{"id":77018,"plugin_id":"plugins_6ab41ed5b8c4819199eb5bbf91ce25f5","kind":"skill","collection_source":"plugin_package","comparison_source":null,"observed_at":"2026-11-03T00:12:19.142Z","digest":"9a1182af1b8267d542779dee6714c18473a836110e16d38f357f45271501c3a9","against":null,"payload":{"description":"Harden ML training, feature engineering, inference and evaluation code for reproducibility and safe production use.","included_files":[],"name":"productionize-ml-code","skill_md_contents":"---\nname: productionize-ml-code\ndescription: Harden ML training, feature engineering, inference and evaluation code for reproducibility and safe production use.\n---\n\n# Productionize Ml Code\n\nUse this skill only for its stated purpose. Follow the shared source-of-truth, permission and evidence rules in `../../references/workflow-contract.md`. Read `../../references/ml-production.md` for detailed method when necessary.\n\n## Procedure\n1. Inspect training/inference contracts, feature provenance, data splits and leakage risks.\n2. Separate preprocessing, training, evaluation, inference and artifact versions.\n3. Add schema checks, reproducible configurations, slice evaluations and meaningful baseline tests.\n4. Check train/serve skew, serialization, resource needs and fallback behavior.\n5. Report observed metrics only; provide an evaluation plan when data/runtime are missing.\n\n## Handoff rule\nState actual artifacts or grounded code findings and validation status. Never invent file paths, executed tests, tool access, or completed subagent work. Keep output proportionate to the user request.\n"},"changes":[],"summary":"First saved snapshot. No earlier version is available for comparison.","summary_kind":"deterministic","summary_metadata":{}}