M3: implement Data Quality Workbench

Five rule scanners (duplicate customers, missing fields, odometer regression, booking overlap, status conflict) run automatically after seed and via an explicit scan endpoint. Issue defer/reject/merge-customers endpoints with transactional customer merge (booking rewiring, tombstone, audit). Data Quality nav + workbench UI with two-column duplicate comparison and inline (non-native) confirm. Dashboard attention items now link to issues. 35 backend tests passing, ruff clean. Fixed a real false-positive bug in odometer-regression detection found through iteration on seed data, and two TS narrowing errors. Verified end-to-end via browser: S2 merge and S4 overlap scenarios.

backend/app/api/routers/dashboard.py

@@ -86,9 +86,11 @@ def get_dashboard(
                 detail=issue.evidence_json.get("summary", ""),
                 link_type=link_type,
                 link_ref=link_ref,
+                issue_ref=issue.public_ref,
             )
         )
     attention_items.sort(key=lambda item: _SEVERITY_ORDER.get(item.severity, 3))
+    attention_items = attention_items[:8]
 
     today = _today()
     bookings = db.scalars(select(Booking)).all()