Agent skill · software engineering · jeremylongshore
coreweave-core-workflow-a
Deploy KServe InferenceService on CoreWeave with autoscaling and GPU
Why this skill is useful
Provides specific YAML configurations and kubectl commands for deploying KServe on CoreWeave, which are not widely known.
What it needs
Requires kubectl installed locally. About 2k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
CoreWeave Core Workflow: KServe Inference Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc. Overview Deploy production inference services on CoreWeave using KServe InferenceService with GPU scheduling, autoscaling, and scale-to-zero. CKS natively integrates with KServe for serverless GPU inference. Prerequisites Completed coreweave-install-auth setup KServe available on your CKS cluster Model stored in S3, GCS, or HuggingFace Instructions Step 1: Deploy an InferenceService Step 2: Scale-to-Zero Configuration Step 3: Test the Endpoint Error Handling Error Cause Solution ------- ------- ---------- InferenceService not ready GPU not available Check node capacity and affinity Scale-to-zero cold start First request after idle Set minScale: 1 for production Model loading timeout Large model download Pre-cache model in PVC OOMKilled Model too large Use multi-GPU or quantized model Output A namespace-scoped inference service with declared compute, GPU, and secret inputs. A readiness and endpoint smoke-test result suitable for the deployment record. A scale-to-zero configuration limited to appropriate non-production workloads, with a documented production availability decision. Examples Deploy to staging and wait for the service readiness condition before sending a minimal health request: If readiness stalls, inspect events, image pull status, GPU availability, and the secret reference. Do not expose the endpoint publicly or replace a secret reference with a plaintext token as a debugging shortcut. Resources CoreWeave Inference KServe Documentation Next Steps For GPU training workloads, see coreweave-core-workflow-b.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/coreweave-core-workflow-a--fede32