fix: resolve duel roots and recover legacy snake data

- Resolve selected moves and enemy replies on the same simulated turn.
- Add an Apex candidate hook and bump the Prism snake to version 1.1.0.
- Rebuild benchmark states from normalized turn data when snapshots are empty.
- Synthesize missing game snake identities during legacy database migration.
- Add regression coverage for duel timing and partial legacy schemas.

snakes/PrismBattleSnake_GPT_5_6_Sol.py

@@ -1,4 +1,4 @@
-"""PrismBattleSnake_GPT_5_6_Sol v1.0.1
+"""PrismBattleSnake_GPT_5_6_Sol v1.1.0
 
 Built on ApexBattleSnake v1.0.0.  All strategic logic is inherited.
 Performance improvement: all spatial primitives (flood fill, territory,
@@ -25,6 +25,7 @@ Key speedups:
   S11: _future_survival_tree inlines legal-move check with bitboard ops.
   S12: Duel minimax uses tuple bodies and bitboard move generation.
   S13: Iterative deepening reuses a transposition table and move-order hints.
+  S14: Candidate duel moves and enemy replies resolve on the same root turn.
 """
 
 from __future__ import annotations
@@ -41,7 +42,7 @@ _DIR_DELTAS = ((0, 1), (0, -1), (-1, 0), (1, 0))
 _DIR_NAMES = ("up", "down", "left", "right")
 
 class PrismBattleSnake_GPT_5_6_Sol(ApexBattleSnake):
-  VERSION = "1.0.1"
+  VERSION = "1.1.0"
 
   def __init__(self) -> None:
     super().__init__()
@@ -268,6 +269,28 @@ class PrismBattleSnake_GPT_5_6_Sol(ApexBattleSnake):
       deadline=deadline,
     )
 
+  def _minimax_candidate_id(
+    self, my_body: list, enemy_body: list, my_target: tuple[int, int],
+    food_set: set, hazard_set: set,
+    my_health: int, enemy_health: int, hazard_damage: int, hazard_count: dict,
+    width: int, height: int, max_depth: int, alpha: float, beta: float,
+    deadline: float | None, previous_hazard_set: set | None = None,
+  ) -> tuple[float, int]:
+    """Resolve our selected move and every enemy reply simultaneously."""
+    search = self._new_duel_search(
+      food_set, hazard_set, hazard_count, hazard_damage,
+      width, height, deadline,
+    )
+    return search.search_candidate(
+      my_body=my_body,
+      enemy_body=enemy_body,
+      my_target=my_target,
+      my_health=my_health,
+      enemy_health=enemy_health,
+      max_depth=max_depth,
+      previous_hazards=previous_hazard_set if previous_hazard_set is not None else hazard_set,
+    )
+
   def _minimax_sim_id(
     self, my_body: list, enemy_body: list, food_set: set, hazard_set: set,
     my_health: int, enemy_health: int, hazard_damage: int, hazard_count: dict,