Add operator YOLO dataset quality audit

scripts/README.md

@@ -328,6 +328,23 @@ negative tiles, and records `yolo_tile_dataset_summary.json` with
 It remains operator tooling only: no provider fetch, no API mutation and no
 automatic model training.
 
+Audit the generated tile dataset before spending another long training run:
+
+```bash
+python scripts/audit_operator_yolo_dataset_quality.py \
+  --summary-path /mnt/user/appdata/geointel/storage/operator-data/yolo-building-tile-hardneg160r8/yolo_tile_dataset_summary.json \
+  --output-dir /mnt/user/appdata/geointel/artifacts/operator-yolo-dataset-audit/hardneg160r8
+```
+
+The audit reads the tile summary and YOLO label files, then writes
+`operator_yolo_dataset_quality_audit.json` and
+`operator_yolo_dataset_quality_audit.md`. It reports positive/background sample
+coverage, train/validation split coverage, repeated hard-negative pressure,
+missing or invalid label rows and normalized box-area signals. Treat
+`needs_attention` as a dataset-design warning, not as a runtime failure: the
+next action is usually more positive AOIs, better validation coverage or more
+unique hard negatives rather than simply extending epochs.
+
 For hard-negative-balanced experiments, repeat only train-split negative tiles
 from samples marked `sample_role=background_candidate`: