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Kubernetes Skill

Community
LukasNiessen
kubernetes-skill

Prevent Kubernetes hallucinations by diagnosing and fixing failure modes: insecure workload defaults, resource starvation, network exposure, privilege sprawl, fragile rollouts, and API drift. Use when generating, reviewing, refactoring, or migrating manifests, Helm charts, Kustomize overlays, cluster policies, and platform-specific Kubernetes work for EKS, GKE, AKS, OpenShift, GitOps controllers, or observability stacks.

Overview

PublisherLukasNiessen
Repositorykubernetes-skill
Skill namekubernetes-skill
Stars
465
Forks
83
Bundled files
61
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 61 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LukasNiessen on GitHub. Read the source before you install it.

Installation

Install the Kubernetes Skill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LukasNiessen/kubernetes-skill/tree/main
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LukasNiessen/kubernetes-skill.git \
  .claude/skills/kubernetes-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kubernetes Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Kubernetes Skill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Kubernetes Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 1 more files.

Frequently asked questions

What does the Kubernetes Skill AI skill do?

Prevent Kubernetes hallucinations by diagnosing and fixing failure modes: insecure workload defaults, resource starvation, network exposure, privilege sprawl, fragile rollouts, and API drift. Use when generating, reviewing, refactoring, or migrating manifests, Helm charts, Kustomize overlays, cluster policies, and platform-specific Kubernetes work for EKS, GKE, AKS, OpenShift, GitOps controllers, or observability stacks.

Why use Kubernetes Skill on TypingMind?

Because you install it once and use it with any model. Kubernetes Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Kubernetes Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LukasNiessen/kubernetes-skill/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Kubernetes Skill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Kubernetes Skill?

As many as you like. As long as a model supports skills, you can use Kubernetes Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Kubernetes Skill AI skill free?

Yes. It is published on GitHub by LukasNiessen under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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