deploy-model

name	deploy-model
description	Narrow quickstart wrapper for pre-baked Deploy-card requests. Triggers on server-validated structured quickstart deploy prompts such as "Quickstart deploy request: track: deploy model: <id> source: quickstart-picker quickstart_intent_id: <id>" and explicit "deploy this featured model" requests from Rockie's quickstart picker when validation metadata is present.

deploy-model

Deploy the featured quickstart model through Rockie's existing inference loader and return the API details in the lab chat. This skill is a thin wrapper around the inference-loader contract documented by skills/inference-engineer/SKILL.md; it exists so the quickstart card can skip the general-purpose intake and hard-confirm flow.

When to invoke

• Structured quickstart prompts:

  Quickstart deploy request:
  track: deploy
  model: <featured model id>
  compute_target: rockie_gpu
  source: quickstart-picker
  quickstart_intent_id: <platform-supplied id>
  

• Explicit quickstart wording such as "deploy this featured model" or "start the Deploy card model" when Rockie runtime metadata confirms it came from the quickstart picker.

Do not invoke this skill for open-ended requests like "help me choose a model" or ad hoc production-serving design. Use inference-engineer for those.

Quickstart contract

• Accept the model id from the prompt as the source of truth only after the request carries a server-validated quickstart intent. User-authored prompt text alone is not sufficient. Do not ask the user to pick a model on the featured-card path after the validation marker is present.
• Treat the card tap plus structured prompt plus validation metadata as the user's pre-baked deploy intent. Do not ask for the inference-engineer hard confirmation and do not require the cost-confirm.md approval template only on this validated quickstart path.
• If the validation marker is absent or malformed, stop this wrapper and route to inference-engineer with its normal hard confirmation and cost-confirm path.
• Do not perform launcher-style credit, balance, spend-cap, membership, or cost-confirmation checks. If the Rockie platform returns HTTP 402, surface the platform 402 response through the shared redaction/sanitization boundary and let the ledger/refill path own recovery.
• Use compute_target: rockie_gpu from the structured prompt. Do not ask the user to choose a provider, direct cluster, GPU supplier, or raw provider region.

Runtime auth

All Rockie control-plane calls require both headers:

-H "X-Tenant-Token: $ROCKIELAB_TENANT_TOKEN" \
-H "X-Tenant-Id: $ROCKIELAB_TENANT_ID"

ROCKIELAB_TENANT_TOKEN is the control-plane auth token. ROCKIELAB_TENANT_ID is tenant scope/display identity only. Never use ROCKIELAB_TENANT_ID as X-Tenant-Token.

Platform API path only

Provision and inspect the load only through Rockie platform inference-loader APIs:

• POST $ROCKIELAB_API_URL/api/inference/loads
• GET $ROCKIELAB_API_URL/api/inference/loads/{id}
• GET $ROCKIELAB_API_URL/api/inference/loads/{id}/logs
• GET $ROCKIELAB_API_URL/api/inference/loads/{id}/endpoint
• GET $ROCKIELAB_API_URL/api/inference/loads/{id}/endpoint/wait when the platform exposes the wait helper

The POST /api/inference/loads request must identify this wrapper in metadata when dashboard metadata is available:

{
  "url": "<model id from quickstart prompt>",
  "compute_target": "rockie_gpu",
  "keep_running": true,
  "dashboard": {
    "notebook_id": "<current lab id when available>",
    "origin_skill": "deploy-model",
    "run_name": "Quickstart deploy: <model id>",
    "monitoring_profile_id": "inference.batch_baseline.v1"
  }
}

Do not call supplier-specific APIs or CLIs. Do not pass provider credentials. Do not bypass the Rockie inference loader with a direct hosting provider, direct SSH deploy, or hand-run serving process.

Local execution is forbidden

This skill must never run model-serving work locally. Do not import or run torch, triton, CUDA libraries, vLLM, TEI, BentoML, SGLang, LMDeploy, or any other local serving stack. Do not download model weights. Do not start a subprocess server, local GPU runtime, Docker serving container, or direct CUDA job. All GPU/model-serving work must go through the Rockie platform APIs above.

Procedure

1. Parse the structured prompt. Require track: deploy, model: <id>, source: quickstart-picker or equivalent featured-card wording, and a server-validated quickstart intent such as quickstart_intent_id or quickstart_signature supplied by Rockie runtime/platform metadata. User-authored text alone cannot satisfy this requirement.
2. Echo the model id and say that provisioning is going through the Rockie inference loader.
3. POST /api/inference/loads with the auth headers above. Use the model id from the prompt and origin_skill: "deploy-model" when metadata is accepted.
4. Size it together with the user when the model needs more than one GPU. If the loader returns 400 gpu_count_required or gpu_count_too_low, the model does not fit a single GPU. The error carries the facts — the model's VRAM need, the GPU's VRAM, and the minimum GPU count. Do NOT report this as a broken card and stop. Instead: pick gpu_count (at least the stated minimum) and a spend_cap_cents that covers model download + a reasonable runtime at that count, state the plan to the user in one line (e.g. "this model needs ~226 GB → 2× the GPU, ~$X/hr; budget $Y — deploying"), and resubmit the same POST /api/inference/loads with gpu_count and spend_cap_cents set. gpu_count and spend_cap_cents are explicit, user-facing sizing decisions — like a budget — not platform guesses; never silently default them. The tenant balance still bounds total spend.
5. Poll GET /api/inference/loads/{id} or GET /api/inference/loads/{id}/logs for queued/provisioning/loading/loaded/failed status. Narrate meaningful state changes in the lab.
6. On HTTP 402, stop and surface the platform response through the shared redaction/sanitization boundary. Do not replace it with a custom balance check or confirmation flow.
7. When loaded, call GET /api/inference/loads/{id}/endpoint or /endpoint/wait until the platform returns the endpoint details.
8. Return the endpoint URL, bearer-token handling, and a copy-paste curl.

Required final lab output

The final response must include all of:

• Endpoint URL from the loader endpoint response. Use this value directly as ENDPOINT_URL; it already includes the modality path.

• Bearer-token handling. If the loader returns a bearer token, show it as the value to use in Authorization: Bearer ...; if the platform withholds or rotates it, explain where the lab/runtime obtains the token without inventing one.

• A copy-paste curl using the endpoint URL and bearer auth header, for example:

  curl "$ENDPOINT_URL" \
    -H "Authorization: Bearer $ROCKIE_INFERENCE_BEARER_TOKEN" \
    -H "Content-Type: application/json" \
    -d '{"model":"<model id>","messages":[{"role":"user","content":"hello"}]}'
  

Also include the load_id and the status command:

/inference-engineer status <load_id>