finetune-modellisted

# finetune-model Run the Quickstart Track 2 fine-tune flow for a selectable registry model and dataset, then deploy the trained artifact through Rockie's inference loader. This skill composes existing platform APIs; it must not create a separate training service or run training locally. ## When to invoke - Structured Quickstart Track 2 prompts: ```text Quickstart fine-tune request: track: finetune model: <registry model slug> registry_dataset_id: <registry dataset id> compute_target: rockie_gpu source: quickstart-picker ``` - Equivalent structured lab prompts that explicitly ask to fine-tune a selected registry model on a selected registry dataset. Do not invoke this skill for open-ended model selection, dataset creation, private tenant data ingestion, or custom training research. Route those to the appropriate planning or data workflow first. ## Required v1 inputs - `model`: the registry model slug or id from the picker or user input. - `registry_dataset_id`: the registry dataset id from the picker or user input. Both fields are required for v1. If either is missing, stop and ask for that field. Do not infer a dataset from free text and do not treat a private data reference as selectable. Explicit refusal: `private_data_ref` is not supported in v1. Refuse v1 requests that provide `private_data_ref`, even if the prompt says it has been filtered, until the per-tenant data API from issue #1298 exposes a validated training handle. ## Runtime auth Al