Add operator YOLO dataset quality audit

docs/CODEX_EXECUTION_LOG.md

@@ -5643,3 +5643,24 @@ Tested:
 
 Open:
 - The expanded160e50 model is consistently best on the current positive AOI portfolio, but hard-negative/background AOI evidence still prevents blind default promotion.
+# Sprint 146 - Operator YOLO dataset quality audit
+
+## What changed
+
+- Added `scripts/audit_operator_yolo_dataset_quality.py` to inspect generated operator YOLO tile datasets before further model training.
+- Added a focused pytest that creates a synthetic tile summary and YOLO label files, then verifies JSON/Markdown audit output and warning gates.
+- Added the audit script to the readiness syntax gate.
+- Documented the operator audit command in `scripts/README.md`.
+
+## What was tested
+
+- `python -m pytest backend\tests\test_sprint146_operator_yolo_dataset_quality_audit.py -q`
+
+## Known limitations
+
+- The audit is evidence tooling only. It does not modify datasets, train models, fetch external data or change active YOLO configuration.
+- The report flags likely dataset risks, but final promotion decisions must still come from persisted detection QA/QC matrices and hard-negative benchmarks.
+
+## Next recommended pass
+
+- Run the audit against the existing Tower tile datasets and use the results to decide the next training-data expansion pass.

docs/TODO.md

@@ -436,3 +436,9 @@ This file now starts with the current implementation status. Older preparation/b
 - [ ] Add more AOIs after the tile-level baseline so the next local model attempt is not limited to Geel/Mol/Turnhout.
 - [ ] Add negative/background AOIs so the next tile dataset is not all positive tiles.
 - [ ] Improve positive training coverage/label quality before the next higher-capacity model attempt; simply extending the same hardneg r8 run is not enough.
+# Sprint 146 - Operator YOLO dataset quality audit
+
+- [x] Add a dataset/label-quality audit for generated operator YOLO tile datasets.
+- [x] Report sample coverage, validation coverage, repeated hard-negative pressure and YOLO label area integrity.
+- [x] Wire the audit script into the readiness syntax gate.
+- [ ] Use live audit output to decide whether the next model pass needs more positive AOIs, label cleanup or unique hard negatives.