M4: ingestion management (incremental, GS1 supplement, dedup/conflict, quality, scheduler)

- OFF incremental fetch via search API + persistent watermark (ingest_state, migration 0004)
- GS1 barcode supplement adapter (offline mapping + GS1-style API) filling only gaps with field-level provenance
- Non-GTIN dedup with canonical selection + merge_log; field-level conflict resolution (source trust > recency)
- Quality scoring (0.4 completeness + 0.3 source trust + 0.2 multi-source + 0.1 freshness) wired into load/merge
- Jobs: update_off, dedup, schedule; docs/ingestion-management.md
- 19 new tests (pure + DB-integration), ruff clean

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

ingestion/tests/test_quality_db.py

@@ -0,0 +1,21 @@
+import json
+from pathlib import Path
+
+from opengoods.etl.load import ensure_source, load_record
+from opengoods.etl.quality import compute_quality
+from opengoods.etl.transform import transform
+
+FIXTURE = json.loads((Path(__file__).parent / "fixtures" / "off_product.json").read_text())
+
+
+def test_quality_score_set_on_load(db_conn):
+    source_id = ensure_source(db_conn)
+    rec = transform(FIXTURE)
+    pid = load_record(db_conn, rec, source_id, FIXTURE)
+
+    stored = float(
+        db_conn.execute("SELECT quality_score FROM product WHERE id = %s", (pid,)).fetchone()[0]
+    )
+    assert 0.0 < stored <= 1.0
+    # The persisted value matches a fresh recomputation.
+    assert abs(stored - compute_quality(db_conn, pid)) < 1e-9