CoreWeave Skill Pack

24 production-grade Claude Code skills for GPU cloud computing with CoreWeave Kubernetes Service

What Is CoreWeave?

CoreWeave is a specialized GPU cloud platform built for AI/ML workloads. CoreWeave Kubernetes Service (CKS) runs Kubernetes directly on bare-metal GPU nodes -- no hypervisor, no VMs. The platform provides:

• Bare-metal GPU nodes with A100, H100, L40, and GH200 GPUs
• KServe integration for serverless inference with scale-to-zero
• Multi-node training with NVLink and InfiniBand interconnect
• Shared storage (HDD, SSD, NVMe) via Kubernetes PVCs
• DCGM metrics for GPU utilization monitoring out of the box

Access is via standard kubectl with CoreWeave-issued kubeconfig. GPU scheduling uses node affinity with gpu.nvidia.com/class labels. Typical cost savings: 30-50% compared to hyperscaler GPU instances.

This skill pack provides real kubectl commands, YAML manifests, and Python patterns for every stage of CoreWeave deployment.

Installation

/plugin install coreweave-pack@claude-code-plugins-plus

Skills Included

Getting Started (S01-S04)

| Skill | Description | |-------|-------------| | coreweave-install-auth | Kubeconfig setup, API token, GPU access verification | | coreweave-hello-world | First GPU pod: vLLM inference server and batch CUDA job | | coreweave-local-dev-loop | Container build, YAML validation, deploy-watch cycle | | coreweave-sdk-patterns | GPU affinity helpers, inference client, deployment generators |

Core Workflows (S05-S08)

| Skill | Description | |-------|-------------| | coreweave-core-workflow-a | KServe InferenceService with autoscaling and scale-to-zero | | coreweave-core-workflow-b | Distributed GPU training with PyTorch DDP and shared storage | | coreweave-common-errors | Pod Pending, CUDA OOM, NCCL timeout, image pull failures | | coreweave-debug-bundle | Collect node status, GPU allocation, and pod logs for support |

Operations (S09-S12)

| Skill | Description | |-------|-------------| | coreweave-rate-limits | GPU quota management and inference request queuing | | coreweave-security-basics | Secrets for model tokens, network policies, RBAC | | coreweave-prod-checklist | Production readiness for inference and training workloads | | coreweave-upgrade-migration | GPU type migration (A100 to H100), CUDA version upgrades |

Pro Skills (P13-P18)

| Skill | Description | |-------|-------------| | coreweave-ci-integration | GitHub Actions for container build and CKS deployment | | coreweave-deploy-integration | Helm charts and Kustomize overlays for GPU deployments | | coreweave-webhooks-events | Kubernetes event monitoring, GPU metrics, Slack alerts | | coreweave-performance-tuning | GPU selection, vLLM batching, HPA with DCGM metrics | | coreweave-cost-tuning | GPU pricing comparison, scale-to-zero, quantization savings | | coreweave-reference-architecture | Multi-model inference platform architecture |

Flagship Skills (F19-F24)

| Skill | Description | |-------|-------------| | coreweave-multi-env-setup | Dev/staging/prod with different GPU types and quotas | | coreweave-observability | DCGM GPU metrics, Prometheus alerts, Grafana dashboards | | coreweave-incident-runbook | GPU workload failure triage and remediation | | coreweave-data-handling | PVC storage classes, model downloading, dataset management | | coreweave-enterprise-rbac | Namespace isolation, GPU quotas per team, role bindings | | coreweave-migration-deep-dive | Migrate from AWS/GCP GPU instances to CoreWeave CKS |

Quick Start

1. Install the Pack

/plugin install coreweave-pack@claude-code-plugins-plus

2. Configure kubectl

Download your kubeconfig from cloud.coreweave.com and set it up:

export KUBECONFIG=~/.kube/coreweave
kubectl get nodes

3. Deploy Your First GPU Workload

# Run nvidia-smi on an A100
kubectl run gpu-test --image=nvidia/cuda:12.2.0-base-ubuntu22.04 \
  --restart=Never \
  --overrides='{"spec":{"containers":[{"name":"gpu-test","image":"nvidia/cuda:12.2.0-base-ubuntu22.04","command":["nvidia-smi"],"resources":{"limits":{"nvidia.com/gpu":"1"}}}]}}' \
  -- nvidia-smi

kubectl logs gpu-test
kubectl delete pod gpu-test

4. Deploy an Inference Service

Follow coreweave-core-workflow-a to deploy a KServe InferenceService with autoscaling.

Key CoreWeave Links

• CoreWeave Documentation -- CKS and platform docs
• GPU Instance Types -- available GPUs
• CoreWeave Pricing -- per-GPU-hour pricing
• CKS Introduction -- Kubernetes service overview
• CoreWeave Examples -- sample YAML manifests
• CoreWeave Status -- platform status page

License

MIT