Order plan never adds to hurdle-gated Keeps; sub-1% rule as final pass; overvalued candidates need a strong committee; narrative at medium effort

src/orchestrator.py

@@ -521,6 +521,11 @@ def run_analysis(rows: list[dict], emit, context: dict | None = None, budget_s:
                 if isinstance(b, (int, float)) and b > 1.15 and not c.get("instrument"):
                     c["fit"]["cautions"].append(f"beta {b:.2f} — too volatile for a conservative / short-horizon profile")
                     c["fit"]["score"] = round(c["fit"]["score"] - 0.6, 3)
+        for c in scored:   # valuation: a name far above its DCF value needs a strong committee to be an add
+            up = c.get("upside")
+            if isinstance(up, (int, float)) and up < -0.25 and not c.get("instrument"):
+                c["fit"]["cautions"].append(f"price {abs(up) / (1 + up) * 100:.0f}% above DCF value — a diversification case only")
+                c["fit"]["score"] = round(c["fit"]["score"] - 0.15, 3)
         for c in scored:   # trend: buying into a broken trend is disclosed and costs fit — ETFs included
             if c.get("trend_ok") is False:
                 c["fit"]["cautions"].append("below its 200-day average — would be bought into a weak trend")
@@ -834,7 +839,7 @@ def run_analysis(rows: list[dict], emit, context: dict | None = None, budget_s:
         dec = decisions[h["symbol"]]
         flagged = bool((h["data"].get("us") or {}).get("flags"))
         in_over = bool(over_sec) and str(h["data"].get("sector") or "") == str(over_sec)
-        dec["no_add"] = dec["action"] == "Keep" and (dec["conviction"] < 55 or flagged or in_over)
+        dec["no_add"] = dec["action"] == "Keep" and (dec["conviction"] < 55 or flagged or in_over or dec.get("gated_add"))
         dec["protect"] = dec["action"] == "Keep" and str(h["data"].get("sector") or "") != str(over_sec or "")
     emit_bg(88, "bounds/scenarios start")
     loss_tol = M._tol_from_context(context)

src/personas.py

@@ -540,14 +540,6 @@ def decide_actions(panel: dict[str, dict], tickers: list[str],
             action = "Keep"
             size_note = (f"conviction supports adding, but at {w * 100:.0f}% of the book the "
                          f"position is already past the {cap * 100:.0f}% healthy cap")
-        elif action == "Trim" and 0 < w < 0.01 and vals:
-            # a partial sale of a sub-1% position is tax friction, not risk management
-            if med < 40:
-                action = "Sell"
-                size_note = f"at {w * 100:.1f}% of the book a partial sale changes nothing — exit cleanly"
-            else:
-                action = "Keep"
-                size_note = f"at {w * 100:.1f}% of the book a trim is not worth the friction — keep or exit, not shave"
         elif action == "Buy More" and sector_over and t in sector_over:
             # adding to the diagnosed overweight sector deepens the book's main problem
             action = "Keep"
@@ -577,7 +569,18 @@ def decide_actions(panel: dict[str, dict], tickers: list[str],
             low = [k for k, v in scores.items() if v <= 35]
             size_note = (f"cyclical at a peak — the {' and '.join(low)} lens{'es' if len(low) > 1 else ''} "
                          f"see real damage; trim rather than ride the cycle down")
+        gated_add = action == "Keep" and bool(size_note) and ("hurdle" in size_note or "deepens" in size_note
+                                                              or "healthy cap" in size_note)
+        if action == "Trim" and 0 < w < 0.01 and vals:
+            # FINAL pass: a partial sale of a sub-1% position is tax friction, not risk
+            # management — whichever tilt produced the Trim
+            if med < 40:
+                action = "Sell"
+                size_note = f"at {w * 100:.1f}% of the book a partial sale changes nothing — exit cleanly"
+            else:
+                action = "Keep"
+                size_note = f"at {w * 100:.1f}% of the book a trim is not worth the friction — keep or exit, not shave"
         out[t] = {"action": action, "conviction": round(med), "dispersion": round(disp),
                   "votes_low": votes_low, "n_lenses": len(vals), "scores": scores,
-                  "size_note": size_note}
+                  "size_note": size_note, "gated_add": gated_add}
     return out

src/revival.py

@@ -360,7 +360,7 @@ def narrative(plan: dict) -> dict | None:
     import json
     for _ in range(2):
         out = parse_json(call_llm(_PLAN_SYSTEM, json.dumps(plan, default=str),
-                                  max_tokens=1200, tier="standard", label="narrative", effort="low"))
+                                  max_tokens=1200, tier="standard", label="narrative", effort="medium"))
         if isinstance(out, dict) and out.get("moves"):
             return out
     return None