Kubernetes Deploying logo

Kubernetes Deploying

Community
spencerpauly
kubernetes-deploying

Deploy applications to Kubernetes — Deployments, Services, Ingress, ConfigMaps, Secrets, health checks, and scaling.

Overview

Publisherspencerpauly
Repositoryawesome-cursor-skills
Skill namekubernetes-deploying
Stars
848
Forks
169
Bundled files
Instructions only
LicenseCC0-1.0
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 spencerpauly on GitHub. Read the source before you install it.

Installation

Install the Kubernetes Deploying 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/spencerpauly/awesome-cursor-skills/tree/main/resources/kubernetes-deploying
  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/spencerpauly/awesome-cursor-skills.git /tmp/awesome-cursor-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-cursor-skills/resources/kubernetes-deploying .claude/skills/kubernetes-deploying
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kubernetes Deploying 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 Deploying 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 Deploying 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 Deploying

Deploy and manage applications on Kubernetes.

Core Resources

Deployment

yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
  labels:
    app: my-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
        - name: my-app
          image: my-registry/my-app:v1.2.3
          ports:
            - containerPort: 3000
          resources:
            requests:
              cpu: 100m
              memory: 128Mi
            limits:
              cpu: 500m
              memory: 512Mi
          livenessProbe:
            httpGet:
              path: /healthz
              port: 3000
            initialDelaySeconds: 10
            periodSeconds: 30
          readinessProbe:
            httpGet:
              path: /ready
              port: 3000
            initialDelaySeconds: 5
            periodSeconds: 10
          env:
            - name: DATABASE_URL
              valueFrom:
                secretKeyRef:
                  name: my-app-secrets
                  key: database-url
            - name: NODE_ENV
              value: production

Service

yaml
apiVersion: v1
kind: Service
metadata:
  name: my-app
spec:
  selector:
    app: my-app
  ports:
    - port: 80
      targetPort: 3000
  type: ClusterIP

Ingress

yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: my-app
  annotations:
    cert-manager.io/cluster-issuer: letsencrypt-prod
spec:
  tls:
    - hosts:
        - app.example.com
      secretName: my-app-tls
  rules:
    - host: app.example.com
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: my-app
                port:
                  number: 80

ConfigMap & Secret

yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: my-app-config
data:
  LOG_LEVEL: info
  FEATURE_FLAGS: '{"darkMode": true}'
---
apiVersion: v1
kind: Secret
metadata:
  name: my-app-secrets
type: Opaque
stringData:
  database-url: postgresql://user:pass@host:5432/db
  api-key: sk-abc123

Common Commands

bash
# Apply manifests
kubectl apply -f k8s/

# Check deployment status
kubectl rollout status deployment/my-app

# View pods
kubectl get pods -l app=my-app

# View logs
kubectl logs -f deployment/my-app

# Execute into a pod
kubectl exec -it <pod-name> -- /bin/sh

# Scale
kubectl scale deployment/my-app --replicas=5

# Rollback
kubectl rollout undo deployment/my-app

# Port forward for local debugging
kubectl port-forward svc/my-app 3000:80

Deployment Strategies

StrategyHowWhen
Rolling update (default)Replace pods one at a timeMost deployments
RecreateKill all old pods, start new onesWhen you can't run two versions simultaneously
Blue/greenRun two full environments, switch trafficNeed instant rollback
CanaryRoute small % of traffic to new versionHigh-risk changes

Rolling update config:

yaml
spec:
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0

Health Checks

Always set both:

  • livenessProbe: "Is the process healthy?" — restarts the pod if it fails
  • readinessProbe: "Can it handle traffic?" — removes from service if it fails

Common probe types:

  • httpGet: Hit an HTTP endpoint (most common)
  • exec: Run a command in the container
  • tcpSocket: Check if a port is open

Horizontal Pod Autoscaler

yaml
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: my-app
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70

Tips

  • Always set resource requests and limits
  • Use namespaces to isolate environments (dev, staging, prod)
  • Never put secrets in plaintext YAML committed to git — use Sealed Secrets, SOPS, or external secret managers
  • Tag images with specific versions, never use :latest in production
  • Set PodDisruptionBudget for high-availability workloads

Frequently asked questions

What does the Kubernetes Deploying AI skill do?

Deploy applications to Kubernetes — Deployments, Services, Ingress, ConfigMaps, Secrets, health checks, and scaling.

Why use Kubernetes Deploying on TypingMind?

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

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

Which AI models can use Kubernetes Deploying?

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 Deploying?

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

Is the Kubernetes Deploying AI skill free?

Yes. It is published on GitHub by spencerpauly under the CC0-1.0 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇