feat(genome): HypothesisAgentGenome with deterministic id and serde

Dataclass per genoma agente ipotesi con campi prompt/feature/temperature/
top_p/model_tier/lookback/style + parent_ids/generation. Id sha1[:16]
deterministico su contenuto canonico (feature_access ordinate, float
arrotondati). to_dict/from_dict per persistenza.

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

src/multi_swarm/genome/hypothesis.py

@@ -0,0 +1,72 @@
+from __future__ import annotations
+
+import hashlib
+import json
+from dataclasses import dataclass, field
+from enum import StrEnum
+from typing import Any
+
+
+class ModelTier(StrEnum):
+    B = "B"  # Sonnet 4.6 via Anthropic
+    C = "C"  # Qwen 2.5 72B via OpenRouter
+
+
+@dataclass
+class HypothesisAgentGenome:
+    system_prompt: str
+    feature_access: list[str]
+    temperature: float
+    top_p: float
+    model_tier: ModelTier
+    lookback_window: int
+    cognitive_style: str
+    parent_ids: list[str] = field(default_factory=list)
+    generation: int = 0
+    id: str = ""
+
+    def __post_init__(self) -> None:
+        if not self.id:
+            self.id = self._compute_id()
+
+    def _compute_id(self) -> str:
+        payload = {
+            "system_prompt": self.system_prompt,
+            "feature_access": sorted(self.feature_access),
+            "temperature": round(self.temperature, 4),
+            "top_p": round(self.top_p, 4),
+            "model_tier": self.model_tier.value,
+            "lookback_window": self.lookback_window,
+            "cognitive_style": self.cognitive_style,
+        }
+        s = json.dumps(payload, sort_keys=True)
+        return hashlib.sha1(s.encode()).hexdigest()[:16]
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "id": self.id,
+            "system_prompt": self.system_prompt,
+            "feature_access": self.feature_access,
+            "temperature": self.temperature,
+            "top_p": self.top_p,
+            "model_tier": self.model_tier.value,
+            "lookback_window": self.lookback_window,
+            "cognitive_style": self.cognitive_style,
+            "parent_ids": self.parent_ids,
+            "generation": self.generation,
+        }
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> HypothesisAgentGenome:
+        return cls(
+            system_prompt=data["system_prompt"],
+            feature_access=list(data["feature_access"]),
+            temperature=float(data["temperature"]),
+            top_p=float(data["top_p"]),
+            model_tier=ModelTier(data["model_tier"]),
+            lookback_window=int(data["lookback_window"]),
+            cognitive_style=data["cognitive_style"],
+            parent_ids=list(data.get("parent_ids", [])),
+            generation=int(data.get("generation", 0)),
+            id=data.get("id", ""),
+        )