Merge pull request 'fix(learning): כרטיס-אוצֵר כן-3-ערוצים + לכידת ממצאי-אוצֵר (source='curator')' (#339) from worktree-curator-learning-surface into main

web/app.py

@@ -1318,31 +1318,77 @@ async def get_style_analyzer_prompt():
 
 @app.get("/api/training/curator/stats")
 async def get_curator_stats():
-    """Cheap aggregate stats over decision_lessons + style_corpus.
+    """Aggregate over the THREE learning channels that feed the writer, for the
+    /training "אוצֵר" tab. Each channel is surfaced honestly with its real
+    backing store and how much of it actually reaches the writer:
 
-    Used by the Curator-Portrait tab to show "10 curator findings across 24
-    decisions". We deliberately keep this server-side and aggregate so the
-    UI can render a single card without fanning out N queries.
+      A. distillation — Claude draft↔final diff → methodology overrides
+         (appeal_type_rules['_global']). Folded items flow to the writer now.
+      B. panel — DeepSeek+Gemini 2/2 vote → decision_lessons
+         (source='panel:deepseek+gemini'). Flow to the writer only once the
+         chair approves (review_status='approved', INV-LRN1/G10).
+      C. curator — the agent's Opus-read qualitative findings → decision_lessons
+         (source='curator'). Same chair gate as the panel.
+
+    Earlier this endpoint counted ONLY source='curator' and reported it as the
+    whole story, so it read 0 while the panel/methodology channels carried all
+    the real learning. The card was measuring a column nothing wrote (the agent
+    posted findings as Paperclip comments, never as decision_lessons — a
+    spec↔impl drift since closed). It now reports all three (INV-IA2/IA5).
     """
     pool = await db.get_pool()
     async with pool.acquire() as conn:
-        total_lessons = await conn.fetchval(
-            "SELECT count(*) FROM decision_lessons WHERE source = 'curator'"
+        total_corpus = await conn.fetchval("SELECT count(*) FROM style_corpus") or 0
+
+        # ── Channel A — distillation → methodology overrides ──────────────
+        meth_rows = await conn.fetch(
+            "SELECT rule_category, coalesce(jsonb_array_length(rule_value), 0) AS n "
+            "FROM appeal_type_rules "
+            "WHERE appeal_type = '_global' "
+            "  AND rule_category IN ('discussion_rules', 'transition_phrases')"
         )
-        decisions_with_findings = await conn.fetchval(
+        meth = {r["rule_category"]: r["n"] for r in meth_rows}
+        discussion_rules = meth.get("discussion_rules", 0)
+        transition_phrases = meth.get("transition_phrases", 0)
+        pairs_folded = await conn.fetchval(
+            "SELECT count(*) FROM draft_final_pairs WHERE status = 'lessons_folded'"
+        ) or 0
+        pairs_total = await conn.fetchval("SELECT count(*) FROM draft_final_pairs") or 0
+
+        # ── Channels B & C — decision_lessons by source × review_status ───
+        ls_rows = await conn.fetch(
+            "SELECT source, review_status, count(*) AS n FROM decision_lessons "
+            "GROUP BY source, review_status"
+        )
+
+        def _channel(src_predicate) -> dict:
+            buckets = {"approved": 0, "proposed": 0, "rejected": 0}
+            for r in ls_rows:
+                if src_predicate(r["source"] or ""):
+                    st = r["review_status"] or "proposed"
+                    buckets[st] = buckets.get(st, 0) + r["n"]
+            buckets["total"] = sum(buckets.values())
+            return buckets
+
+        panel = _channel(lambda s: s.startswith("panel:"))
+        curator = _channel(lambda s: s == "curator")
+
+        panel_decisions = await conn.fetchval(
+            "SELECT count(DISTINCT style_corpus_id) FROM decision_lessons "
+            "WHERE source LIKE 'panel:%'"
+        ) or 0
+        curator_decisions = await conn.fetchval(
             "SELECT count(DISTINCT style_corpus_id) FROM decision_lessons "
             "WHERE source = 'curator'"
+        ) or 0
+        pa_rows = await conn.fetch(
+            "SELECT sc.practice_area, count(*) AS n "
+            "FROM decision_lessons dl JOIN style_corpus sc ON sc.id = dl.style_corpus_id "
+            "WHERE dl.source LIKE 'panel:%' GROUP BY sc.practice_area"
         )
-        total_corpus = await conn.fetchval("SELECT count(*) FROM style_corpus")
-        # LRN-5 (INV-IA5): count the *real* consumer-mapped gate — review_status
-        # 'approved' is what flows to the writer (INV-LRN1, #126). The old count
-        # of applied_to_skill was a KPI over an informative-only flag (LRN-1)
-        # that writes nowhere, so it reported adoption that never happened.
-        approved = await conn.fetchval(
-            "SELECT count(*) FROM decision_lessons "
-            "WHERE source = 'curator' AND review_status = 'approved'"
-        )
-        # Last 10 curator findings — newest first
+        panel_by_practice = {(r["practice_area"] or "—"): r["n"] for r in pa_rows}
+
+        # Recent curator findings (channel C) — newest first
         recent_rows = await conn.fetch(
             """
             SELECT dl.id, dl.lesson_text, dl.category, dl.review_status,
@@ -1355,11 +1401,27 @@ async def get_curator_stats():
             LIMIT 10
             """
         )
+
     return {
-        "total_findings": total_lessons or 0,
-        "decisions_with_findings": decisions_with_findings or 0,
-        "decisions_total": total_corpus or 0,
-        "findings_approved": approved or 0,
+        "decisions_total": total_corpus,
+        "channels": {
+            "distillation": {
+                "discussion_rules": discussion_rules,
+                "transition_phrases": transition_phrases,
+                "items_total": discussion_rules + transition_phrases,
+                "pairs_folded": pairs_folded,
+                "pairs_total": pairs_total,
+            },
+            "panel": {
+                **panel,
+                "decisions": panel_decisions,
+                "by_practice_area": panel_by_practice,
+            },
+            "curator": {
+                **curator,
+                "decisions_reviewed": curator_decisions,
+            },
+        },
         "recent_findings": [
             {
                 "id": str(r["id"]),