state assumptions explicitly.

2) Diagnose likely failure mode(s)

Select one or more based on user intent and risk:

  • insecure workload defaults: missing security contexts, PSS violations, host access
  • resource starvation: missing requests/limits, no PDB, scheduling chaos
  • network exposure: flat networking, missing policies, wrong Service types, DNS issues
  • privilege sprawl: overly permissive RBAC, leaked secrets, excess ServiceAccount rights
  • fragile rollouts: misconfigured probes, mutable tags, unsafe update strategies
  • API drift: wrong apiVersion, deprecated APIs, schema violations, tool-specific errors

3) Load only the relevant reference file(s)

Primary failure-mode references:

  • references/insecure-workload-defaults.md
  • references/resource-starvation.md
  • references/network-exposure.md
  • references/privilege-sprawl.md
  • references/fragile-rollouts.md
  • references/api-drift.md

Supplemental references (only when needed):

  • references/deployment-patterns.md
  • references/stateful-patterns.md
  • references/job-patterns.md
  • references/daemonset-operator-patterns.md
  • references/security-hardening.md
  • references/observability.md
  • references/multi-tenancy.md
  • references/storage-and-state.md
  • references/helm-patterns.md
  • references/kustomize-patterns.md
  • references/validation-and-policy.md
  • references/examples-good.md
  • references/examples-bad.md
  • references/do-dont-patterns.md

Conditional Reference Retrieval (CRR) references (load only when the signal is detected):

  • references/conditional/eks-patterns.md for EKS, AWS, IRSA, EKS Pod Identity, AWS Load Balancer Controller, EBS/EFS CSI, Karpenter
  • references/conditional/gke-patterns.md for GKE, Autopilot, Workload Identity Federation for GKE, Dataplane V2, GCE Ingress, Config Sync
  • references/conditional/aks-patterns.md for AKS, Microsoft Entra Workload ID, Azure CNI, AGIC, Azure Disk/File/Blob CSI
  • references/conditional/openshift-patterns.md for OpenShift, OKD, ROSA, ARO, Routes, SCCs, OLM, oc
  • references/conditional/gitops-controllers.md for Argo CD, ApplicationSet, Flux, GitOps reconciliation, sync waves
  • references/conditional/observability-stacks.md for Prometheus Operator, ServiceMonitor, PodMonitor, OpenTelemetry, Loki, Grafana

Do not load multiple CRR files unless the task spans multiple detected platforms/tools.

4) Propose fix path with explicit risk controls

For each fix, include:

  • why this addresses the failure mode
  • what could still go wrong at deploy time or runtime
  • guardrails (validation commands, policy checks, rollback path)

5) Generate implementation artifacts

When applicable, output:

  • Kubernetes manifests (YAML with security contexts, resource limits, labels)
  • Helm values/templates or Kustomize overlays
  • NetworkPolicies, RBAC resources, PodDisruptionBudgets
  • Policy rules (Kyverno/OPA) and admission controls

6) Validate before finalize

Always provide validation steps tailored to deployment method and risk tier:

  • kubectl apply --dry-run=server or kubectl diff
  • kubeconform for schema validation against target cluster version
  • cross-resource consistency check (label/selector/port alignment)
  • policy scan (PSS profile check, Kyverno/OPA audit) Never recommend direct production apply without reviewed diff and approval.

7) Output contract

Return:

  • assumptions and cluster version floor
  • selected failure mode(s)
  • chosen remediation and tradeoffs
  • validation/test plan
  • rollback/recovery notes (rollout undo, revision history, data safety)

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