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K8s Manifest Generator

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HermeticOrmus
k8s-manifest-generator

Create production-ready Kubernetes manifests for Deployments, Services, ConfigMaps, and Secrets following best practices and security standards. Use when generating Kubernetes YAML manifests, creating K8s resources, or implementing production-grade Kubernetes configurations.

Overview

PublisherHermeticOrmus
RepositoryLibreUIUX-Claude-Code
Skill namek8s-manifest-generator
Stars
104
Forks
18
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the K8s Manifest Generator 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/HermeticOrmus/LibreUIUX-Claude-Code.git /tmp/LibreUIUX-Claude-Code
mkdir -p .claude/skills
cp -r /tmp/LibreUIUX-Claude-Code/plugins/kubernetes-operations/skills/k8s-manifest-generator .claude/skills/k8s-manifest-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable K8s Manifest Generator 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 K8s Manifest Generator 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 K8s Manifest Generator 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 Manifest Generator

Step-by-step guidance for creating production-ready Kubernetes manifests including Deployments, Services, ConfigMaps, Secrets, and PersistentVolumeClaims.

Purpose

This skill provides comprehensive guidance for generating well-structured, secure, and production-ready Kubernetes manifests following cloud-native best practices and Kubernetes conventions.

When to Use This Skill

Use this skill when you need to:

  • Create new Kubernetes Deployment manifests
  • Define Service resources for network connectivity
  • Generate ConfigMap and Secret resources for configuration management
  • Create PersistentVolumeClaim manifests for stateful workloads
  • Follow Kubernetes best practices and naming conventions
  • Implement resource limits, health checks, and security contexts
  • Design manifests for multi-environment deployments

Step-by-Step Workflow

1. Gather Requirements

Understand the workload:

  • Application type (stateless/stateful)
  • Container image and version
  • Environment variables and configuration needs
  • Storage requirements
  • Network exposure requirements (internal/external)
  • Resource requirements (CPU, memory)
  • Scaling requirements
  • Health check endpoints

Questions to ask:

  • What is the application name and purpose?
  • What container image and tag will be used?
  • Does the application need persistent storage?
  • What ports does the application expose?
  • Are there any secrets or configuration files needed?
  • What are the CPU and memory requirements?
  • Does the application need to be exposed externally?

2. Create Deployment Manifest

Follow this structure:

yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: <app-name>
  namespace: <namespace>
  labels:
    app: <app-name>
    version: <version>
spec:
  replicas: 3
  selector:
    matchLabels:
      app: <app-name>
  template:
    metadata:
      labels:
        app: <app-name>
        version: <version>
    spec:
      containers:
      - name: <container-name>
        image: <image>:<tag>
        ports:
        - containerPort: <port>
          name: http
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: /health
            port: http
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: http
          initialDelaySeconds: 5
          periodSeconds: 5
        env:
        - name: ENV_VAR
          value: "value"
        envFrom:
        - configMapRef:
            name: <app-name>-config
        - secretRef:
            name: <app-name>-secret

Best practices to apply:

  • Always set resource requests and limits
  • Implement both liveness and readiness probes
  • Use specific image tags (never :latest)
  • Apply security context for non-root users
  • Use labels for organization and selection
  • Set appropriate replica count based on availability needs

Reference: See references/deployment-spec.md for detailed deployment options

3. Create Service Manifest

Choose the appropriate Service type:

ClusterIP (internal only):

yaml
apiVersion: v1
kind: Service
metadata:
  name: <app-name>
  namespace: <namespace>
  labels:
    app: <app-name>
spec:
  type: ClusterIP
  selector:
    app: <app-name>
  ports:
  - name: http
    port: 80
    targetPort: 8080
    protocol: TCP

LoadBalancer (external access):

yaml
apiVersion: v1
kind: Service
metadata:
  name: <app-name>
  namespace: <namespace>
  labels:
    app: <app-name>
  annotations:
    service.beta.kubernetes.io/aws-load-balancer-type: nlb
spec:
  type: LoadBalancer
  selector:
    app: <app-name>
  ports:
  - name: http
    port: 80
    targetPort: 8080
    protocol: TCP

Reference: See references/service-spec.md for service types and networking

4. Create ConfigMap

For application configuration:

yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: <app-name>-config
  namespace: <namespace>
data:
  APP_MODE: production
  LOG_LEVEL: info
  DATABASE_HOST: db.example.com
  # For config files
  app.properties: |
    server.port=8080
    server.host=0.0.0.0
    logging.level=INFO

Best practices:

  • Use ConfigMaps for non-sensitive data only
  • Organize related configuration together
  • Use meaningful names for keys
  • Consider using one ConfigMap per component
  • Version ConfigMaps when making changes

Reference: See assets/configmap-template.yaml for examples

5. Create Secret

For sensitive data:

yaml
apiVersion: v1
kind: Secret
metadata:
  name: <app-name>-secret
  namespace: <namespace>
type: Opaque
stringData:
  DATABASE_PASSWORD: "changeme"
  API_KEY: "secret-api-key"
  # For certificate files
  tls.crt: |
    -----BEGIN CERTIFICATE-----
    ...
    -----END CERTIFICATE-----
  tls.key: |
    -----BEGIN PRIVATE KEY-----
    ...
    -----END PRIVATE KEY-----

Security considerations:

  • Never commit secrets to Git in plain text
  • Use Sealed Secrets, External Secrets Operator, or Vault
  • Rotate secrets regularly
  • Use RBAC to limit secret access
  • Consider using Secret type: kubernetes.io/tls for TLS secrets

6. Create PersistentVolumeClaim (if needed)

For stateful applications:

yaml
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: <app-name>-data
  namespace: <namespace>
spec:
  accessModes:
  - ReadWriteOnce
  storageClassName: gp3
  resources:
    requests:
      storage: 10Gi

Mount in Deployment:

yaml
spec:
  template:
    spec:
      containers:
      - name: app
        volumeMounts:
        - name: data
          mountPath: /var/lib/app
      volumes:
      - name: data
        persistentVolumeClaim:
          claimName: <app-name>-data

Storage considerations:

  • Choose appropriate StorageClass for performance needs
  • Use ReadWriteOnce for single-pod access
  • Use ReadWriteMany for multi-pod shared storage
  • Consider backup strategies
  • Set appropriate retention policies

7. Apply Security Best Practices

Add security context to Deployment:

yaml
spec:
  template:
    spec:
      securityContext:
        runAsNonRoot: true
        runAsUser: 1000
        fsGroup: 1000
        seccompProfile:
          type: RuntimeDefault
      containers:
      - name: app
        securityContext:
          allowPrivilegeEscalation: false
          readOnlyRootFilesystem: true
          capabilities:
            drop:
            - ALL

Security checklist:

  • Run as non-root user
  • Drop all capabilities
  • Use read-only root filesystem
  • Disable privilege escalation
  • Set seccomp profile
  • Use Pod Security Standards

8. Add Labels and Annotations

Standard labels (recommended):

yaml
metadata:
  labels:
    app.kubernetes.io/name: <app-name>
    app.kubernetes.io/instance: <instance-name>
    app.kubernetes.io/version: "1.0.0"
    app.kubernetes.io/component: backend
    app.kubernetes.io/part-of: <system-name>
    app.kubernetes.io/managed-by: kubectl

Useful annotations:

yaml
metadata:
  annotations:
    description: "Application description"
    contact: "team@example.com"
    prometheus.io/scrape: "true"
    prometheus.io/port: "9090"
    prometheus.io/path: "/metrics"

9. Organize Multi-Resource Manifests

File organization options:

Option 1: Single file with --- separator

yaml
# app-name.yaml
---
apiVersion: v1
kind: ConfigMap
...
---
apiVersion: v1
kind: Secret
...
---
apiVersion: apps/v1
kind: Deployment
...
---
apiVersion: v1
kind: Service
...

Option 2: Separate files

manifests/
├── configmap.yaml
├── secret.yaml
├── deployment.yaml
├── service.yaml
└── pvc.yaml

Option 3: Kustomize structure

base/
├── kustomization.yaml
├── deployment.yaml
├── service.yaml
└── configmap.yaml
overlays/
├── dev/
│   └── kustomization.yaml
└── prod/
    └── kustomization.yaml

10. Validate and Test

Validation steps:

bash
# Dry-run validation
kubectl apply -f manifest.yaml --dry-run=client

# Server-side validation
kubectl apply -f manifest.yaml --dry-run=server

# Validate with kubeval
kubeval manifest.yaml

# Validate with kube-score
kube-score score manifest.yaml

# Check with kube-linter
kube-linter lint manifest.yaml

Testing checklist:

  • Manifest passes dry-run validation
  • All required fields are present
  • Resource limits are reasonable
  • Health checks are configured
  • Security context is set
  • Labels follow conventions
  • Namespace exists or is created

Common Patterns

Pattern 1: Simple Stateless Web Application

Use case: Standard web API or microservice

Components needed:

  • Deployment (3 replicas for HA)
  • ClusterIP Service
  • ConfigMap for configuration
  • Secret for API keys
  • HorizontalPodAutoscaler (optional)

Reference: See assets/deployment-template.yaml

Pattern 2: Stateful Database Application

Use case: Database or persistent storage application

Components needed:

  • StatefulSet (not Deployment)
  • Headless Service
  • PersistentVolumeClaim template
  • ConfigMap for DB configuration
  • Secret for credentials

Pattern 3: Background Job or Cron

Use case: Scheduled tasks or batch processing

Components needed:

  • CronJob or Job
  • ConfigMap for job parameters
  • Secret for credentials
  • ServiceAccount with RBAC

Pattern 4: Multi-Container Pod

Use case: Application with sidecar containers

Components needed:

  • Deployment with multiple containers
  • Shared volumes between containers
  • Init containers for setup
  • Service (if needed)

Templates

The following templates are available in the assets/ directory:

  • deployment-template.yaml - Standard deployment with best practices
  • service-template.yaml - Service configurations (ClusterIP, LoadBalancer, NodePort)
  • configmap-template.yaml - ConfigMap examples with different data types
  • secret-template.yaml - Secret examples (to be generated, not committed)
  • pvc-template.yaml - PersistentVolumeClaim templates

Reference Documentation

  • references/deployment-spec.md - Detailed Deployment specification
  • references/service-spec.md - Service types and networking details

Best Practices Summary

  1. Always set resource requests and limits - Prevents resource starvation
  2. Implement health checks - Ensures Kubernetes can manage your application
  3. Use specific image tags - Avoid unpredictable deployments
  4. Apply security contexts - Run as non-root, drop capabilities
  5. Use ConfigMaps and Secrets - Separate config from code
  6. Label everything - Enables filtering and organization
  7. Follow naming conventions - Use standard Kubernetes labels
  8. Validate before applying - Use dry-run and validation tools
  9. Version your manifests - Keep in Git with version control
  10. Document with annotations - Add context for other developers

Troubleshooting

Pods not starting:

  • Check image pull errors: kubectl describe pod <pod-name>
  • Verify resource availability: kubectl get nodes
  • Check events: kubectl get events --sort-by='.lastTimestamp'

Service not accessible:

  • Verify selector matches pod labels: kubectl get endpoints <service-name>
  • Check service type and port configuration
  • Test from within cluster: kubectl run debug --rm -it --image=busybox -- sh

ConfigMap/Secret not loading:

  • Verify names match in Deployment
  • Check namespace
  • Ensure resources exist: kubectl get configmap,secret

Next Steps

After creating manifests:

  1. Store in Git repository
  2. Set up CI/CD pipeline for deployment
  3. Consider using Helm or Kustomize for templating
  4. Implement GitOps with ArgoCD or Flux
  5. Add monitoring and observability

Related Skills

  • helm-chart-scaffolding - For templating and packaging
  • gitops-workflow - For automated deployments
  • k8s-security-policies - For advanced security configurations

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 K8s Manifest Generator AI skill do?

Create production-ready Kubernetes manifests for Deployments, Services, ConfigMaps, and Secrets following best practices and security standards. Use when generating Kubernetes YAML manifests, creating K8s resources, or implementing production-grade Kubernetes configurations.

Why use K8s Manifest Generator on TypingMind?

Because you install it once and use it with any model. K8s Manifest Generator 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 K8s Manifest Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HermeticOrmus/LibreUIUX-Claude-Code/tree/main/plugins/kubernetes-operations/skills/k8s-manifest-generator. 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 K8s Manifest Generator?

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 K8s Manifest Generator?

As many as you like. As long as a model supports skills, you can use K8s Manifest Generator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the K8s Manifest Generator AI skill free?

Yes. It is published on GitHub by HermeticOrmus 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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