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notque
kubernetes

Kubernetes operations: debugging, security, RBAC, and infrastructure tooling.

Overview

Publishernotque
Repositoryvexjoy-agent
Skill namekubernetes
Stars
443
Forks
50
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Kubernetes 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/notque/vexjoy-agent/tree/main/skills/infrastructure/kubernetes
  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/notque/vexjoy-agent.git /tmp/vexjoy-agent
mkdir -p .claude/skills
cp -r /tmp/vexjoy-agent/skills/infrastructure/kubernetes .claude/skills/kubernetes
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kubernetes 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 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 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.

Kubernetes Skill

Three domains: debugging (pod triage, networking, resources), security (RBAC, pod hardening, network isolation, supply chain), and cobaltcore (KVM exporter, hypervisor metrics). Select by request signal, then follow the phases below.

Always specify -n <namespace> in every kubectl command. Use read-only commands to gather evidence before proposing changes.


Domain Selection

SignalDomain
CrashLoopBackOff, OOMKilled, ImagePullBackOff, PendingDebugging
Service unreachable, DNS failure, port-forwardDebugging (network)
CPU throttling, memory limit, disk pressureDebugging (resources)
RBAC, permissions, roles, ServiceAccountSecurity (access)
Pod hardening, container security, PodSecuritySecurity (pods)
NetworkPolicy, default-deny, namespace isolationSecurity (network)
Image signing, secrets, admission controlSecurity (supply chain)
KVM exporter, cobaltcore, hypervisor metricsCobaltcore

Phase 1: TRIAGE

Debugging Triage Flow

Follow this sequence for every pod or workload issue. Do not skip steps -- many failures are only visible in events and describe output, not in logs.

bash
kubectl get pods -n <namespace> -o wide
kubectl describe pod <pod-name> -n <namespace>
kubectl logs <pod-name> -n <namespace> -c <container-name>
kubectl logs <pod-name> -n <namespace> -c <container-name> --previous
kubectl get events -n <namespace> --sort-by='.lastTimestamp'
kubectl exec -it <pod-name> -n <namespace> -c <container-name> -- /bin/sh

Always check --previous logs for crashed containers before current logs -- restarting destroys them permanently.

Diagnosis routing:

SymptomAction
CrashLoopBackOff, ImagePullBackOff, Pending, FailedSchedulingCheck describe output for events, previous logs, image pull errors
Service unreachable, DNS failureCheck service endpoints, CoreDNS, NetworkPolicy below
CPU throttling, OOMKill, disk pressureCheck resource limits, requests vs actual, node capacity
"no endpoints available for service"Compare svc selector with pod labels

Network debugging:

bash
# Verify service has endpoints
kubectl get endpoints <service-name> -n <namespace>
# DNS lookup from inside cluster
kubectl run dns-debug --rm -it --restart=Never --image=busybox:1.36 -n <namespace> -- \
  nslookup <service-name>.<namespace>.svc.cluster.local
# Check CoreDNS
kubectl get pods -n kube-system -l k8s-app=kube-dns
kubectl logs -n kube-system -l k8s-app=kube-dns --tail=50
# Port-forward for local testing
kubectl port-forward svc/<service-name> -n <namespace> 8080:80

Security Domain Selection

For security requests, provide concrete YAML manifests from the patterns below. Reference-backed specifics, not generic advice.

RBAC patterns: Prefer namespace-scoped Roles over ClusterRoles. Write exact verbs and resources. Create dedicated ServiceAccounts per workload. Set automountServiceAccountToken: false on pods that need no API access.

yaml
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  namespace: app-team
  name: deployment-reader
rules:
  - apiGroups: ["apps"]
    resources: ["deployments"]
    verbs: ["get", "list", "watch"]

Pod security: Enforce PodSecurity labels at namespace level. All containers: runAsNonRoot: true, readOnlyRootFilesystem: true, allowPrivilegeEscalation: false, capabilities: drop: ["ALL"]. Use distroless base images. Pin image digests.

yaml
labels:
  pod-security.kubernetes.io/enforce: restricted
  pod-security.kubernetes.io/warn: restricted

Network policies: Start with default-deny for ingress and egress. Add allow-list rules per service. Always allow DNS egress (UDP/TCP 53).

yaml
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: default-deny-all
spec:
  podSelector: {}
  policyTypes: [Ingress, Egress]

Cobaltcore Domain

Components: KVM Exporter (cobaltcore-dev/kvm-exporter). Load references/cobalt-kvm-exporter.md for architecture, metric catalogs, configuration, and deployment. Pair with go-patterns for code, prometheus-grafana-engineer for metrics.


Phase 2: DIAGNOSE / RESPOND

Debugging: Follow the triage flow. Gather evidence with read-only commands before proposing changes.

Security: Provide copy-paste-ready YAML using the patterns in Phase 1.

Cobaltcore: Use component-specific references for architecture, metrics, concurrency patterns, and testing.


Phase 3: VERIFY

  • Debugging: Confirm the fix resolves the symptom with the same triage commands.
  • Security: Validate against the PodSecurity standards and RBAC least-privilege patterns above.
  • Cobaltcore: Verify against component test patterns in references/cobalt-testing-patterns.md.

Deep References

SignalReferenceContent
KVM exporter architecture, metrics, configreferences/cobalt-kvm-exporter.mdFull component reference (463 lines)
Cobaltcore concurrency, goroutine, semaphorereferences/cobalt-concurrency-patterns.mdGo concurrency patterns (268 lines)
Cobaltcore testing, mock, Kind clusterreferences/cobalt-testing-patterns.mdTesting strategies (271 lines)

Bundled files

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

Frequently asked questions

What does the Kubernetes AI skill do?

Kubernetes operations: debugging, security, RBAC, and infrastructure tooling.

Why use Kubernetes on TypingMind?

Because you install it once and use it with any model. Kubernetes 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 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/notque/vexjoy-agent/tree/main/skills/infrastructure/kubernetes. 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?

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?

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

Is the Kubernetes AI skill free?

Yes. It is published on GitHub by notque 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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