feat(runner): Monte Carlo runner, closed-form metrics and balance CLI

Adds the S6 layer on top of the deterministic combat engine:
- metrics.py: closed-form binomial statistics (win rate, standard error,
  3-sigma confidence band) with clamped degenerate proportions.
- runner.py: EncounterSpec/Side definitions, per-run SeededRng streams,
  global duplicate-id disambiguation, attrition averages per combatant.
- cli.py: pf1e-sim entry point producing a French balance report with
  win rates, 3-sigma bands, draws, rounds, and attrition.
- Movement fix: greedy straight-line heuristic could oscillate at walls;
  _step_toward now follows the true shortest path via an unbounded
  Dijkstra cost field from the target (Grid.reachable budget=None).
- Regression tests: melee unit routes around a wall and engages; grid
  unbounded-budget coverage.

tests/test_combat.py

@@ -394,3 +394,24 @@ def test_nearest_enemy_targeting_and_full_rounds() -> None:
         "round 4 gob: orc-2 down",
         "battle over: players win in 4 rounds",
     )
+
+
+def test_melee_routes_around_wall() -> None:
+    """Melee unit follows the true shortest path instead of oscillating at a wall."""
+    legend = {
+        ".": TerrainType(type="floor", move_cost=1),
+        "#": TerrainType(type="wall", move_cost=None, blocks_los=True),
+    }
+    spec = MapSpec(
+        name="wall-test",
+        terrain=("....#...", "....#...", "........"),
+        legend=legend,
+    )
+    grid = Grid.from_spec(spec)
+    mover = make_combatant("mover", hp=20, speed=30)
+    target = make_combatant("target", speed=0)
+    states = [make_state(mover, "players", (1, 1)), make_state(target, "monsters", (1, 5))]
+    engine = CombatEngine(SeededRng(42), grid, states)
+    result = engine.run()
+    assert result.winner == "players"
+    assert result.stats["mover"].hits > 0