Skill audit report

audit-ml-pipeline Audit report.

Owns the `audit/` folder: one `# %%` (jupytext percent) Python file per experiment, aligned 1:1 with `experiments/NN_<short_name>.py` and `journal/NN_<short_name>.md`, that loads the experiment's skore report **read-only** and uses bare-last-expression cells whose `__repr__` carries the audit's signal. The agent executes the audit file via the bundled in-process runner (`audit-ml-pipeline/scripts/run_cells.py` — IPython `InteractiveShell.run_cell`), which streams a markdown digest of each cell's stdout + last-expression repr to stdout (optionally also to a file). The digest fuels narrative work (the `JOURNAL.md` Status + History update, follow-up questions about a past experiment, cross-experiment comparison). Stops at "audit/NN_*.py is placed, executed, and the digest is available." Never calls `skore.evaluate(...)` or `project.put(...)`. TRIGGER — any of: - `iterate-ml-experiment` § 4 record-outcome — audit is dispatched FIRST (replaces scratch probes for metric extraction). - The us