{
  "id": "python-ml-ai-production",
  "name": "python-ml-ai-production",
  "version": "0.1.0",
  "type": "skill",
  "provider": "python",
  "harnesses": [
    "codex",
    "claude-code",
    "cursor",
    "gemini",
    "kiro",
    "other"
  ],
  "summary": "Static review of Python ML/AI production correctness — training-serving skew, feature/data leakage, artifact serialization safety, reproducibility, drift signals, batch-vs-online consistency, and model/prompt config provenance. Reads training/serving source, config, and eval artifacts only; never trains, loads, or serves a model.",
  "source_type": "original",
  "official_docs": [
    "https://scikit-learn.org/stable/model_persistence.html",
    "https://scikit-learn.org/stable/common_pitfalls.html",
    "https://docs.python.org/3/library/pickle.html",
    "https://numpy.org/doc/stable/reference/random/generator.html"
  ],
  "security_notes": "Static review only — reads training and serving source, feature-transformation code, model-persistence configuration, and evaluation artifacts to assess ML production correctness; never trains a model, loads a model artifact, serves inference, or connects to a live system. A claim about actual offline/online metric values or drift is flagged as needing observation against a real evaluation run. Never requests training/customer data or model-registry credentials.",
  "last_verified": "2026-07-26",
  "path": "skills/python/python-ml-ai-production",
  "author": "github: VincentChuWaiChow"
}