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Kubernetes Specialist

CommunityPopular
Jeffallan
kubernetes-specialist

Use when deploying or managing Kubernetes workloads. Invoke to create deployment manifests, configure pod security policies, set up service accounts, define network isolation rules, debug pod crashes, analyze resource limits, inspect container logs, or right-size workloads. Use for Helm charts, RBAC policies, NetworkPolicies, storage configuration, performance optimization, GitOps pipelines, and multi-cluster management.

Overview

PublisherJeffallan
Repositoryclaude-skills
Skill namekubernetes-specialist
Stars
11.5K
Forks
1.1K
Bundled files
11
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.

  • 11 bundled files

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

  • Open source

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

Installation

Install the Kubernetes Specialist 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.
  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/Jeffallan/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/skills/kubernetes-specialist .claude/skills/kubernetes-specialist
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kubernetes Specialist 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 Specialist 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 Specialist 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 Specialist

When to Use This Skill

  • Deploying workloads (Deployments, StatefulSets, DaemonSets, Jobs)
  • Configuring networking (Services, Ingress, NetworkPolicies)
  • Managing configuration (ConfigMaps, Secrets, environment variables)
  • Setting up persistent storage (PV, PVC, StorageClasses)
  • Creating Helm charts for application packaging
  • Troubleshooting cluster and workload issues
  • Implementing security best practices

Core Workflow

  1. Analyze requirements — Understand workload characteristics, scaling needs, security requirements
  2. Design architecture — Choose workload types, networking patterns, storage solutions
  3. Implement manifests — Create declarative YAML with proper resource limits, health checks
  4. Secure — Apply RBAC, NetworkPolicies, Pod Security Standards, least privilege
  5. Validate — Run kubectl rollout status, kubectl get pods -w, and kubectl describe pod <name> to confirm health; roll back with kubectl rollout undo if needed

Reference Guide

Load detailed guidance based on context:

TopicReferenceLoad When
Workloadsreferences/workloads.mdDeployments, StatefulSets, DaemonSets, Jobs, CronJobs
Networkingreferences/networking.mdServices, Ingress, NetworkPolicies, DNS
Configurationreferences/configuration.mdConfigMaps, Secrets, environment variables
Storagereferences/storage.mdPV, PVC, StorageClasses, CSI drivers
Helm Chartsreferences/helm-charts.mdChart structure, values, templates, hooks, testing, repositories
Troubleshootingreferences/troubleshooting.mdkubectl debug, logs, events, common issues
Custom Operatorsreferences/custom-operators.mdCRD, Operator SDK, controller-runtime, reconciliation
Service Meshreferences/service-mesh.mdIstio, Linkerd, traffic management, mTLS, canary
GitOpsreferences/gitops.mdArgoCD, Flux, progressive delivery, sealed secrets
Cost Optimizationreferences/cost-optimization.mdVPA, HPA tuning, spot instances, quotas, right-sizing
Multi-Clusterreferences/multi-cluster.mdCluster API, federation, cross-cluster networking, DR

Constraints

MUST DO

  • Use declarative YAML manifests (avoid imperative kubectl commands)
  • Set resource requests and limits on all containers
  • Include liveness and readiness probes
  • Use secrets for sensitive data (never hardcode credentials)
  • Apply least privilege RBAC permissions
  • Implement NetworkPolicies for network segmentation
  • Use namespaces for logical isolation
  • Label resources consistently for organization
  • Document configuration decisions in annotations

MUST NOT DO

  • Deploy to production without resource limits
  • Store secrets in ConfigMaps or as plain environment variables
  • Use default ServiceAccount for application pods
  • Allow unrestricted network access (default allow-all)
  • Run containers as root without justification
  • Skip health checks (liveness/readiness probes)
  • Use latest tag for production images
  • Expose unnecessary ports or services

Common YAML Patterns

Deployment with resource limits, probes, and security context

yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
  namespace: my-namespace
  labels:
    app: my-app
    version: "1.2.3"
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
        version: "1.2.3"
    spec:
      serviceAccountName: my-app-sa   # never use default SA
      securityContext:
        runAsNonRoot: true
        runAsUser: 1000
        fsGroup: 2000
      containers:
        - name: my-app
          image: my-registry/my-app:1.2.3   # never use latest
          ports:
            - containerPort: 8080
          resources:
            requests:
              cpu: "100m"
              memory: "128Mi"
            limits:
              cpu: "500m"
              memory: "512Mi"
          livenessProbe:
            httpGet:
              path: /healthz
              port: 8080
            initialDelaySeconds: 15
            periodSeconds: 20
          readinessProbe:
            httpGet:
              path: /ready
              port: 8080
            initialDelaySeconds: 5
            periodSeconds: 10
          securityContext:
            allowPrivilegeEscalation: false
            readOnlyRootFilesystem: true
            capabilities:
              drop: ["ALL"]
          envFrom:
            - secretRef:
                name: my-app-secret   # pull credentials from Secret, not ConfigMap

Minimal RBAC (least privilege)

yaml
apiVersion: v1
kind: ServiceAccount
metadata:
  name: my-app-sa
  namespace: my-namespace
---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: my-app-role
  namespace: my-namespace
rules:
  - apiGroups: [""]
    resources: ["configmaps"]
    verbs: ["get", "list"]   # grant only what is needed
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: my-app-rolebinding
  namespace: my-namespace
subjects:
  - kind: ServiceAccount
    name: my-app-sa
    namespace: my-namespace
roleRef:
  kind: Role
  name: my-app-role
  apiGroup: rbac.authorization.k8s.io

NetworkPolicy (default-deny + explicit allow)

yaml
# Deny all ingress and egress by default
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: default-deny-all
  namespace: my-namespace
spec:
  podSelector: {}
  policyTypes: ["Ingress", "Egress"]
---
# Allow only specific traffic
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-my-app
  namespace: my-namespace
spec:
  podSelector:
    matchLabels:
      app: my-app
  policyTypes: ["Ingress"]
  ingress:
    - from:
        - podSelector:
            matchLabels:
              app: frontend
      ports:
        - protocol: TCP
          port: 8080

Validation Commands

After deploying, verify health and security posture:

bash
# Watch rollout complete
kubectl rollout status deployment/my-app -n my-namespace

# Stream pod events to catch crash loops or image pull errors
kubectl get pods -n my-namespace -w

# Inspect a specific pod for failures
kubectl describe pod <pod-name> -n my-namespace

# Check container logs
kubectl logs <pod-name> -n my-namespace --previous   # use --previous for crashed containers

# Verify resource usage vs. limits
kubectl top pods -n my-namespace

# Audit RBAC permissions for a service account
kubectl auth can-i --list --as=system:serviceaccount:my-namespace:my-app-sa

# Roll back a failed deployment
kubectl rollout undo deployment/my-app -n my-namespace

Output Templates

When implementing Kubernetes resources, provide:

  1. Complete YAML manifests with proper structure
  2. RBAC configuration if needed (ServiceAccount, Role, RoleBinding)
  3. NetworkPolicy for network isolation
  4. Brief explanation of design decisions and security considerations

Documentation

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 Specialist AI skill do?

Use when deploying or managing Kubernetes workloads. Invoke to create deployment manifests, configure pod security policies, set up service accounts, define network isolation rules, debug pod crashes, analyze resource limits, inspect container logs, or right-size workloads. Use for Helm charts, RBAC policies, NetworkPolicies, storage configuration, performance optimization, GitOps pipelines, and multi-cluster management.

Why use Kubernetes Specialist on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Jeffallan/claude-skills/tree/main/skills/kubernetes-specialist. 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 Specialist?

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 Specialist?

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

Is the Kubernetes Specialist AI skill free?

Yes. It is published on GitHub by Jeffallan 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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