Claude-skill-registry k3d

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/k3d" ~/.claude/skills/majiayu000-claude-skill-registry-k3d && rm -rf "$T"

manifest: skills/data/k3d/SKILL.md

k3d - Kubernetes Clusters

Overview

The

k3d

command manages lightweight Kubernetes (k3s) clusters running in Podman containers. Clusters are joined to the bazzite-ai network, enabling DNS-based service discovery with other pods (ollama, jupyter, etc.).

Key Concept: k3d wraps k3s (lightweight Kubernetes) in containers, providing fast cluster creation with full Kubernetes API compatibility. GPU passthrough is supported via NVIDIA device plugin.

Quick Reference

Action	Command	Description
Config	`ujust k3d config [--port=...] [--agents=...]`	Create k3d cluster on bazzite-ai network
Start	`ujust k3d start [--instance=N]`	Start k3d cluster
Stop	`ujust k3d stop [--instance=N]`	Stop k3d cluster
Restart	`ujust k3d restart [--instance=N]`	Restart k3d cluster
Logs	`ujust k3d logs [--instance=N] [--lines=N]`	View k3s server logs
Status	`ujust k3d status [--instance=N]`	Show cluster status and nodes
Shell	`ujust k3d shell [--instance=N] [-- CMD]`	Execute kubectl commands
GPU	`ujust k3d gpu [--instance=N]`	Setup GPU support (NVIDIA device plugin)
Kubeconfig	`ujust k3d kubeconfig [--instance=N]`	Show kubeconfig path
List	`ujust k3d list`	List all k3d clusters
Delete	`ujust k3d delete [--instance=N]`	Remove k3d cluster and cleanup

Parameters

Parameter	Long Flag	Short	Default	Description
action	(positional)	-	required	Action: config, start, stop, etc.
port	`--port`	`-p`	`6443`	Kubernetes API port
bind	`--bind`	`-b`	`127.0.0.1`	Bind address for API server
agents	`--agents`	`-a`	`0`	Number of agent (worker) nodes
instance	`--instance`	`-n`	`1`	Cluster instance number
gpu_type	`--gpu-type`	`-g`	`auto`	GPU type (auto/nvidia/amd/intel/none)
lines	`--lines`	`-l`	`50`	Log lines to show
http_port	`--http-port`	-	`80`	Traefik HTTP ingress port
https_port	`--https-port`	-	`443`	Traefik HTTPS ingress port
k3s_version	`--k3s-version`	-	(latest)	Specific k3s version
disable_traefik	`--disable-traefik`	-	`false`	Disable built-in Traefik ingress
disable_servicelb	`--disable-servicelb`	-	`false`	Disable built-in ServiceLB (Klipper)

Configuration

# Default: Single-node cluster, port 6443, auto-detect GPU
ujust k3d config

# Custom API port (long form)
ujust k3d config --port=6444

# Custom API port (short form)
ujust k3d config -p 6444

# Add 2 agent (worker) nodes
ujust k3d config --agents=2

# With GPU support (NVIDIA)
ujust k3d config --gpu-type=nvidia

# Combine parameters (short form)
ujust k3d config -p 6444 -a 2 -g nvidia

# Network-wide API access
ujust k3d config --bind=0.0.0.0

# Custom ingress ports
ujust k3d config --http-port=8080 --https-port=8443

# Disable built-in components
ujust k3d config --disable-traefik --disable-servicelb

# Specific k3s version
ujust k3d config --k3s-version=v1.28.5+k3s1

Update Existing Configuration

Running

config

when already configured will recreate the cluster with new settings. Data is not preserved - export important resources first.

Lifecycle Commands

Start/Stop/Restart

# Start cluster (instance 1 default)
ujust k3d start

# Start specific instance (long form)
ujust k3d start --instance=1

# Start specific instance (short form)
ujust k3d start -n 1

# Stop cluster
ujust k3d stop

# Restart cluster
ujust k3d restart

View Logs

# View k3s server logs (default 50 lines)
ujust k3d logs

# More lines (long form)
ujust k3d logs --lines=100

# More lines (short form)
ujust k3d logs -l 100

# Specific instance
ujust k3d logs -n 2 -l 100

Status

# Show cluster status and node list
ujust k3d status

# List all clusters
ujust k3d list

Shell Access (kubectl)

Execute kubectl commands directly in the cluster context:

# Interactive shell with kubectl available
ujust k3d shell

# Run kubectl commands (use -- separator)
ujust k3d shell -- kubectl get nodes
ujust k3d shell -- kubectl get pods -A
ujust k3d shell -- kubectl apply -f deployment.yaml

# Specific instance
ujust k3d shell -n 2 -- kubectl get services

GPU Support

k3d supports GPU passthrough for NVIDIA GPUs via the NVIDIA device plugin.

Setup GPU

# Install NVIDIA device plugin in cluster
ujust k3d gpu

# Specific instance
ujust k3d gpu --instance=2

Verify GPU Access

# Check device plugin is running
ujust k3d shell -- kubectl get pods -n kube-system | grep nvidia

# Run GPU test pod
ujust k3d shell -- kubectl run gpu-test --image=nvidia/cuda:12.0-base \
  --restart=Never --rm -it --command -- nvidia-smi

GPU Pod Example

apiVersion: v1
kind: Pod
metadata:
  name: gpu-pod
spec:
  containers:
  - name: cuda-container
    image: nvidia/cuda:12.0-base
    resources:
      limits:
        nvidia.com/gpu: 1

bazzite-ai Network Integration

k3d clusters join the

bazzite-ai

network, enabling service discovery:

# From inside k8s pods, access other bazzite-ai services:
curl http://ollama:11434/api/tags
curl http://jupyter:8888/

Available DNS names:

```
ollama:11434
```
- Ollama API
```
jupyter:8888
```
- JupyterLab
```
openwebui:3000
```
- Open WebUI
```
comfyui:8188
```
- ComfyUI

Multi-Instance

Run multiple k3d clusters simultaneously:

# Create first cluster
ujust k3d config -n 1 -p 6443

# Create second cluster with different port
ujust k3d config -n 2 -p 6444

# List all clusters
ujust k3d list

# Interact with specific cluster
ujust k3d shell -n 2 -- kubectl get nodes

Instance	API Port	Cluster Name
1	6443	k3d-1
2	6444	k3d-2
N	6442+N	k3d-N

Built-in Components

k3d includes these components by default:

Component	Purpose	Disable Flag
Traefik	Ingress controller	`--disable-traefik`
ServiceLB (Klipper)	LoadBalancer support	`--disable-servicelb`
Local Path Provisioner	Persistent volumes	(always enabled)
CoreDNS	Cluster DNS	(always enabled)

Common Workflows

Initial Setup

# 1. Create cluster
ujust k3d config

# 2. Start cluster
ujust k3d start

# 3. Verify nodes
ujust k3d shell -- kubectl get nodes

# 4. Deploy application
ujust k3d shell -- kubectl apply -f my-app.yaml

GPU ML Workloads

# 1. Create cluster with GPU
ujust k3d config --gpu-type=nvidia

# 2. Start and setup GPU support
ujust k3d start
ujust k3d gpu

# 3. Deploy GPU workload
ujust k3d shell -- kubectl apply -f gpu-deployment.yaml

Development Cluster

# Fast local cluster for testing
ujust k3d config -a 1  # 1 server + 1 agent

# Deploy test app
ujust k3d shell -- kubectl create deployment nginx --image=nginx
ujust k3d shell -- kubectl expose deployment nginx --port=80 --type=LoadBalancer

# Access via traefik
curl http://localhost

Troubleshooting

Cluster Won't Start

Check:

ujust k3d status
ujust k3d logs --lines=100

Common causes:

Port already in use
Podman not running
Insufficient resources

Fix:

# Use different port
ujust k3d delete
ujust k3d config --port=6444
ujust k3d start

GPU Not Available in Pods

Symptom: Pods requesting GPU resources stay Pending

Check:

ujust k3d shell -- kubectl describe pod <pod-name>
ujust k3d shell -- kubectl get pods -n kube-system | grep nvidia

Fix:

# Reinstall device plugin
ujust k3d gpu

# Or recreate cluster with GPU
ujust k3d delete
ujust k3d config --gpu-type=nvidia
ujust k3d start
ujust k3d gpu

Service Discovery Fails

Symptom: Can't reach ollama:11434 from k8s pods

Check:

# Verify network membership
podman network inspect bazzite-ai

Fix:

# Recreate cluster (joins network)
ujust k3d delete
ujust k3d config
ujust k3d start

kubectl Connection Refused

Symptom: kubectl commands fail with "connection refused"

Check:

ujust k3d status

Fix:

# Restart cluster
ujust k3d restart

Configuration Files

File	Purpose	Location
kubeconfig	kubectl config	`~/.kube/config` (merged)
k3d config	Cluster settings	Managed by k3d

Cross-References

Related Skills:
```
portainer
```
(container UI),
```
ollama
```
(LLM inference)
k3d Docs: https://k3d.io/
k3s Docs: https://docs.k3s.io/
GPU Setup:
```
ujust config gpu setup
```

When to Use This Skill

Use when the user asks about:

"kubernetes", "k8s", "k3d", "k3s"
"local kubernetes", "dev cluster"
"kubectl", "deploy to kubernetes"
"kubernetes GPU", "GPU pods"
"start k3d", "configure k3d", "k3d not working"