Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/coreweave-install-auth/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill coreweave-install-auth. Copyright stays with the author (MIT).

CoreWeave Install & Auth

Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.

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
• kubectl v1.28+ installed
• Kubernetes namespace provisioned by CoreWeave

Instructions

Step 1: Download Kubeconfig

1. Log in to https://cloud.coreweave.com
2. Navigate to API Access > Kubeconfig
3. 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

Output

• A namespace-scoped Kubernetes context and a verified GPU scheduling result.
• A credential setup that references the approved secret manager and never commits or prints live values.
• A deleted smoke-test Pod after its redacted result is recorded.

Examples

Configure a temporary shell session from your secret manager, then run the smallest GPU smoke test in a sandbox namespace:

export KUBECONFIG="$HOME/.kube/coreweave-sandbox"
kubectl -n sandbox apply -f test-gpu.yaml
kubectl -n sandbox wait --for=condition=Ready pod/gpu-test --timeout=10m
kubectl -n sandbox logs gpu-test
kubectl -n sandbox delete -f test-gpu.yaml

If access is denied or capacity is unavailable, retain the redacted status and contact the namespace owner. Do not put a token in .env, a manifest, shell history, or support tickets.

Resources

• CoreWeave Documentation
• CKS Introduction
• GPU Instance Types

Next Steps

Proceed to coreweave-hello-world to deploy your first inference service.