docs: session handover — Block F complete, pipeline done, G-pre1 analysis

Session 03-05.05.2026:
- Block F1-F5 complete (DB migration of hardcoded dicts)
- Control Generation: 1,599 controls + 11,522 obligations + 1,147 atomics
- Production sync: 2,625 controls + 11,522 obligations synced
- G-pre1 analysis: 183k objects → 144k after normalize (needs hierarchical clustering)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

control-pipeline/scripts/gpre1_analyze.py

@@ -0,0 +1,37 @@
+#!/usr/bin/env python3
+"""G-pre1: Analyze unique objects and test normalization reduction."""
+from collections import Counter
+from sqlalchemy import create_engine, text
+
+engine = create_engine(
+    "postgresql://breakpilot:breakpilot123@postgres:5432/breakpilot_db",
+    connect_args={"options": "-c search_path=compliance,public"},
+)
+
+with engine.connect() as c:
+    rows = c.execute(text("""
+        SELECT DISTINCT
+            split_part(generation_metadata->>'merge_group_hint', ':', 2) AS obj
+        FROM canonical_controls
+        WHERE generation_metadata->>'merge_group_hint' IS NOT NULL
+          AND generation_metadata->>'merge_group_hint' != ''
+    """)).fetchall()
+
+objects = [r[0] for r in rows if r[0] and r[0].strip()]
+print("Unique raw objects: %d" % len(objects))
+
+from services.control_dedup import normalize_object
+
+norm_counts: Counter = Counter()
+for obj in objects:
+    norm_counts[normalize_object(obj)] += 1
+
+print("After normalize_object(): %d unique" % len(norm_counts))
+print("Reduction: %.1f%%" % ((1 - len(norm_counts) / len(objects)) * 100))
+print()
+print("Top 20 normalized objects:")
+for token, count in norm_counts.most_common(20):
+    print("  %5d  %s" % (count, token))
+print()
+print("Singletons (only 1 raw object): %d" % sum(1 for c in norm_counts.values() if c == 1))
+print("Groups with 2+ members: %d" % sum(1 for c in norm_counts.values() if c >= 2))