feat(phase-2.5): Task 6 cost_kind attribution + fees_eat_alpha threshold CLI

Task 6 del piano Phase 2.5 (deferito → ora completato):
- CostRecord: nuovo campo call_kind (default "hypothesis")
- CostTracker.record: accetta call_kind opzionale, summary include
  by_call_kind breakdown (hypothesis vs mutation)
- Schema cost_records: aggiunta colonna call_kind TEXT NOT NULL DEFAULT
  'hypothesis' + migration soft via ALTER TABLE in init_schema (silently
  catched per DB pre-Task 6)
- Repository.save_cost_record: nuova arg call_kind opzionale
- mutate_prompt_llm: accetta cost_tracker/repo/run_id opzionali e logga
  la call mutator con call_kind="mutation" quando sink presente
- weighted_random_mutate, next_generation: propagano cost sink
- orchestrator.run_phase1: passa cost_tracker+repo+run_id a
  next_generation solo se prompt_mutation_weight > 0

Esposto fees_eat_alpha_threshold come parametro AdversarialAgent
(default 0.5 = comportamento Phase 1.5 invariato), propagato via
RunConfig.fees_eat_alpha_threshold e flag CLI
--fees-eat-alpha-threshold. Abilita ablation con soglia 0.7-0.8 senza
modificare codice — adversarial finding dominante in tutti i run
Phase 2/2.5 (50+ HIGH per run).

Tests (+4 → 186 totale):
- test_cost_tracker: default call_kind="hypothesis"; breakdown
  by_call_kind con hypothesis+mutation
- test_mutation_prompt_llm: logging mutation cost con sink completo;
  backward compat senza sink (no errore)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/multi_swarm/agents/adversarial.py

@@ -59,8 +59,13 @@ class AdversarialReport:
 class AdversarialAgent:
     """Agente hand-crafted che applica check euristici a una strategia."""
 
-    def __init__(self, fees_bp: float = 5.0) -> None:
+    def __init__(
+        self,
+        fees_bp: float = 5.0,
+        fees_eat_alpha_threshold: float = 0.5,
+    ) -> None:
         self._engine = BacktestEngine(fees_bp=fees_bp)
+        self._fees_eat_alpha_threshold = fees_eat_alpha_threshold
 
     def review(self, strategy: Strategy, ohlcv: pd.DataFrame) -> AdversarialReport:
         signal_fn = compile_strategy(strategy)
@@ -163,7 +168,7 @@ class AdversarialAgent:
         # Se gross_pnl <= 0 il check non si applica (gia' perdente).
         gross_pnl = sum(t.gross_pnl for t in result.trades)
         total_fees = sum(t.fees for t in result.trades)
-        if gross_pnl > 0 and total_fees / gross_pnl > 0.5:
+        if gross_pnl > 0 and total_fees / gross_pnl > self._fees_eat_alpha_threshold:
             report.findings.append(
                 Finding(
                     name="fees_eat_alpha",