feat(phase-2.5): mutate_prompt_llm operator + weighted dispatcher + GA wiring

Implementazione completa Phase 2.5 (LLM prompt mutator) seguendo il piano in
docs/superpowers/plans/2026-05-11-mutate-prompt-llm-phase-2-5.md.

Nuovo modulo src/multi_swarm/genome/mutation_prompt_llm.py:
- 6 mutation instructions (tighten_threshold, swap_comparator, add_condition,
  remove_condition, change_timeframe, add_temporal_gate)
- mutate_prompt_llm(g, llm, rng, mutator_tier=ModelTier.B): clona genome con
  tier B per la call mutator, costruisce system+user prompt con istruzione
  scelta random, estrae prompt da tag <prompt>...</prompt>, valida
- is_valid_prompt(): 3 check (lunghezza >= 50, keyword tecnica, diff > 5%
  Levenshtein-like via difflib.SequenceMatcher)
- Fallback random_mutate su qualsiasi validation fail O LLM exception

Esteso src/multi_swarm/genome/mutation.py con weighted_random_mutate dispatcher:
con probabilità prompt_mutation_weight invoca mutate_prompt_llm, altrimenti
random_mutate. Backward compat: llm=None oppure weight=0 → solo scalare.

Integrazione GA loop + RunConfig:
- GAConfig.prompt_mutation_weight: float = 0.0 (default off)
- next_generation(llm=...) propagato all'invocazione mutator
- RunConfig.prompt_mutation_weight con stesso default
- run_phase1: passa llm a next_generation solo se weight > 0
- scripts/run_phase1.py: flag CLI --prompt-mutation-weight

Tests (+18, 175 totale):
- tests/unit/test_mutation_prompt_llm.py (12): extract_prompt, is_valid_prompt
  3 check, operator success + fallback su 3 modi (invalid/identical/exception),
  tier B per LLM call, istruzione scelta dal pool
- tests/unit/test_mutation_dispatcher.py (4): weight 0/1/None + distribuzione
  30/70 su 1000 estrazioni con tolleranza ±5%
- tests/integration/test_ga_loop_with_prompt_mutator.py (2): loop con
  weight=1.0 produce prompt evoluti; backward compat weight=0 invariato

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

tests/unit/test_mutation_dispatcher.py

@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+import random
+from collections import Counter
+from dataclasses import dataclass
+
+from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
+from multi_swarm.genome.mutation import weighted_random_mutate
+
+_PROMPT = (
+    "Strategia mean-reversion 1h BTC. Entry long quando RSI(14) < 30 e "
+    "close > SMA(50). Exit short quando RSI(14) > 70."
+)
+
+
+def _make_genome() -> HypothesisAgentGenome:
+    return HypothesisAgentGenome(
+        system_prompt=_PROMPT,
+        feature_access=["close"],
+        temperature=0.9,
+        top_p=0.95,
+        model_tier=ModelTier.C,
+        lookback_window=200,
+        cognitive_style="physicist",
+    )
+
+
+@dataclass
+class _R:
+    text: str
+
+
+class _AlwaysMutateLLM:
+    """Mock LLM che ritorna sempre un prompt mutato valido."""
+
+    def __init__(self) -> None:
+        self.calls = 0
+
+    def complete(self, genome, system, user, max_tokens: int = 2000) -> _R:
+        self.calls += 1
+        return _R(
+            text=(
+                "<prompt>Strategia momentum 1h BTC. Entry long quando close > "
+                f"SMA(70) e ATR(14) crescente. Exit con stop loss 3% (call #{self.calls}).</prompt>"
+            )
+        )
+
+
+def test_weighted_dispatcher_zero_weight_never_calls_llm() -> None:
+    llm = _AlwaysMutateLLM()
+    rng = random.Random(0)
+    parent = _make_genome()
+
+    for _ in range(50):
+        weighted_random_mutate(parent, rng, llm=llm, prompt_mutation_weight=0.0)
+
+    assert llm.calls == 0
+
+
+def test_weighted_dispatcher_full_weight_always_calls_llm() -> None:
+    llm = _AlwaysMutateLLM()
+    rng = random.Random(0)
+    parent = _make_genome()
+
+    for _ in range(20):
+        child = weighted_random_mutate(
+            parent, rng, llm=llm, prompt_mutation_weight=1.0
+        )
+        assert child.system_prompt != parent.system_prompt
+
+    assert llm.calls == 20
+
+
+def test_weighted_dispatcher_none_llm_falls_back_to_scalar() -> None:
+    """Senza llm passato (backward compat) → solo mutazione scalare."""
+    rng = random.Random(0)
+    parent = _make_genome()
+
+    for _ in range(50):
+        child = weighted_random_mutate(parent, rng, llm=None, prompt_mutation_weight=0.5)
+        assert child.system_prompt == parent.system_prompt
+
+
+def test_weighted_dispatcher_distribution_30_70() -> None:
+    """Su 1000 estrazioni con weight=0.3 il prompt mutator deve essere chiamato ~300 volte."""
+    llm = _AlwaysMutateLLM()
+    rng = random.Random(123)
+    parent = _make_genome()
+
+    counter: Counter[str] = Counter()
+    for _ in range(1000):
+        child = weighted_random_mutate(
+            parent, rng, llm=llm, prompt_mutation_weight=0.3
+        )
+        if child.system_prompt != parent.system_prompt:
+            counter["prompt"] += 1
+        else:
+            counter["scalar"] += 1
+
+    # 30% ± 5% tolerance
+    assert 250 <= counter["prompt"] <= 350, f"prompt mutations: {counter['prompt']}"
+    assert 650 <= counter["scalar"] <= 750, f"scalar mutations: {counter['scalar']}"