{"id":7371,"plugin_id":"plugin_asdk_app_6a5fc156fad88191a3977b60131d7391","kind":"skill","collection_source":null,"comparison_source":null,"observed_at":"2026-09-30T22:50:27.105Z","digest":"6634014ad127b478cd9c42f03a807892b2dd4caeb61cc8aab1bb5ed351ce1010","against":null,"payload":{"name":"tamarind-mcp-inverse-folding","description":"Design sequences for a fixed protein backbone or run protein language models for embeddings, mutation scoring, likelihoods, and sequence generation with Tamarind Bio through MCP. Use for ProteinMPNN-, LigandMPNN-, ESM-IF-, or PLM-style tasks. Not for de novo backbone generation, antibody CDR design, or ordinary folding.","included_files":[],"skill_md_contents":"---\nname: tamarind-mcp-inverse-folding\ndescription: Design sequences for a fixed protein backbone or run protein language models for embeddings, mutation scoring, likelihoods, and sequence generation with Tamarind Bio through MCP. Use for ProteinMPNN-, LigandMPNN-, ESM-IF-, or PLM-style tasks. Not for de novo backbone generation, antibody CDR design, or ordinary folding.\n---\n\n# Run inverse folding and protein language models\n\nSeparate two task families: inverse folding starts from a 3D backbone and produces sequences; protein language models start from sequence and produce embeddings, scores, likelihoods, or variants.\n\n## Discover and validate\n\nCall `listTags` when needed, then query `getAvailableTools` for inverse folding, protein language models, or embeddings. Inspect the chosen tool with `getJobSchema`.\n\nFor inverse folding, confirm designed chains/residues, fixed context, ligand context, sequence count, temperature/noise, excluded residues, and model variant. For PLMs, confirm task, model size, sequence limit, output format, and scan or generation settings.\n\nUpload a backbone with `uploadFile` or pass an exact accepted upstream `s3Path`. Call `validateJob`, require no mutation warning, then call `estimateTime`. Surface model size, sequence count, temperature, batch size, and estimated spend.\n\n## Execute and verify\n\nUse `tamarind-mcp-submit-and-poll`. For multiple inputs or downstream folds, use `tamarind-mcp-batch` rather than looping over `submitJob`.\n\nInverse-folded sequences require structural validation. Refold a diverse bounded subset with `tamarind-mcp-structure-prediction`, then inspect backbone recovery and confidence/interface metrics. Pass designed sequences to the downstream schema's sequence field; do not place them in a template or structure-file field.\n"},"changes":[],"summary":"First saved snapshot. No earlier version is available for comparison.","summary_kind":"deterministic","summary_metadata":{}}