audit-ml-pipeline
Installation
SKILL.md
Audit ML Pipeline
Per-experiment, human-readable, agent-executable narrative of a skore
report — produced by executing a bare-expression # %% file and
reading the digest. Read-only against the skore Project.
Next-step pointers
| Came here from… | After audit, next is… |
|---|---|
iterate-ml-experiment § 4 record-outcome |
→ Read audit digest, fill Status block + JOURNAL row |
| User free-text ("audit 02", "re-audit 04") | → Surface metrics to the user; no further dispatch |
| Re-run of an existing experiment | → Re-execute the existing audit file; surface diff if metrics changed |
The audit is dispatched FIRST in § 4, before any scratch probes.
The digest carries the checks summary and the metrics summary — it
replaces ad-hoc scratch/<ts>_inspect_*.py files for the metric
extraction step.