nn_auditlisted

# nn_audit — grounded NNs, or synthetic copies of rules? A learned component only earns its place if it's trained on **real data a rule can't capture**. A net trained on `np.random` + hand-coded labels just approximates a deterministic function we already wrote — strictly worse than the rule (adds error + opacity, learns nothing). This audits which micro-NNs are real. ```bash python -m skills.nn_audit.cli # audit skills/botte_nn python -m skills.nn_audit.cli <dir> --json ``` Per model it reports: - **data_source** — `real` | `synthetic` | `unknown`, inferred from the training script's content (distill/corpus/labelled → real; `np.random` → synthetic). - **wired** — does any *production* file (not tests/training/registry) consume it, and which (`usage`)? Tells **synthetic-but-driving-behaviour** (real risk) apart from **synthetic-but-orphan** (dead weight). - **has_provenance** — does the `.json` record `trained_on` / `eval_accuracy` / `data`? - **has_test_guard** — does a test assert a specific real-world output for it? - **verdict** — `grounded` | `synthetic — drives behaviour: ground it` | `synthetic + orphan: delete or wire` | `unknown`; **`at_risk` = synthetic AND wired**. The fix is decided by both axes: a **wired synthetic** net (e.g. `binary_router` in the routing belt) must be **grounded** on real data (distillation / active- learning, like `error_classifier`); an **orphan synthetic** net is dead weight to **delete or wire**. Exposed via [[l