feat(goldset): AI second-opinion per item (QA aid) — compare vs human tag

The chair wanted an independent recommendation beside each tag, to reconsider
his own judgments. Adds a NON-ground-truth AI second-opinion:

- schema: halacha_goldset.ai_is_holding / ai_correct_type / ai_rationale /
  ai_generated_at (additive).
- db.goldset_set_ai_recommendation + goldset_list now returns the ai_* fields.
- scripts/goldset_ai_recommend.py — local claude_session judges is_holding +
  type + a one-line rationale per item, INDEPENDENTLY (own legal rubric).
  Independent of the rule-based validators #81.8 measures → no circularity.
  Never auto-applied; QA aid only.
- web-ui: each card shows "🤖 המלצת AI: הלכה/לא · type" + rationale and an
  agreement/disagreement chip vs the human tag (amber on disagree); a
  "⚠ אי-הסכמות AI (N)" filter to review only the conflicts.

Methodology note kept explicit: the human stays the ground truth; the AI is a
prompt to reconsider, not to copy.

Verified: tsc --noEmit 0; generator stores recs and flags disagreements with
existing human tags.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

scripts/goldset_ai_recommend.py

@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+"""Generate the AI second-opinion for gold-set items (#81.7 QA aid).
+
+For each gold-set halacha, an INDEPENDENT local-LLM (claude_session, zero cost)
+judges: is it a real generalizable holding, what is its correct rule_type, and a
+one-line rationale. Stored in halacha_goldset.ai_* and shown beside the human
+tag so the chair can spot disagreements and reconsider.
+
+This is a QA aid, NOT ground truth and NOT auto-applied. It is also independent
+of the rule-based validators that #81.8 measures, so it doesn't bias that score.
+
+Must run locally (claude_session needs the local CLI — not the container):
+
+    cd ~/legal-ai/mcp-server
+    .venv/bin/python ../scripts/goldset_ai_recommend.py            # missing only
+    .venv/bin/python ../scripts/goldset_ai_recommend.py --force    # regenerate all
+    .venv/bin/python ../scripts/goldset_ai_recommend.py --limit 10 # smoke
+"""
+from __future__ import annotations
+
+import argparse
+import asyncio
+import sys
+from uuid import UUID
+
+from legal_mcp.services import claude_session, db
+
+VALID_TYPES = {"binding", "interpretive", "obiter", "application", "procedural", "persuasive"}
+
+SYSTEM = (
+    "אתה בוחן-איכות משפטי המסווג 'הלכות' שחולצו מהחלטות ועדת-ערר ומפסקי-דין. "
+    "לכל פריט הכרע שתי שאלות, באופן עצמאי ולפי המהות:\n"
+    "1) is_holding — האם זו הלכה אמיתית בת-הכללה ובת-הסתמכות (true), או שזו יישום "
+    "תלוי-עובדות / אמרת-אגב / ציטוט-עובדה ולא כלל בר-הכללה (false).\n"
+    "2) type — הסוג הנכון: 'binding' (עיקרון הכרחי להכרעה), 'interpretive' (פרשנות "
+    "חוק/מונח/תכנית), 'procedural' (סדר-דין: מועדים/סמכות/מיצוי/נטל), 'persuasive' "
+    "(אסמכתה לא-מחייבת), 'application' (החלה על עובדות התיק — לרוב לא-הלכה), "
+    "'obiter' (אמרת-אגב שלא הוכרעה — לא-הלכה).\n"
+    "עקביות: is_holding=true → binding/interpretive/procedural/persuasive; "
+    "is_holding=false → application/obiter.\n"
+    'החזר JSON בלבד: {"is_holding": true/false, "type": "<אחד מהשישה>", '
+    '"rationale": "<משפט אחד קצר בעברית>"}. ללא markdown.'
+)
+
+
+def _prompt(item: dict) -> str:
+    src = "פסק-דין" if item.get("source_type") == "court_ruling" else "החלטת ועדת-ערר"
+    return (
+        f"מקור: {src} ({item.get('case_number') or ''}).\n"
+        f"סוג שהמכונה נתנה: {item.get('rule_type')}.\n\n"
+        f"ניסוח הכלל:\n{item.get('rule_statement') or ''}\n\n"
+        f"ציטוט תומך:\n{item.get('supporting_quote') or ''}"
+    )
+
+
+async def main(args: argparse.Namespace) -> int:
+    items = await db.goldset_list(args.batch)
+    todo = [it for it in items if args.force or not it.get("ai_generated_at")]
+    if args.limit:
+        todo = todo[: args.limit]
+    print(f"gold-set {args.batch}: {len(items)} items, {len(todo)} to recommend", flush=True)
+
+    ok, fail, disagree = 0, 0, 0
+    for i, it in enumerate(todo, 1):
+        try:
+            v = await claude_session.query_json(_prompt(it), system=SYSTEM, effort="low")
+        except Exception as e:  # noqa: BLE001
+            fail += 1
+            print(f"[{i}/{len(todo)}] {it['case_number']}: FAIL {e}", flush=True)
+            continue
+        if not isinstance(v, dict):
+            fail += 1
+            continue
+        ai_hold = bool(v.get("is_holding"))
+        ai_type = str(v.get("type") or "").strip()
+        if ai_type not in VALID_TYPES:
+            ai_type = ""
+        await db.goldset_set_ai_recommendation(
+            UUID(str(it["id"])), ai_is_holding=ai_hold, ai_correct_type=ai_type,
+            ai_rationale=str(v.get("rationale") or "")[:300],
+        )
+        ok += 1
+        # note disagreements with the human tag (if tagged)
+        flag = ""
+        if it.get("is_holding") is not None and it["is_holding"] != ai_hold:
+            disagree += 1
+            flag = "  ⚠ DISAGREE is_holding"
+        print(f"[{i}/{len(todo)}] {it['case_number']}: ai={ai_hold}/{ai_type}{flag}", flush=True)
+
+    print(f"\nDONE — {ok} stored, {fail} failed, {disagree} disagree with existing human tag",
+          flush=True)
+    return 0
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--batch", default="default")
+    ap.add_argument("--force", action="store_true", help="regenerate even if present")
+    ap.add_argument("--limit", type=int, default=None)
+    sys.exit(asyncio.run(main(ap.parse_args())))