feat(halacha-triage): quality-gated + prioritized review queue + metrics (#84)

Backend for the halacha approval-queue triage (#84). The keyboard UI, batch
actions and defer/reject (#84.4–6) already shipped; this adds the gating,
prioritization and metrics the queue was missing.

db.list_halachot — two opt-in triage controls:
  * exclude_low_quality (#84.1): drop items carrying ANY quality_flag
    (application / quote_unverified / truncated / non_decision / thin /
    nli_unsupported / near_duplicate) — they belong in a 'needs extraction fix'
    bucket, not the chair's approve queue.
  * order_by_priority (#84.3): active-learning order — negatively-treated
    first, then most-uncertain (lowest confidence), then oldest — instead of
    FIFO, so the highest-value decisions surface first.

halachot_pending (MCP) — now gated + prioritized BY DEFAULT; include_low_quality=
true reveals the needs-fix bucket. The agent review path benefits immediately.

GET /api/halachot — same two params, default OFF (non-breaking; the UI opts in).

metrics.halacha_backlog (#84.7) — splits pending into clean vs flagged, adds
deferred, reviewed_total, approve_ratio, and a pending_by_flag breakdown, so the
backlog distinguishes real review work from extraction noise.

Deferred (documented): #84.2 near-duplicate cluster cards and wiring the UI
fetch to the new params require frontend work + an api:types regen AFTER this
deploys (the new query params aren't in prod's OpenAPI until then) — a clean
follow-up. The backend fully supports both now.

Verified against the live DB (read-only):
- pending 177 → gated-clean 110, 0 flagged items leak into the clean queue.
- priority order surfaces the lowest-confidence items first (0.55, 0.55, ...).
- backlog: pending_clean=110 / pending_flagged=67 / approve_ratio=0.916,
  pending_by_flag={nli_unsupported:59, quote_unverified:3, thin:3, truncated:2}.
- pytest tests/test_halacha_quality.py — 52 passed (no regression).

Invariants: G1 (gate at source — SQL filter, not post-hoc); G2 (no parallel
path — same list_halachot); §6 (flagged items routed to a bucket, never dropped).

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

mcp-server/src/legal_mcp/services/db.py

@@ -3792,7 +3792,19 @@ async def list_halachot(
     practice_area: str | None = None,
     limit: int = 200,
     offset: int = 0,
+    exclude_low_quality: bool = False,
+    order_by_priority: bool = False,
 ) -> list[dict]:
+    """List halachot with optional triage controls (#84).
+
+    exclude_low_quality — drop items carrying ANY quality_flag (application /
+      truncated_quote / quote_unverified / non_decision / thin_restatement /
+      nli_unsupported / near_duplicate). These belong in a 'needs extraction
+      fix' bucket, not the chair's approve queue (#84.1).
+    order_by_priority — replace FIFO with an active-learning order (#84.3):
+      negatively-treated first, then most-uncertain (lowest confidence), then
+      oldest — so the chair sees the highest-value decisions first.
+    """
     pool = await get_pool()
     conditions = []
     params: list = []
@@ -3809,7 +3821,16 @@ async def list_halachot(
         conditions.append(f"${idx} = ANY(h.practice_areas)")
         params.append(practice_area)
         idx += 1
+    if exclude_low_quality:
+        # a clean item has an empty/NULL quality_flags array
+        conditions.append("COALESCE(array_length(h.quality_flags, 1), 0) = 0")
     where_sql = f"WHERE {' AND '.join(conditions)}" if conditions else ""
+    order_sql = (
+        "ORDER BY corroboration_negative DESC, h.confidence ASC NULLS LAST, "
+        "h.created_at ASC"
+        if order_by_priority
+        else "ORDER BY h.case_law_id, h.halacha_index"
+    )
     params.extend([limit, offset])
     sql = f"""
         SELECT h.id, h.case_law_id, h.halacha_index, h.rule_statement,
@@ -3837,7 +3858,7 @@ async def list_halachot(
             GROUP BY halacha_id
         ) cor ON cor.halacha_id = h.id
         {where_sql}
-        ORDER BY h.case_law_id, h.halacha_index
+        {order_sql}
         LIMIT ${idx} OFFSET ${idx + 1}
     """
     rows = await pool.fetch(sql, *params)