Coreweave install auth
Skill ComeOnOliver/skillshub/skills/jeremylongshore/claude-code-plugins-plus-skills/coreweave-install-auth
Configure CoreWeave Kubernetes Service (CKS) access with kubeconfig and API tokens. Use when setting up kubectl access to CoreWeave, configuring CKS clusters, or authenticating with CoreWeave cloud services. Trigger with phrases like "install coreweave", "setup coreweave", "coreweave kubeconfig", "coreweave auth", "connect to coreweave".From its SKILL.md
npx -y skills add ComeOnOliver/skillshub --skill coreweave-install-authAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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CoreWeave Install & Auth
Overview
Set up access to CoreWeave Kubernetes Service (CKS). CKS runs bare-metal Kubernetes with NVIDIA GPUs -- no hypervisor overhead. Access is via standard kubeconfig with CoreWeave-issued credentials.
Prerequisites
- CoreWeave account at https://cloud.coreweave.com
kubectlv1.28+ installed- Kubernetes namespace provisioned by CoreWeave
Instructions
Step 1: Download Kubeconfig
- Log in to https://cloud.coreweave.com
- Navigate to API Access > Kubeconfig
- Download the kubeconfig file
# Save kubeconfig
mkdir -p ~/.kube
cp ~/Downloads/coreweave-kubeconfig.yaml ~/.kube/coreweave
# Set as active context
export KUBECONFIG=~/.kube/coreweave
# Verify connection
kubectl get nodes
kubectl get namespaces
Step 2: Configure API Token
# CoreWeave API token for programmatic access
export COREWEAVE_API_TOKEN="your-api-token"
# Store securely
echo "COREWEAVE_API_TOKEN=${COREWEAVE_API_TOKEN}" >> .env
echo "KUBECONFIG=~/.kube/coreweave" >> .env
Step 3: Verify GPU Access
# List available GPU nodes
kubectl get nodes -l gpu.nvidia.com/class -o custom-columns=\
NAME:.metadata.name,GPU:.metadata.labels.gpu\.nvidia\.com/class,\
STATUS:.status.conditions[-1].type
# Check GPU allocatable resources
kubectl describe nodes | grep -A5 "Allocatable:" | grep nvidia
Step 4: Test with a Simple GPU Pod
# test-gpu.yaml
apiVersion: v1
kind: Pod
metadata:
name: gpu-test
spec:
restartPolicy: Never
containers:
- name: cuda-test
image: nvidia/cuda:12.2.0-base-ubuntu22.04
command: ["nvidia-smi"]
resources:
limits:
nvidia.com/gpu: 1
affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: gpu.nvidia.com/class
operator: In
values: ["A100_PCIE_80GB"]
kubectl apply -f test-gpu.yaml
kubectl logs gpu-test # Should show nvidia-smi output
kubectl delete pod gpu-test
Error Handling
| Error | Cause | Solution |
|---|---|---|
Unable to connect to the server | Wrong kubeconfig | Verify KUBECONFIG path |
Forbidden | Missing namespace permissions | Contact CoreWeave support |
| No GPU nodes found | Wrong node labels | Check gpu.nvidia.com/class labels |
| Pod stuck Pending | GPU capacity exhausted | Try different GPU type or region |
Resources
Next Steps
Proceed to coreweave-hello-world to deploy your first inference service.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.