feat: add Zenith strategy and move latency tracing

- Register Zenith 1.1 with balanced duel verdicts and forced-outcome overrides.
- Prioritize permanent-trail space in Constrictor and health-aware hazard routes.
- Deduct executor queue delays and return fallback directions on move timeouts.
- Correlate response headers, replay timings, and sampled structured trace logs.
- Move shared metrics publication off the response path without losing local counts.
- Allow configurable tournament opponents and add a psql helper recipe.
- Preserve empty latency values through compact replay encoding and migration.
- Update Arena game links and use default assets for missing customizations.
- Cover strategy, HTTP lifecycle, tracing, replay codecs, and dashboard behavior.
- Validate with 260 passing Python tests, 4 skips, and 2 passing JavaScript tests.

snakes/strategies/__init__.py

@@ -2,5 +2,10 @@
 
 from snakes.strategies.apex import ApexBattleSnake
 from snakes.strategies.prism import PrismBattleSnake_GPT_5_6_Sol
+from snakes.strategies.zenith import ZenithBattleSnake_ClaudeOpus5_5
 
-__all__ = ("ApexBattleSnake", "PrismBattleSnake_GPT_5_6_Sol")
+__all__ = (
+  "ApexBattleSnake",
+  "PrismBattleSnake_GPT_5_6_Sol",
+  "ZenithBattleSnake_ClaudeOpus5_5",
+)

snakes/strategies/zenith.py

@@ -0,0 +1,126 @@
+"""ZenithBattleSnake_ClaudeOpus5_5 v1.1.0
+
+Built on PrismBattleSnake_GPT_5_6_Sol v1.5.0. Prism's bitboard engine, duel
+search, and adversarial rollouts are inherited unchanged.
+
+Prism only runs the exact duel search on near-tied heuristic candidates
+(score gap <= 8). A move outside that window is never checked against every
+enemy reply, so a forced loss with a strong heuristic score can still be
+played, and a forced win with a weaker score can be skipped.
+
+Z1: Root verdict pass — after Prism decides, every safe duel candidate is
+    resolved against all simultaneous enemy replies with the shared
+    transposition table (cheap: most subtrees are already cached).
+Z2: Proven-loss veto — a candidate whose completed search proves a loss is
+    replaced by the best non-losing candidate.
+Z3: Proven-win override — a candidate whose completed search proves a win is
+    played even if the heuristic preferred another move.
+"""
+from time import perf_counter
+
+from snakes.engine.duel_search import BitboardDuelSearch
+from snakes.strategies.zenith_survival import ZenithSurvivalMixin
+from snakes.strategies.prism import PrismBattleSnake_GPT_5_6_Sol
+
+# A completed search value beyond half of WIN/LOSS can only come from a terminal.
+_PROVEN_WIN = BitboardDuelSearch.WIN / 2
+_PROVEN_LOSS = BitboardDuelSearch.LOSS / 2
+# Reserve kept for Apex's history bookkeeping after the verdict pass.
+_VERDICT_RESERVE_MS = 25.0
+
+class ZenithBattleSnake_ClaudeOpus5_5(ZenithSurvivalMixin, PrismBattleSnake_GPT_5_6_Sol):
+  VERSION = "1.1.0"
+
+  def __init__(self) -> None:
+    super().__init__()
+    self.name = "ZenithBattleSnake"
+    self.version = self.VERSION
+
+  def _get_timeout_buffer_ms(self):
+    # Leave room for executor scheduling, history, JSON and network delivery.
+    return max(180, super()._get_timeout_buffer_ms())
+
+  def _choose_duel_move(
+    self, safe_moves, my_body, my_len, my_health, other_snakes, food_set,
+    hazard_set, hazard_damage, hazard_count, previous_hazard_set,
+    enemy_attack_map, enemy_can_grow, total_occupancy, width, height, deadline,
+  ):
+    best_move, scores, depth = super()._choose_duel_move(
+      safe_moves=safe_moves, my_body=my_body, my_len=my_len,
+      my_health=my_health, other_snakes=other_snakes, food_set=food_set,
+      hazard_set=hazard_set, hazard_damage=hazard_damage,
+      hazard_count=hazard_count, previous_hazard_set=previous_hazard_set,
+      enemy_attack_map=enemy_attack_map, enemy_can_grow=enemy_can_grow,
+      total_occupancy=total_occupancy, width=width, height=height,
+      deadline=deadline,
+    )
+    if len(safe_moves) < 2:
+      return best_move, scores, depth
+
+    verdicts = self._duel_verdicts(
+      safe_moves=safe_moves, my_body=my_body, enemy=other_snakes[0],
+      my_health=my_health, food_set=food_set, hazard_set=hazard_set,
+      hazard_damage=hazard_damage, hazard_count=hazard_count,
+      previous_hazard_set=previous_hazard_set, width=width, height=height,
+      deadline=deadline,
+    )
+    chosen = self._apply_verdicts(best_move, scores, verdicts)
+    if chosen != best_move:
+      self.add_to_history({
+        "turn": self.game_board.get_turn(), "mode": "duel",
+        "zenith_verdict_changed_move": True,
+        "from": best_move, "to": chosen,
+        "verdicts": {m: round(v, 1) for m, (v, _) in verdicts.items()},
+      })
+    depth = max([depth, *(d for _, d in verdicts.values())])
+    return chosen, scores, depth
+
+  def _duel_verdicts(
+    self, safe_moves, my_body, enemy, my_health, food_set, hazard_set,
+    hazard_damage, hazard_count, previous_hazard_set, width, height, deadline,
+  ) -> dict[str, tuple[float, int]]:
+    """Return completed exact-search values per candidate (Z1)."""
+    verdicts: dict[str, tuple[float, int]] = {}
+    # Search every root at equal depth. A full iterative search for the first
+    # move could consume the deadline before the other roots were visited.
+    search_deadline = None if deadline is None else deadline - _VERDICT_RESERVE_MS / 1000
+    if search_deadline is not None and perf_counter() >= search_deadline:
+      return verdicts
+    search = self._new_duel_search(
+      food_set, hazard_set, hazard_count, hazard_damage, width, height, search_deadline,
+    )
+    search.deadline = search_deadline
+    for depth in range(1, self._planning_depth + 1):
+      layer = {}
+      for move, pos in safe_moves.items():
+        value, completed = search.search_candidate(
+          my_body, enemy["body"], (pos["x"], pos["y"]),
+          my_health, enemy.get("health", 100), depth, previous_hazard_set,
+        )
+        if completed != depth:
+          return verdicts
+        layer[move] = (value, completed)
+      verdicts = layer
+    return verdicts
+
+  @staticmethod
+  def _apply_verdicts(
+    best_move: str, scores: dict[str, float],
+    verdicts: dict[str, tuple[float, int]],
+  ) -> str:
+    """Apply proven-win override (Z3) and proven-loss veto (Z2)."""
+    def rank(move: str) -> tuple[float, float]:
+      return verdicts[move][0], scores.get(move, float("-inf"))
+
+    wins = [m for m, (v, _) in verdicts.items() if v >= _PROVEN_WIN]
+    if wins and best_move not in wins:
+      return max(wins, key=rank)
+
+    best_verdict = verdicts.get(best_move)
+    if best_verdict is None or best_verdict[0] > _PROVEN_LOSS:
+      return best_move
+    alternatives = [m for m, (v, _) in verdicts.items() if v > _PROVEN_LOSS]
+    if not alternatives:
+      return best_move
+    # Prefer Prism's heuristic among surviving moves; fall back to search value.
+    return max(alternatives, key=lambda m: (scores.get(m, float("-inf")), verdicts[m][0]))

snakes/strategies/zenith_survival.py

@@ -0,0 +1,97 @@
+"""Zenith-specific permanent-trail and health-aware scoring."""
+from snakes.engine.health_routes import health_route_cost
+from snakes.engine.survival_search import CompactSurvivalSearch
+
+class ZenithSurvivalMixin:
+  def _choose_constrictor_move(
+    self, safe_moves, my_body, my_len, my_health, other_snakes, food_set,
+    hazard_set, hazard_damage, hazard_count, previous_hazard_set,
+    enemy_attack_map, enemy_can_grow, total_occupancy, width, height, deadline,
+  ):
+    # In constrictor bodies never vacate. Remaining free cells are a survival
+    # horizon, not storage for the existing body. Requiring free space >= body
+    # length incorrectly marks every late-game candidate dead.
+    bb = self._get_bb(width, height)
+    occupied = bb.set_to_bits({
+      (p['x'], p['y']) for body in [my_body, *(s['body'] for s in other_snakes)]
+      for p in body
+    })
+    scores = {}
+    for move, pos in safe_moves.items():
+      target = bb.idx(pos['x'], pos['y'])
+      space = bb.flood_count(target, occupied)
+      liberties = bb.open_neighbor_count(target, occupied)
+      danger = enemy_attack_map.get((pos['x'], pos['y']), 0) >= my_len
+      # Preserve free capacity first. Enemy confinement must not outweigh our
+      # own ability to survive, and tail access has no meaning in this mode.
+      scores[move] = space * 10.0 + liberties * 8.0 - (10000 if danger else 0)
+      if liberties == 0:
+        scores[move] -= 5000
+
+    # Give each candidate the same shallow horizon before considering deeper
+    # layers. Never let the first candidate consume everyone else's budget.
+    search = CompactSurvivalSearch(
+      bb, set(), True, deadline, branch=3, enemy_branch=2, response_cap=8,
+    )
+    ranked = scores
+    for depth in range(1, 5):
+      layer = {}
+      for move, pos in safe_moves.items():
+        if self._remaining_ms(deadline) < 15:
+          break
+        before = search.deadline_exits
+        value = search.search_selected(my_body, other_snakes, (pos['x'], pos['y']), depth)
+        if search.deadline_exits != before:
+          break
+        layer[move] = value
+      if len(layer) != len(safe_moves):
+        break
+      # Rollout can veto immediate/near-term death, but cannot turn temporary
+      # enemy confinement into an overwhelming speculative attack reward.
+      ranked = {m: scores[m] + v * 0.15 for m, v in layer.items()}
+      best = max(ranked, key=ranked.get)
+      if depth == 4:
+        return best, ranked
+    return max(ranked, key=ranked.get), ranked
+
+  def _score_move(self, *args, **kwargs):
+    score, info = super()._score_move(*args, **kwargs)
+    # Apex calls this hook with keywords. Constrictor uses its dedicated path.
+    if not kwargs or kwargs['is_constrictor']:
+      return score, info
+    point = (kwargs['pos']['x'], kwargs['pos']['y'])
+    food = kwargs['food_set']
+    hazards = kwargs['hazard_set']
+    damage = kwargs['hazard_damage']
+    stacks = kwargs['hazard_count']
+    health = 100 if point in food else kwargs['my_health'] - 1 - (
+      damage * stacks.get(point, 1) if point in hazards else 0
+    )
+    if health <= 0:
+      score -= 20000
+      info['is_survivable'] = False
+      return score, info
+    if not hazards:
+      return score, info
+    blocked = info['blocked'] - {point}
+    cost = health_route_cost(
+      point, food, hazards, stacks, damage, blocked,
+      kwargs['width'], kwargs['height'], refuge=True,
+    )
+    # Existing hazards deal damage on entry even when newly seen. Prefer
+    # leaving lethal hazard corridors before low-health urgency kicks in.
+    if cost is None or cost >= health:
+      score -= 12000
+      info['is_survivable'] = False
+    else:
+      score -= cost * 8.0
+    if health < 55:
+      food_cost = health_route_cost(
+        point, food, hazards, stacks, damage, blocked,
+        kwargs['width'], kwargs['height'],
+      )
+      if food_cost is None or food_cost >= health:
+        score -= 2500
+      else:
+        score += (55 - health) * 12.0 / (food_cost + 1)
+    return score, info