Agent skill · software engineering · jeremylongshore
coreweave-sdk-patterns
Production-ready patterns for CoreWeave GPU workload management with
Why this skill is useful
Adds executable Python scripts and deployment templates for managing GPU workloads on CoreWeave that are not available in public documentation.
What it needs
Requires kubectl, python installed locally. Requires coreweave account access. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
CoreWeave SDK Patterns Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc. Overview CoreWeave is Kubernetes-native -- use kubectl, Kubernetes Python client, or Helm for programmatic management. These patterns cover GPU-aware deployment templates, inference client wrappers, and node affinity configurations. Instructions GPU Affinity Helper Inference Client Wrapper Deployment Template Generator Error Handling Error Cause Solution ------- ------- ---------- GPU class not found Typo in node label Use exact values from gpu.nvidia.com/class OOM on inference Model too large for GPU Use larger GPU or quantized model Connection refused Service not ready Check pod readiness probe Prerequisites A namespace-scoped Kubernetes credential and endpoint from the approved environment. An image, GPU class, and resource budget reviewed for the target workload. A secret-manager reference for private registry or model access; never pass tokens into generated YAML or application logs. Output A reusable client or deployment manifest pattern with explicit GPU resources and affinity constraints. A readiness-aware request path that distinguishes unavailable services from a valid application response. A generated manifest that can be reviewed, versioned, and rolled back before apply. Examples Generate a manifest, inspect it for the expected namespace and GPU resource limit, then apply it first in staging: If validation or rollout fails, retain the reviewed manifest and redacted events; do not broaden the client credential or bypass the admission policy. Resources CoreWeave GPU Instances Kubernetes Python Client Next Steps Apply patterns in coreweave-core-workflow-a for KServe inference deployments.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/coreweave-sdk-patterns