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Kubernetes Operations

CommunityPopular
rohitg00
kubernetes-operations

Kubernetes operations including manifests, Helm charts, operators, troubleshooting, and resource management

Overview

Publisherrohitg00
Repositoryawesome-claude-code-toolkit
Skill namekubernetes-operations
Stars
2.6K
Forks
963
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Kubernetes Operations 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/rohitg00/awesome-claude-code-toolkit.git /tmp/awesome-claude-code-toolkit
mkdir -p .claude/skills
cp -r /tmp/awesome-claude-code-toolkit/skills/kubernetes-operations .claude/skills/kubernetes-operations
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kubernetes Operations 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 Operations 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 Operations 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 Operations

Deployment Manifest

yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: api-server
  labels:
    app: api-server
    version: v1
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  selector:
    matchLabels:
      app: api-server
  template:
    metadata:
      labels:
        app: api-server
        version: v1
    spec:
      containers:
        - name: api
          image: registry.example.com/api:1.2.0
          ports:
            - containerPort: 8080
          resources:
            requests:
              cpu: 100m
              memory: 128Mi
            limits:
              cpu: 500m
              memory: 512Mi
          livenessProbe:
            httpGet:
              path: /healthz
              port: 8080
            initialDelaySeconds: 10
            periodSeconds: 15
          readinessProbe:
            httpGet:
              path: /ready
              port: 8080
            initialDelaySeconds: 5
            periodSeconds: 5
          env:
            - name: DATABASE_URL
              valueFrom:
                secretKeyRef:
                  name: db-credentials
                  key: url
      topologySpreadConstraints:
        - maxSkew: 1
          topologyKey: kubernetes.io/hostname
          whenUnsatisfiable: DoNotSchedule
          labelSelector:
            matchLabels:
              app: api-server

Always set resource requests and limits. Use topology spread constraints for high availability.

Helm Chart Structure

chart/
  Chart.yaml
  values.yaml
  values-staging.yaml
  values-production.yaml
  templates/
    deployment.yaml
    service.yaml
    ingress.yaml
    hpa.yaml
    _helpers.tpl
yaml
# values.yaml
replicaCount: 2
image:
  repository: registry.example.com/api
  tag: "1.2.0"
  pullPolicy: IfNotPresent
resources:
  requests:
    cpu: 100m
    memory: 128Mi
  limits:
    cpu: 500m
    memory: 512Mi
autoscaling:
  enabled: true
  minReplicas: 2
  maxReplicas: 10
  targetCPUUtilization: 70

HorizontalPodAutoscaler

yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: api-server
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api-server
  minReplicas: 2
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70
    - type: Resource
      resource:
        name: memory
        target:
          type: Utilization
          averageUtilization: 80
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300

Troubleshooting Commands

bash
# Pod diagnostics
kubectl describe pod <pod-name> -n <namespace>
kubectl logs <pod-name> -c <container> --previous
kubectl exec -it <pod-name> -- /bin/sh

# Resource usage
kubectl top pods -n <namespace> --sort-by=memory
kubectl top nodes

# Network debugging
kubectl run debug --image=nicolaka/netshoot --rm -it -- bash
nslookup <service-name>.<namespace>.svc.cluster.local

# Events sorted by time
kubectl get events -n <namespace> --sort-by='.lastTimestamp'

# Find pods not running
kubectl get pods -A --field-selector=status.phase!=Running

Anti-Patterns

  • Running containers as root without securityContext.runAsNonRoot: true
  • Missing resource requests/limits (causes scheduling issues and noisy neighbors)
  • Using latest tag instead of pinned image versions
  • Not setting PodDisruptionBudget for critical workloads
  • Storing secrets in ConfigMaps instead of Secrets (or external secret managers)
  • Ignoring pod anti-affinity for replicated deployments

Checklist

  • All containers have resource requests and limits
  • Liveness and readiness probes configured
  • Images use specific version tags, not latest
  • Secrets stored in Kubernetes Secrets or external vault
  • PodDisruptionBudget set for production workloads
  • NetworkPolicies restrict traffic between namespaces
  • Topology spread constraints or anti-affinity for HA
  • Helm values split per environment (staging, production)

Frequently asked questions

What does the Kubernetes Operations AI skill do?

Kubernetes operations including manifests, Helm charts, operators, troubleshooting, and resource management

Why use Kubernetes Operations on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/awesome-claude-code-toolkit/tree/main/skills/kubernetes-operations. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Kubernetes Operations?

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 Operations?

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

Is the Kubernetes Operations AI skill free?

Yes. It is published on GitHub by rohitg00 under the Apache-2.0 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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