Audit the micro-NNs — is each model grounded in REAL data, or a synthetic copy of a hand-coded rule? Scans skills/botte-nn and reports, per model, the training data source (real/synthetic/unknown), whether the model file records provenance (trained_on/eval_accuracy), whether a test guards a real-world output, and a grounded/synthetic verdict. Deterministic, 0 cloud tokens. Use to tell which learned components are real vs placeholder, and which should be grounded or replaced by the rule they imitate.
