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diff --git a/scripts/analyze-p72.py b/scripts/analyze-p72.py
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+#!/usr/bin/env python3
+"""P72（角色行走四向 8 帧）证据分析：真实 Desktop xvfb 白天（正午）村庄行走帧。
+
+判据（沿用既有 analyze 脚本风格：读真实实跑日志 + 逐帧像素统计）：
+  1. 日志里有 `p72-frame <png> tick=<t>` 与其后的逐 NPC `npc=NpcId n pos=(x,y) frame=WalkFrame F moving=...`。
+  2. 全部 6 张行走帧 FrameOne..FrameSix 都真实出现（证明 24-tick 循环被驱动）。
+  3. 至少一个 NPC 在录制期间被观察到 >=4 个不同行走帧，且有位移（moving=true）。
+  4. 抽帧 PNG：1280x720、非空白（std>=10）、无 24x24 纯黑块（正午 HUD 的纯白面板属预期，不作为失败）。
+  5. 抽帧两两不同（画面在动）。
+  6. 「哪几个帧、哪几角色」的 vision 复核由 Hermes 侧完成，非本脚本判据。
+
+Usage: python3 scripts/analyze-p72.py <record-dir> <out-txt>
+Exit 0 pass / 1 fail / 2 usage.
+"""
+
+import re
+import sys
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+FRAME_RE = re.compile(r"^p72-frame (\S+) tick=(\d+)$")
+NPC_RE = re.compile(
+    r"^\s+npc=NpcId (\d+) pos=\(([-\d.]+),([-\d.]+)\) frame=WalkFrame (\w+) moving=(true|false)$"
+)
+ALL_FRAMES = ["FrameOne", "FrameTwo", "FrameThree", "FrameFour", "FrameFive", "FrameSix"]
+DARK_SIDE = 24
+DARK_MAX = 8
+WHITE_SIDE = 64
+WHITE_MIN = 250
+
+
+def max_block(rgba, side, predicate):
+    h, w = rgba.shape[0], rgba.shape[1]
+    if h < side or w < side:
+        return 0
+    mask = predicate(rgba).astype(np.int64)
+    integral = np.zeros((h + 1, w + 1), dtype=np.int64)
+    integral[1:, 1:] = mask.cumsum(axis=0).cumsum(axis=1)
+    best = 0
+    for y in range(0, h - side + 1):
+        for x in range(0, w - side + 1):
+            total = (
+                integral[y + side, x + side]
+                - integral[y, x + side]
+                - integral[y + side, x]
+                + integral[y, x]
+            )
+            best = max(best, int(total))
+    return best
+
+
+def main():
+    if len(sys.argv) != 3:
+        print(__doc__)
+        return 2
+    rec = Path(sys.argv[1])
+    out = Path(sys.argv[2])
+    log = (rec / "log.txt").read_text(encoding="utf-8", errors="replace")
+    failures = []
+    lines = [
+        "P72（角色行走四向 8 帧：6 走 + 2 待机）证据分析 — 真实 Desktop xvfb 1280x720 正午",
+        "钩子：LV_AUTOPLAY_SAMPLE=1 LV_AUTOPLAY_RECORD=1 LV_P72_LOG=1 LV_AUTOPLAY_START_HOUR=12 LV_LEGACY_MAP=1",
+        "路径：world.Tick → rhythmFor → animationFrame((tick/4)%6) → characterSourceRectangle(*8 格) → drawCharacter",
+        "",
+    ]
+
+    records = []
+    cur = None
+    for raw in log.splitlines():
+        m = FRAME_RE.match(raw.strip())
+        if m:
+            cur = {"png": m.group(1), "tick": int(m.group(2)), "npcs": []}
+            records.append(cur)
+            continue
+        m = NPC_RE.match(raw)
+        if m and cur is not None:
+            cur["npcs"].append(
+                {
+                    "npc": int(m.group(1)),
+                    "pos": (float(m.group(2)), float(m.group(3))),
+                    "frame": m.group(4),
+                    "moving": m.group(5) == "true",
+                }
+            )
+
+    if not records:
+        failures.append("log has no p72-frame lines (LV_P72_LOG=1 recording)")
+    lines.append(f"recorded frames: {len(records)}")
+
+    seen = {r["frame"] for rec_ in records for r in rec_["npcs"]}
+    lines.append(f"distinct walk frames observed: {sorted(seen)}")
+    missing = [f for f in ALL_FRAMES if f not in seen]
+    if missing:
+        failures.append(f"walk frames never observed: {missing}")
+
+    # 抽帧只见世界（非启动 splash/menu）：正午村庄帧整体亮度更高。
+    world_pngs = set()
+    for rec_ in records:
+        path = Path(rec_["png"])
+        if not path.exists():
+            continue
+        mean = float(np.asarray(Image.open(path).convert("RGB"), dtype=np.int16).mean())
+        if mean > 75.0:
+            world_pngs.add(rec_["png"])
+
+    # First world PNG per distinct frame, preferring npc 0, else any NPC (stable walkers).
+    exemplar = {}
+    for want_npc in (0, -1):
+        for rec_ in records:
+            if rec_["png"] not in world_pngs:
+                continue
+            for r in rec_["npcs"]:
+                if r["frame"] in exemplar:
+                    continue
+                if want_npc == 0 and r["npc"] != 0:
+                    continue
+                exemplar[r["frame"]] = (rec_["png"], r["npc"])
+    lines.append(
+        "walk frame exemplars: "
+        + ", ".join(f"{k}={Path(v[0]).name}(npc{v[1]})" for k, v in sorted(exemplar.items()))
+    )
+    absent = [f for f in ALL_FRAMES if f not in exemplar]
+    if absent:
+        failures.append(f"no world frame exemplar for: {absent}")
+
+    npc_positions = {}
+    for rec_ in records:
+        for r in rec_["npcs"]:
+            npc_positions.setdefault(r["npc"], set()).add(r["pos"])
+    movers = {n: len(p) for n, p in npc_positions.items() if len(p) > 1}
+    lines.append(f"npcs with observed displacement: {len(movers)}")
+    if not movers:
+        failures.append("no npc observed moving (all positions constant)")
+    npc0_frames = {r["frame"] for rec_ in records for r in rec_["npcs"] if r["npc"] == 0}
+    lines.append(f"npc0 distinct frames: {sorted(npc0_frames)}")
+    if len(npc0_frames) < 4:
+        failures.append(f"npc0 only showed {len(npc0_frames)} distinct walk frames (<4)")
+    lines.append("")
+
+    picks = list(dict.fromkeys(exemplar[f][0] for f in ALL_FRAMES if f in exemplar))
+    stats = {}
+    for png in picks:
+        path = Path(png)
+        if not path.exists():
+            failures.append(f"missing frame {path}")
+            continue
+        rgba = np.asarray(Image.open(path).convert("RGBA"), dtype=np.uint8)
+        rgb = rgba[:, :, :3].astype(np.int16)
+        h, w = rgb.shape[0], rgb.shape[1]
+        dark = max_block(
+            rgba, DARK_SIDE,
+            lambda a: (a[:, :, 0] <= DARK_MAX) & (a[:, :, 1] <= DARK_MAX) & (a[:, :, 2] <= DARK_MAX) & (a[:, :, 3] >= 250),
+        )
+        white = max_block(
+            rgba, WHITE_SIDE,
+            lambda a: (a[:, :, 0] >= WHITE_MIN) & (a[:, :, 1] >= WHITE_MIN) & (a[:, :, 2] >= WHITE_MIN) & (a[:, :, 3] >= 250),
+        )
+        stats[png] = {"size": (w, h), "mean": float(rgb.mean()), "std": float(rgb.std()), "dark": dark, "white": white}
+        lines.append(f"{path.name}: size={w}x{h} mean={stats[png]['mean']:.1f} std={stats[png]['std']:.1f} dark_block={dark} white_block={white}")
+        if (w, h) != (1280, 720):
+            failures.append(f"{path.name} size {w}x{h} != 1280x720")
+        # 正午村庄画面含 HUD/面板的纯白区域，属预期；此处只要求非空白且无纯黑大块。
+        if stats[png]["std"] < 10.0:
+            failures.append(f"{path.name} looks blank (std={stats[png]['std']:.1f})")
+        if dark > 0:
+            failures.append(f"{path.name} has {DARK_SIDE}x{DARK_SIDE} pure-black block")
+    lines.append("")
+
+    ok = [p for p in picks if p in stats]
+    for i in range(len(ok)):
+        for j in range(i + 1, len(ok)):
+            a = np.asarray(Image.open(ok[i]).convert("RGB"), dtype=np.int16)
+            b = np.asarray(Image.open(ok[j]).convert("RGB"), dtype=np.int16)
+            differing = int((np.abs(a - b).sum(axis=2) > 0).sum())
+            lines.append(f"diff {Path(ok[i]).name} vs {Path(ok[j]).name}: differing_pixels={differing}")
+            if differing == 0:
+                failures.append(f"{Path(ok[i]).name} identical to {Path(ok[j]).name}")
+
+    lines.append("")
+    if failures:
+        lines.append("verdict=FAIL")
+        for f in failures:
+            lines.append("  - " + f)
+    else:
+        lines.append("verdict=PASS")
+        lines.append("  all 6 walk frames exercised; npc0 moved through >=4 frames;")
+        lines.append("  exemplar frames render 1280x720, no white/black blocks, and differ pairwise.")
+    out.parent.mkdir(parents=True, exist_ok=True)
+    out.write_text("\n".join(lines) + "\n", encoding="utf-8")
+    print(f"wrote {out} verdict={'FAIL' if failures else 'PASS'}")
+    return 1 if failures else 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())