{"id":7354,"plugin_id":"plugin_asdk_app_6a5fc156fad88191a3977b60131d7391","kind":"skill","collection_source":null,"comparison_source":null,"observed_at":"2026-09-30T22:50:25.267Z","digest":"7d2fcae22c725fdc50fb5d53d9559aaa8ed621826487b231b0e1fbc20ee7240f","against":null,"payload":{"name":"tamarind-mcp-finetune","description":"Fine-tune a supported Tamarind-hosted model on labeled data and run its matching inference tool through MCP. Use for live account-visible finetune/inference pairs in protein, affinity, enzyme, or small-molecule workflows. Not for stock inference, ordinary batch prediction, or unsupported custom training code.","included_files":[],"skill_md_contents":"---\nname: tamarind-mcp-finetune\ndescription: Fine-tune a supported Tamarind-hosted model on labeled data and run its matching inference tool through MCP. Use for live account-visible finetune/inference pairs in protein, affinity, enzyme, or small-molecule workflows. Not for stock inference, ordinary batch prediction, or unsupported custom training code.\n---\n\n# Fine-tune and run inference through MCP\n\nTreat training and inference as two durable, separately validated jobs with explicit data and evaluation boundaries.\n\n## Confirm a supported pair\n\nCall `getAvailableTools(function=\"finetuning\")` or use a narrow `search`. Inspect both the training and inference tools with `getJobSchema`. Require the live schemas to document the handoff; do not infer a pair from names alone.\n\n## Prepare training\n\nCheck required columns, target units, identifiers, split strategy, class balance, leakage, held-out evaluation, and size limits. Upload the dataset with `uploadFile` and use the returned bare filename.\n\nCall `validateJob` for the training payload, require no mutation warning, and call `estimateTime`. Surface the base model, epochs or steps, dataset size, split, expected runtime, and weighted hours when available. Obtain explicit authorization because training can be materially expensive.\n\n## Train and infer\n\nRun training with `tamarind-mcp-submit-and-poll`. Require an explicit success state. Inspect `listJobFiles` and the training row for the exact trained-model reference required by the inference schema.\n\nBuild and validate the inference payload with that exact reference. Estimate and separately authorize inference when it materially expands scope, then run it through `tamarind-mcp-submit-and-poll` or `tamarind-mcp-batch` for many independent inputs.\n\nEvaluate on held-out data and compare against the stock/base model. Report leakage risks, calibration limits, dataset applicability, and uncertainty before using predictions downstream.\n"},"changes":[],"summary":"First saved snapshot. No earlier version is available for comparison.","summary_kind":"deterministic","summary_metadata":{}}