Serverless deploy

Turn any deployable source into a queue-backed Serverless endpoint proven with one real job; the user leaves knowing what exists and what it bills. Steps: read the source, decide engine and hardware, provision, warm, smoke-test one real job, hand back the URL and a curl, state what is billing. Tear down only when the user framed the deploy as a throwaway test or asks for cleanup.

Two decisions are yours, unprompted: the engine family (the right worker image for this modality and model format) and the provisioning path (bake weights in, download-on-start, or a network volume; each costs a different cold start). Size hardware from the model's real metadata, never a guess. Among suitable workers prefer the smallest, most-proven image for a smoke-able deploy (a giant image makes the first job a many-minute cold start; the smoke test proves the journey, not maximal quality), and name the trade-off if the request implies a heavier engine. Creating an endpoint bills: state the GPU class's hourly cost before creating; the smoke job costs money too.

For code-first Python (writing endpoint functions in a project and iterating with the Runpod SDK's rp flash dev loop), this MCP-only bundle has no path. Say so instead of improvising one. This skill deploys an existing source (Hub repo, container image, ComfyUI workflow, Hub release) to an endpoint through the MCP tools.

Tool binding

Read the catalog before creating; create only what the user asked to deploy. A tool named here missing from the session: say so and use the nearest read; never invent a result.