#!/usr/bin/env python3
"""
run_procedure_dsl.py — 拿 procedure-dsl/spec.md 当指令本, 让 Claude Agent SDK
(走 ~/.claude OAuth Max 订阅额度) 对单个 post 跑完三阶段提取, 落 case-N.{md,html}.

设计:
- 复用 Agent 仓 examples/process_pipeline/run_pipeline.py --use-claude-sdk 的 OAuth
  思路: 子进程 env 把 ANTHROPIC_API_KEY / BASE_URL / AUTH_TOKEN 显式置空, 让
  claude CLI 回落到 OAuth 凭证.
- 与 agent/llm/claude_code_oauth.py 的 one-shot wrapper 不同: 那个 max_turns=1
  + allowed_tools=[], 本脚本让 Agent 真正自主跑——开 Read/Write/Bash/Edit/Glob/Grep,
  multi-turn, 自己读 spec.md / source / 调 bin/taxonomy-lookup.py / 写产物.
- 单 post 输入, 多终端 = 多并行 (用户自己开).

用法 (本脚本位于 procedure-dsl/, SDK cwd 默认设到 procedure-dsl/):
    # source 是 input/case-N-raw.json, 脚本自动从 image_url_list 拉图作多模态.
    # --out-dir 必填: 是 outputs/ 下的目录名, 产物全落这里; case_id 自动从它的 basename 推.
    python run_procedure_dsl.py input/case-2-raw.json --out-dir case-2
    python run_procedure_dsl.py input/case-2-raw.json --out-dir case-2 \\
        --extra-image /path/to/local-ref.png --model claude-sonnet-4-6
    # 跑实验版 spec-test/ 对比 (产物落 outputs/case-2_test/):
    python run_procedure_dsl.py input/case-2-raw.json --out-dir case-2_test --version test
    # 中断后恢复 (产物保留, agent 重读磁盘接着跑):
    python run_procedure_dsl.py input/case-2-raw.json --out-dir case-2 --resume

source 文件 schema (procedure-dsl/input/*.json 约定):
    {
      "title": str, "link": str, "body_text": str,
      "image_url_list": [{"image_type": int, "image_url": str}, ...],
      "publish_timestamp": ..., "channel_account_name": str,
    }
"""
import argparse
import asyncio
import base64
import json
import logging
import sys
import time
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List

# run_procedure_dsl.py → procedure-dsl/
DSL_ROOT = Path(__file__).resolve().parent


def _derive_case_id(out_dir: str) -> str:
    """从 --out-dir 自动派生 case_id (用于 prompt 文件名 + case_data.case_id + suggestions record).

    规则: Path(out_dir).name 去掉 "case-" 前缀 (--out-dir 是 outputs/ 下的相对名).
      --out-dir case-5             → "5"
      --out-dir case-5-newflow     → "5-newflow"
      --out-dir photo-album        → "photo-album"   (无 case- 前缀也行)
      --out-dir case-11            → "11"

    这样用户传 --out-dir 控制目录名, case_id 自动跟随 — 不再撞历史 case 目录.
    """
    name = Path(out_dir).name
    if name.startswith("case-"):
        name = name[5:]
    return name or "?"


def _resolve_out_dir(out_dir_arg: str, workdir: Path) -> Path:
    """把 --out-dir 解析成实际工作目录. --out-dir 是 `outputs/` 下的相对名:

      --out-dir newdir          → <workdir>/outputs/newdir
      --out-dir case-5          → <workdir>/outputs/case-5
      --out-dir outputs/newdir  → <workdir>/outputs/newdir   (容忍已带 outputs/ 前缀, 不重复嵌套)

    传绝对路径则原样用 (escape hatch).
    """
    p = Path(out_dir_arg)
    if p.is_absolute():
        return p.resolve()
    parts = p.parts
    if parts and parts[0] == "outputs":          # 容忍历史习惯的 outputs/ 前缀
        parts = parts[1:]
    base = workdir / "outputs"
    return (base / Path(*parts)).resolve() if parts else base.resolve()

# Sonnet 4.6 公开价目 (per 1M tokens, USD), 用于 estimated cost.
# OAuth Max 模式 SDK 报的 total_cost_usd 通常是 None — 我们自己按 token 算个
# "如果走 API 等价多少钱"作为 quota 消耗的直观代理.
PRICE_SONNET_4_6 = {
    "input": 3.00,
    "output": 15.00,
    "cache_creation": 3.75,
    "cache_read": 0.30,
}


def _estimate_cost_usd(usage: Dict[str, Any]) -> float:
    if not usage:
        return 0.0
    return (
        usage.get("input_tokens", 0) / 1_000_000 * PRICE_SONNET_4_6["input"]
        + usage.get("output_tokens", 0) / 1_000_000 * PRICE_SONNET_4_6["output"]
        + usage.get("cache_creation_input_tokens", 0) / 1_000_000 * PRICE_SONNET_4_6["cache_creation"]
        + usage.get("cache_read_input_tokens", 0) / 1_000_000 * PRICE_SONNET_4_6["cache_read"]
    )


# ──── 多模态: 图片 → Anthropic content block ──────────────────────────────────

_MEDIA_TYPE = {
    ".png": "image/png",
    ".jpg": "image/jpeg",
    ".jpeg": "image/jpeg",
    ".gif": "image/gif",
    ".webp": "image/webp",
}

# client-side image cache: DSL_ROOT/.image_cache/<sha256>.<ext>
# 同一 URL 多次跑 / resume 不重复下载. 不影响 SDK / OAuth, 纯本地 IO.
_IMAGE_CACHE_DIR = DSL_ROOT / ".image_cache"

# ── 多图压缩策略 (防止几十张图把单轮 output 撑破 32K/64K 上限) ──────────────────
# 阶梯: 图少 → 单图只降分辨率; 图多 (>OVER) → 拼 3×3 九宫格把"图块数"压 9:1, 模型逐图
# 铺陈的冲动小了, 骨架/output 也跟着小. (跳过极多图帖那一档在 batch 选片侧做, 见
# batch_extract_procedures.py 的 _IMG_SKIP_CAP — 那边知道图数又控制 top-N 名额回填.)
_IMG_MAX_EDGE = 1024      # 单图降分辨率: 长边上限 px
_IMG_MONTAGE_OVER = 18    # 图数 > 此值 → 拼九宫格 (与 batch 选片阈值需各自维护, 语义不同)
_IMG_GRID_CELL = 512      # 九宫格每格 px → 3×3 拼图长边 1536, 压在 Anthropic 1568 上限内

# 浏览器 UA, 避免 mmbiz / 其他图床对裸 python-requests 默认 UA 直接 403.
_DOWNLOAD_UA = (
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
    "(KHTML, like Gecko) Chrome/124.0.0.0 Safari/537.36"
)


def _url_to_cached_path(url: str, timeout: float = 15.0) -> Path:
    """下载 url 到 _IMAGE_CACHE_DIR, 返回本地路径. 已缓存直接返回不重下.

    用 URL 的 sha256 当文件名, 扩展名从 URL path 推断 (找不到默认 .png).
    设计目的: 绕过 Anthropic 服务端对 image URL 的 robots.txt 检查
    (mmbiz.qpic.cn 等图床 robots disallow 会让 API 直接 400).
    """
    import hashlib
    from urllib.parse import urlparse
    from urllib.request import Request, urlopen

    _IMAGE_CACHE_DIR.mkdir(exist_ok=True)
    digest = hashlib.sha256(url.encode("utf-8")).hexdigest()[:24]
    ext = Path(urlparse(url).path).suffix.lower()
    if ext not in _MEDIA_TYPE:
        ext = ".png"
    local = _IMAGE_CACHE_DIR / f"{digest}{ext}"

    if local.exists() and local.stat().st_size > 0:
        return local

    req = Request(url, headers={"User-Agent": _DOWNLOAD_UA, "Accept": "image/*,*/*;q=0.8"})
    with urlopen(req, timeout=timeout) as resp:
        data = resp.read()
    local.write_bytes(data)
    return local


def _resolve_local(ref: str) -> Path:
    """图 ref → 本地路径: URL 先下载进缓存 (绕 robots.txt), 本地路径校验存在。"""
    if ref.startswith(("http://", "https://")):
        return _url_to_cached_path(ref)
    p = Path(ref).expanduser().resolve()
    if not p.exists():
        raise FileNotFoundError(f"image not found: {ref}")
    return p


def _pil_to_block(im: Any) -> Dict[str, Any]:
    """PIL.Image → Anthropic base64 content block (统一 JPEG q90, 体积可控)。"""
    import io
    buf = io.BytesIO()
    im.save(buf, format="JPEG", quality=90)
    return {
        "type": "image",
        "source": {"type": "base64", "media_type": "image/jpeg",
                   "data": base64.standard_b64encode(buf.getvalue()).decode()},
    }


_OCR_ENGINE: Any = None
_OCR_UNAVAILABLE = False


def _ocr_image(im: Any) -> str:
    """对一张 PIL.Image 做全分辨率 OCR, 返回拼接后的文字 (失败/无字 → 空串)。
    九宫格会把图压到 512px 小字糊掉, 故在缩放前先 OCR 把文字走文本通道保住。
    RapidOCR 未装 → 整体禁用 OCR (只此一次, 之后不再尝试), 退化成纯拼图。"""
    global _OCR_ENGINE, _OCR_UNAVAILABLE
    if _OCR_UNAVAILABLE:
        return ""
    try:
        if _OCR_ENGINE is None:
            from rapidocr_onnxruntime import RapidOCR
            _OCR_ENGINE = RapidOCR()
        import numpy as np
        result, _ = _OCR_ENGINE(np.array(im))
        if not result:
            return ""
        # result: [[box, text, score], ...] 按检测顺序 (大致从上到下) → 直接拼
        return " ".join(seg[1] for seg in result if seg and len(seg) >= 2 and seg[1]).strip()
    except ImportError:
        _OCR_UNAVAILABLE = True
        print("[ocr] ⚠️ rapidocr 未装, 本次禁用 OCR (九宫格仅拼图无文字旁挂)", flush=True)
        return ""
    except Exception as e:
        print(f"[ocr] skip 1 张 ({type(e).__name__}: {e})", flush=True)
        return ""


def _format_ocr_sidecar(ocr_texts: List[str]) -> str:
    """把每图 OCR 文字按"拼图#·格#"标注成一个文本块 (跟九宫格阅读顺序对齐)。
    全空 → 返回空串 (不加块)。i 从 0 起: grid=i//9, cell=i%9 (逐行 左→右 上→下)。"""
    lines = []
    for i, t in enumerate(ocr_texts):
        if not t:
            continue
        lines.append(f"■ 拼图{i // 9 + 1}·格{i % 9 + 1}: {t}")
    if not lines:
        return ""
    return ("【配图 OCR 文字】图已压成九宫格, 小字可能糊; 以下是各格**全分辨率** OCR 原文, "
            "与拼图位置一一对应 (格号即拼图内 从左到右、从上到下 的序), 用它补全图里看不清的"
            "菜单/参数/prompt 文字:\n" + "\n".join(lines))


def _montage(pil_imgs: List[Any], cell: int) -> List[Any]:
    """把图按每 9 张拼成一张 3×3 九宫格 (每格 cell px, 白底居中)。返回拼图列表。
    最后一组不足 9 张, 余下格子留白。阅读顺序: 逐行 从左到右、从上到下 (封面=第一张拼图左上)。"""
    from PIL import Image
    grids: List[Any] = []
    for i in range(0, len(pil_imgs), 9):
        canvas = Image.new("RGB", (cell * 3, cell * 3), (255, 255, 255))
        for j, im in enumerate(pil_imgs[i:i + 9]):
            t = im.copy()
            t.thumbnail((cell, cell))                      # 等比缩进格子
            row, col = divmod(j, 3)
            canvas.paste(t, (col * cell + (cell - t.width) // 2,
                             row * cell + (cell - t.height) // 2))
        grids.append(canvas)
    return grids


def _append_image_blocks_raw(blocks: List[Dict[str, Any]], images: List[str]) -> None:
    """回落路径 (PIL 不可用时): 逐图原样 base64, 不降分辨率/不拼图。"""
    n_ok = n_fail = 0
    for ref in images:
        try:
            local = _resolve_local(ref)
            blocks.append({
                "type": "image",
                "source": {"type": "base64",
                           "media_type": _MEDIA_TYPE.get(local.suffix.lower(), "image/png"),
                           "data": base64.standard_b64encode(local.read_bytes()).decode()},
            })
            n_ok += 1
        except Exception as e:
            n_fail += 1
            print(f"[image] skip {ref[:80]}... ({type(e).__name__}: {e})", flush=True)
    if images:
        print(f"[image] (无 PIL) {n_ok}/{len(images)} 原样 base64, {n_fail} 失败跳过", flush=True)


def _append_image_blocks(blocks: List[Dict[str, Any]], images: List[str]) -> None:
    """把 images 转成 content block 加到 blocks，按阶梯压缩 (见 _IMG_* 常量):
       ≤OVER 张 → 逐图降分辨率单发; >OVER 张 → 拼 3×3 九宫格减块数。
    单张下载/解码失败不阻塞整批。PIL 不可用 → 回落原样 base64。
    base / variant driver 共用 — 保证 image 容错策略一致。"""
    if not images:
        return
    try:
        from PIL import Image
    except Exception:
        _append_image_blocks_raw(blocks, images)
        return

    pil_imgs: List[Any] = []
    n_fail = 0
    for ref in images:
        try:
            pil_imgs.append(Image.open(_resolve_local(ref)).convert("RGB"))
        except Exception as e:
            n_fail += 1
            print(f"[image] skip {ref[:80]}... ({type(e).__name__}: {e})", flush=True)
    n = len(pil_imgs)
    if n == 0:
        print(f"[image] 0/{len(images)} 可用 ({n_fail} 失败)", flush=True)
        return

    if n > _IMG_MONTAGE_OVER:
        ocr_texts = [_ocr_image(im) for im in pil_imgs]   # 缩放前全分辨率 OCR 保文字
        grids = _montage(pil_imgs, _IMG_GRID_CELL)
        for g in grids:
            blocks.append(_pil_to_block(g))
        sidecar = _format_ocr_sidecar(ocr_texts)
        if sidecar:
            blocks.append({"type": "text", "text": sidecar})
        n_ocr = sum(1 for t in ocr_texts if t)
        print(f"[image] {n} 张 > {_IMG_MONTAGE_OVER} → 拼九宫格 {len(grids)} 张 "
              f"(每张≤9图·格{_IMG_GRID_CELL}px) + {n_ocr} 格 OCR 文字旁挂, "
              f"{n_fail} 失败跳过", flush=True)
    else:
        for im in pil_imgs:
            im.thumbnail((_IMG_MAX_EDGE, _IMG_MAX_EDGE))   # 等比降分辨率 (小图不放大)
            blocks.append(_pil_to_block(im))
        print(f"[image] {n} 张单图 (降分辨率≤{_IMG_MAX_EDGE}px), {n_fail} 失败跳过", flush=True)


def _image_prompt_note(n_images: int) -> str:
    """配图行文案: 跟 _append_image_blocks 的阶梯保持一致, 拼图时务必告诉模型
    它收到的是九宫格 (否则会以为有 N 张独立图, 引用错位 / 逐图铺陈撑爆 output)。"""
    if n_images == 0:
        return "本消息未附图."
    if n_images > _IMG_MONTAGE_OVER:
        import math
        n_grid = math.ceil(n_images / 9)
        return (f"本消息附了 {n_grid} 张【九宫格拼图】—— 由原始 {n_images} 张配图按 3×3 网格拼接 "
                f"(每张最多含 9 个子图, 已统一降分辨率)。原图过多, 拼图是为压 token; "
                f"分析时把**每个格子当一张独立配图**看, 阅读顺序: 每张拼图内 从左到右、从上到下逐行; "
                f"封面图在第一张拼图的左上格 (老格式 image_type=2 / 新格式原序第一张)。"
                f"图后另附一个【配图 OCR 文字】文本块 (各格全分辨率 OCR), 拼图里小字糊掉时**以 OCR 文字为准**.")
    return (f"本消息附了 {n_images} 张图作多模态内容 (已降分辨率; URL 抽自老格式的 image_url_list "
            f"或新格式的 images; 老格式 image_type=2 封面排最前, 新格式按原序).")


# ──── source JSON → 自动拉 image_url_list ────────────────────────────────────

def _images_from_source(source_path: Path) -> List[str]:
    """从 procedure-dsl/input/*.json 抽 URL 列表, 兼容两套 schema:

    1. 老 case-*-raw.json: `image_url_list: [{image_type, image_url}, ...]`,
       image_type=2 (封面) 排最前, 其余维持原序.
    2. 新 eval_case-*.json: `images: [url_str, ...]`, 无封面信号, 原序返回.

    schema 错配时如果检测到疑似图字段但抽不到任何 URL, 会打 warning —
    multimodal pipeline 静默 0 张图比报错更难排查.
    """
    if source_path.suffix.lower() != ".json":
        return []
    try:
        with source_path.open(encoding="utf-8") as f:
            data = json.load(f)
    except (json.JSONDecodeError, OSError):
        return []
    if not isinstance(data, dict):
        return []

    # 新 schema (eval_case-*.json): 裸 URL 数组
    flat = data.get("images")
    if isinstance(flat, list):
        urls = [u for u in flat if isinstance(u, str) and u]
        if not urls and flat:
            print(f"[image] ⚠️ {source_path.name} 有 images 字段但抽不到 URL "
                  f"(首项类型={type(flat[0]).__name__})", flush=True)
        return urls

    # 老 schema (case-*-raw.json): {image_type, image_url} dict 数组
    items = data.get("image_url_list")
    if not isinstance(items, list):
        return []
    covers, others = [], []
    for it in items:
        if not isinstance(it, dict):
            continue
        url = it.get("image_url")
        if not isinstance(url, str) or not url:
            continue
        (covers if it.get("image_type") == 2 else others).append(url)
    return covers + others


# ──── 初始 user 消息 (text + 图片 blocks) ─────────────────────────────────────

def _build_initial_blocks(
    source_ref: str, case_id: str, out_dir_arg: str, images: List[str], workdir: Path,
    spec_name: str = "spec",
) -> List[Dict[str, Any]]:
    # 全部路径用绝对路径喂给 Agent — 它训练习惯就吃绝对, 顺水推舟省心.
    # 用 .as_posix() 统一 forward slash, 跟 f-string 拼接的 "/..." 一致.
    # spec_name 由 --version 决定 (spec / spec-backup / spec-test ...), 默认 "spec".
    spec_dir    = (workdir / spec_name).as_posix()
    spec_readme = (workdir / spec_name / "README.md").as_posix()
    spec_tools  = (workdir / spec_name / "tools.md").as_posix()
    # --out-dir 是 outputs/ 下的相对名 (--out-dir newdir → outputs/newdir); 绝对路径原样用.
    _out_dir = _resolve_out_dir(out_dir_arg, workdir)
    case_dir    = _out_dir.as_posix()
    scratch_dir = (_out_dir / "_scratch").as_posix()
    # source 路径直接用 source_ref (用户传的 positional arg). 不再从 case_id 推 —
    # 否则当 --out-dir 跟 source 文件名不对齐时 (e.g. --out-dir outputs/update + source=input/case-5.json),
    # Agent 会收到 "input/case-update-raw.json" 这种不存在路径, 浪费 10+ turn 自我探索.
    _src = Path(source_ref)
    case_input = _src.as_posix() if _src.is_absolute() else (workdir / _src).resolve().as_posix()

    text = f"""请按 {spec_name}/ 目录里的 SKILL 处理这个 post.

## 起手指令 (路径已给绝对值, 直接照搬, 不要改, 不要先 find 探查)

1. `Read(file_path="{spec_readme}")` — self-driven skill 的入口, 含完整 phase 加载指南 (累积式) + 自查清单 + 工具调用规则. 读完不要再 Read 它.
2. `Read(file_path="{spec_tools}")` — 外部脚本接口手册. 读完不要重读.
3. 按 README phase 加载指南**累积式**前进, 中间产物是**单个 `workflow.json` 文件**, 各 phase 都 in-place Edit 它 (不写多个中间快照):
   - Phase 1.1 心智模型 → `{case_dir}/understanding.md` (含 procedure 数量判断)
   - Phase 1.2 骨架 → **Write** `{case_dir}/workflow.json` (procedures 数组骨架)
   - Phase 1.3 IO 闭合 → **Edit** workflow.json 加 anchor
   - Phase 2A/2B/2C 归一化 → **Edit** workflow.json 加 effect/action/type/sub/form + procedures[i].type_registry
   - Phase 3 lint + 渲染 → 调 render-case.py + lint-case.py (不写 case_data.json, renderer 内存组装)
   - Phase 3 .md → Write `{case_dir}/case-{case_id}-<slug>.md`

## ❌ 重复读取禁令 (CRITICAL: ZERO REPEATED READS)

你拥有完美的长期记忆（由于 Context 累积，你读取过的所有文件内容会永远保留在你的 Context 中）。
请**绝对不要**重复读取任何文件！任何重复的 `Read` 动作都是对 Token 和回合数 Budget 的极大浪费。
- **禁忌 1**：不要因为看到 spec 文档中写了 `详见 [tools.md §2]` 就去重新 `Read(file_path="{spec_tools}")`。你已经在 Turn 3 读过它了，直接检索你的记忆！
- **禁忌 2**：不要因为进入了 Phase 2B，就去重新 `Read` 任何 Phase 2 的 spec 文件（如 `phase2-normalize.md`）。你已经在 Phase 2A 开始时读过它了，它就在你的记忆中，直接使用它！
- 在发出任何 `Read` 指令前，必须自我核对：“我之前读过这个文件吗？”。如果读过，绝对不要再次 Read！

## 输入

- case 原文: `{case_input}`
  schema (两种都可能):
    · 老格式 (case-*-raw.json): {{title, link, body_text, image_url_list:[{{image_type,image_url}}], publish_timestamp, channel_account_name}}
    · 新格式 (eval_case-*.json): {{title, link, body_text, images:[url_str], videos, channel, content_type, like_count, publish_timestamp, channel_content_id, ...}}
  正文 (body_text) 和元数据从此文件 Read; 两种 schema 的 body_text 含义一致.
- 配图: {_image_prompt_note(len(images))}

## 输出目录

`{case_dir}/` (这一 case 的所有产物都放这里, 不要污染其他目录)

## 读取范围 (硬约束)

你**只能** Read 以下三处 (用绝对路径 + 子路径):

1. `{spec_dir}/` 及其所有子目录 (skill 全部内容: README.md / tools.md / 各 phase 规格文件 / output/ / taxonomy/ / templates/ / tools/ — 但 tools/*.py **不要 Read 源码**, 只通过 Bash 调用; 具体子目录名以 {spec_name}/README.md 的章节地图为准, **不要脑补 part1-/part2- 这类对称目录**)
2. `{case_input}` (当前 case 的原文, 只此一个)
3. `{case_dir}/` 目录 (你自己的工作产物, 含其下 `_scratch/`)

## 长输出 dump 区 (sanctioned scratch)

如果你需要 dump 大 Bash 输出做后续分析 (e.g. `taxonomy-lookup --subtree` 输出过大想分段读, 或 `find` 结果想保留), **只写到 `{scratch_dir}/`** (runner 已预创建):

✅ `python {spec_dir}/tools/taxonomy-lookup.py --dim 实质 --subtree /表象/视觉 > {scratch_dir}/subst_visual.txt`
✅ 然后 `Read(file_path="{scratch_dir}/subst_visual.txt")` 提取需要的段
✅ 一次性小探查 / smoke test (验证某条 tool 命令、看某段数据长啥样) 也可以放 `{scratch_dir}/`, 用完即弃

❌ **不要用 Python 脚本生成 / 批量改写 `workflow.json`** — 它由你**直接 Write 骨架 + 逐字段填** 演化 (见各 phase 规格). 写 build_workflow / add_anchors / normalize 这类脚本去拼 JSON, 反而踩转义 / 控制字符坑 (LLM 拼长 JSON 极易坏), 还把本该逐 step 的语义判断埋进一次性脚本里. 几十处字段要批量改 → 用 **`spec/tools/wf-patch.py`** (path=value 清单, 工具负责安全写 JSON + 写入即校验; 见 {spec_name}/tools.md §2), 零星单处用 Edit. `_scratch/` 的 Python **只用于「dump 大 tool 输出」和「smoke test」, 不是 workflow.json 的生产线**.
❌ **不要写到项目根 `scratch/`** (那是 dev/repo 维护用的临时区, 禁区)
❌ **不要 ls / Read 项目根 `scratch/`** (别人的 dev 临时文件, 跟你无关; 你的 scratch 在上面的 `_scratch/`)

**禁读区** (不要 Read 也不要 ls 这些位置): 项目根的 spec.md / design.md (旧文件), examples/ 全目录, input/ 下其他 case-*-raw.json, outputs/case-OTHER/ (其他 case 目录), bin/ 全目录 (已废弃), **项目根 `scratch/`** (跟你的 `_scratch/` 不是一回事 — 上面 ✅ 那个 `_scratch/` 作用类似 outputs 目录下的 `_scratch/`).

## 阶段三流程 (脚本组装, 不写 case_data.json)

新工作流: Phase 2 已经把所有结构化数据 in-place Edit 进 `{case_dir}/workflow.json`. Phase 3 直接调 2 个脚本命令, **不需要写 case_data.json** (renderer 在内存把 workflow + source-input + page_title + case_id 组装成 case_data 喂给 build_html).

```bash
# Step A: lint + 自动 record 新 type 到 type_suggestions.md
python {spec_dir}/tools/lint-case.py --workflow {case_dir}/workflow.json --case-id {case_id}
# stdout 报 "type 完整性: N 个提示" 时, 回 Phase 2 Edit workflow.json 补 procedures[i].type_registry, 重跑 lint
# stdout 报 "已 record M 条新 type" 时, 表示 {spec_name}/taxonomy/type_suggestions.md 已自动同步

# Step B: 渲染 HTML (--source-input 必带, --page-title --case-id 必带)
python {spec_dir}/tools/render-case.py \
    --workflow {case_dir}/workflow.json \
    --source-input {case_input} \
    --page-title "Case {case_id} · <主题>" \
    --case-id {case_id} \
    --out {case_dir}/case-{case_id}-<slug>.html

# Step C: Write {case_dir}/case-{case_id}-<slug>.md (DSL 文本版, 按 {spec_name}/output/md-structure.md §11 结构)
```

**`--source-input` 行为**: renderer 自动从 raw 抽 body_text + 封面图 + 图集兜底填到 case_data.source (内存里). 微信公众号长文走 inline 图 + 封面; 小红书短文走 body + "--- 附图 ---" 末尾追加非封面图. 你**不必手工**复制 raw.body_text — 完全交给 renderer.

`workflow.json` 的契约见 [{spec_name}/output/case-data.schema.json]({spec_name}/output/case-data.schema.json) 里 Procedure 的 definition (顶层 `{{procedures: [{{id, name, purpose, declarations, type_registry?, steps, return_row?}}]}}`). **不要参考其他 `outputs/case-*/` 下任何文件**.

## 其他约束

- 推断补全用 `inferred: true` + `inferred_reason` 标在 IO item 上, 不要静默插入.
- 完成后用一段话总结: 工序梗概 + 输出文件路径 + 你对 DSL 的关键发现.
"""
    blocks: List[Dict[str, Any]] = [{"type": "text", "text": text}]
    _append_image_blocks(blocks, images)
    return blocks


# ──── Trace 写盘 (outputs/case-N/_trace.md) ───────────────────────────────────
# 每次 run 把 turn / tool_use / text / result 写到一份 markdown 流水, 实时 append.
# Ctrl-C 后还能离线 review Agent 走过哪些 turn / 读了哪些文件 / 调了哪些 Bash.

def _fmt_tool_input(name: str, inp: Any) -> str:
    """tool_use input → 人读 1 行摘要."""
    if not isinstance(inp, dict):
        return f"`{repr(inp)[:200]}`"
    if name == "Read":
        suffix = ""
        if inp.get("offset") or inp.get("limit"):
            suffix = f" (offset={inp.get('offset', 0)}, limit={inp.get('limit', '-')})"
        return f"`{inp.get('file_path', '')}`{suffix}"
    if name == "Write":
        return f"`{inp.get('file_path', '')}` ({len(inp.get('content', '')):,} chars)"
    if name == "Edit":
        return f"`{inp.get('file_path', '')}`"
    if name == "Bash":
        cmd = inp.get("command", "")
        if len(cmd) > 200:
            cmd = cmd[:200] + "..."
        return f"`{cmd}`"
    if name == "Grep":
        return f"pattern=`{inp.get('pattern', '')}` path=`{inp.get('path', '')}`"
    if name == "Glob":
        return f"pattern=`{inp.get('pattern', '')}`"
    if name in ("Task", "Agent"):
        return f"`{inp.get('description', '')}` [subagent={inp.get('subagent_type', '?')}]"
    preview = str(inp)
    return f"`{preview[:200] + '...' if len(preview) > 200 else preview}`"


def _trace_banner(trace_path: Path, args, resume_sid, source_path, n_images: int) -> None:
    """session 起手 banner. Append 模式 — 历次 run 累积在同一 _trace.md."""
    trace_path.parent.mkdir(parents=True, exist_ok=True)
    now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    if resume_sid:
        chunk = (
            f"\n\n---\n\n## ▶ Resume @ {now}\n\n"
            f"恢复 session `{resume_sid[:8]}` (case-{args.case_id})\n"
        )
    else:
        chunk = (
            f"\n\n---\n\n## ▶ Fresh @ {now}\n\n"
            f"- case: `{args.case_id}`\n"
            f"- source: `{source_path}`\n"
            f"- spec: `{getattr(args, 'spec_name', 'spec')}`\n"
            f"- images: `{n_images}`\n"
            f"- model: `{args.model}`\n"
            f"- max_turns: `{args.max_turns}`\n"
        )
    with trace_path.open("a", encoding="utf-8") as f:
        f.write(chunk)


def _trace_init(trace_path: Path, sid: str, data: Dict[str, Any]) -> None:
    """SDK init → 写 session_id / model 到 trace."""
    line = (
        f"\n_session={sid[:8]} · model={data.get('model')!r} "
        f"· apiKeySource={data.get('apiKeySource')!r}_\n"
    )
    with trace_path.open("a", encoding="utf-8") as f:
        f.write(line)


def _trace_turn(trace_path: Path, turn: int, msg: Any) -> None:
    """每个 AssistantMessage → text + tool_use 摘要追加到 trace."""
    now = datetime.now().strftime("%H:%M:%S")
    parts = [f"\n\n### Turn {turn} · {now}\n"]
    texts: List[str] = []
    tools: List[tuple] = []
    for block in msg.content:
        if hasattr(block, "text"):
            t = block.text.strip()
            if t:
                texts.append(t)
        elif hasattr(block, "name") and hasattr(block, "input"):
            tools.append((block.name, block.input))
        # thinking blocks 跳过
    if texts:
        for t in texts:
            for line in t.split("\n"):
                parts.append(f"> {line}\n" if line else ">\n")
            parts.append("\n")
    if tools:
        for name, inp in tools:
            parts.append(f"- `{name}` — {_fmt_tool_input(name, inp)}\n")
    with trace_path.open("a", encoding="utf-8") as f:
        f.write("".join(parts))


def _trace_result(trace_path: Path, msg: Any, elapsed: float,
                  usage: Dict[str, Any], est_cost: float) -> None:
    """ResultMessage → final summary 追加到 trace."""
    now = datetime.now().strftime("%H:%M:%S")
    sdk_cost = msg.total_cost_usd
    sdk_cost_str = f"${sdk_cost:.4f}" if sdk_cost is not None else "None (OAuth Max)"
    chunk = (
        f"\n\n### ◀ Result · {now}\n\n"
        f"- subtype: `{msg.subtype}` · is_error: `{msg.is_error}`\n"
        f"- num_turns: `{msg.num_turns}` · duration: `{msg.duration_ms}ms` · wall: `{elapsed:.1f}s`\n"
        f"- tokens: in={usage.get('input_tokens', 0):,} "
        f"out={usage.get('output_tokens', 0):,} "
        f"cache_w={usage.get('cache_creation_input_tokens', 0):,} "
        f"cache_r={usage.get('cache_read_input_tokens', 0):,}\n"
        f"- cost: sdk={sdk_cost_str}, est_if_api=${est_cost:.4f}\n"
    )
    with trace_path.open("a", encoding="utf-8") as f:
        f.write(chunk)


# ──── 主流程: 跑 ClaudeSDKClient ──────────────────────────────────────────────

async def run(args: argparse.Namespace) -> None:
    from claude_agent_sdk import (
        AssistantMessage,
        ClaudeAgentOptions,
        ClaudeSDKClient,
        ClaudeSDKError,
        RateLimitEvent,
        ResultMessage,
        TextBlock,
        AgentDefinition,
    )

    workdir = Path(args.workdir or DSL_ROOT).resolve()
    if not workdir.exists():
        sys.exit(f"❌ workdir not found: {workdir}")
    source_path = Path(args.source).expanduser().resolve()
    if not source_path.exists():
        sys.exit(f"❌ source not found: {source_path}")

    auto_imgs = _images_from_source(source_path)
    extra_imgs = args.extra_image or []
    images = auto_imgs + extra_imgs

    # 给 Agent 的路径优先用 workdir 相对路径 (它的 cwd 就是 workdir),
    # 不在 workdir 内才回落到绝对路径.
    try:
        source_for_agent = source_path.relative_to(workdir).as_posix()
    except ValueError:
        source_for_agent = str(source_path)
    # --out-dir 是 outputs/ 下的相对名 (--out-dir newdir → outputs/newdir); 绝对路径原样用.
    out_dir = _resolve_out_dir(args.out_dir, workdir)
    out_dir.mkdir(parents=True, exist_ok=True)
    sid_file = out_dir / ".session_id"
    # 预创建 sanctioned scratch 目录, 让 Agent 看到就在那, 不用犹豫能不能 mkdir
    (out_dir / "_scratch").mkdir(exist_ok=True)

    resume_sid = None
    if args.resume:
        if not sid_file.exists():
            sys.exit(f"❌ --resume 但未找到 {sid_file}; 先正常跑一次建立 session")
        resume_sid = sid_file.read_text(encoding="utf-8").strip() or None
        if not resume_sid:
            sys.exit(f"❌ {sid_file} 为空, 无法 resume")

    try:
        rel_out = out_dir.relative_to(workdir).as_posix()
    except ValueError:
        rel_out = out_dir.as_posix()

    if resume_sid:
        # resume 时不重发图和原始指令 (历史里都有); 只给一段 "接着做" 增量.
        # 关键: 提醒 Agent 用户可能改过中间产物, 必须 Read 当前磁盘版本.
        blocks = [{"type": "text", "text": (
            f"上次中断了, 接续做 case-{args.case_id} 的提取流程.\n\n"
            f"先 ls {rel_out}/ 看当前已落盘哪些产物;\n"
            f"用户可能在中断期间编辑过任何中间产物 (understanding.md / workflow.json) "
            f"或改过 spec/ 内任何文件 — 务必 Read 这些**当前磁盘版本**, "
            f"不要凭之前记忆继续. 如发现明显人工修订痕迹, 沿用用户改过的版本.\n\n"
            f"⚠️【重要禁令与强制要求】：如果流程进行到 Phase 2（归一化与分类匹配），主 Agent **绝对禁止**手动调用 taxonomy-lookup.py 查询或手动决策！你必须强制阅读最新的 `spec/extraction/phase2-normalize.md` 规范，通过运行 `prepare-subtask.py` 生成物理任务切片，然后调用 `Agent`（或 `Task`）工具将任务分别分发给预定义好的 `phase-2a-normalizer` 和 `phase-2b-matcher` 子 Agent 并行协作执行！"
        )}]
    else:
        blocks = _build_initial_blocks(source_for_agent, args.case_id, args.out_dir, images, workdir, args.spec_name)

    print(f"[setup] workdir       = {workdir}")
    print(f"[setup] version       = {args.version if args.version else '(default)'}")
    print(f"[setup] spec dir      = {args.spec_name}/")
    print(f"[setup] source        = {source_path}")
    print(f"[setup] → as agent    = {source_for_agent}")
    print(f"[setup] case_id       = {args.case_id}")
    print(f"[setup] output dir    = {out_dir}")
    print(f"[setup] auto images   = {len(auto_imgs)} (from image_url_list)"
          if not resume_sid else f"[setup] auto images   = -- (resume 跳过)")
    print(f"[setup] extra images  = {len(extra_imgs)} {extra_imgs if extra_imgs else ''}"
          if not resume_sid else f"[setup] extra images  = -- (resume 跳过)")
    print(f"[setup] model         = {args.model}")
    print(f"[setup] max_turns     = {args.max_turns}")
    if resume_sid:
        print(f"[setup] resume        = {resume_sid[:8]} (跳过初始 prompt + 图)")
    else:
        print(f"[setup] resume        = no (fresh start)")
    print(flush=True)

    # 起手写 trace banner. 之后每 turn / result 都会 append 到这个 _trace.md.
    trace_path = out_dir / "_trace.md"
    _trace_banner(trace_path, args, resume_sid, source_path, len(images))
    print(f"[trace] -> {trace_path}", flush=True)

    stderr_buf: List[str] = []

    def _capture_stderr(line: str) -> None:
        if line:
            stderr_buf.append(line)
            print(f"[stderr] {line}", flush=True)

    agents = {
        "phase-2a-normalizer": AgentDefinition(
            description="Expert in Phase 2A (effect/action/type normalization). Use this agent to read task_2a.json, normalize action/effect/type against spec trees, manage procedure-level type_registry, and generate outputs/case-N/_scratch/patch_2a.json.",
            prompt="""You are a dedicated Phase 2A normalization sub-agent.
Your goal is to process the inputs and outputs of a workflow for effect, action, and type normalization:
1. Read the outputs/case-N/_scratch/task_2a.json file to get the steps and IO variables.
2. Normalize every step's `effect` and `action` against the taxonomy specs in `spec/taxonomy/effect.json` and `spec/taxonomy/action.json`.
3. Normalize every IO variable's `type` against `spec/taxonomy/type.json`. If a custom type is used, register it in the procedure's `type_registry` with extends and description.
4. Output a standard `patch_2a.json` JSON file under outputs/case-N/_scratch/.
IMPORTANT: The format of `patch_2a.json` MUST be a flat JSON array of objects, where each object has a "path" and a "value" key (exactly conforming to the `wf-patch.py` tool contract).
Example format:
[
  {"path": "p1.s1.effect", "value": "预处理"},
  {"path": "p1.s1.action", "value": "提取/化学提取/反推"},
  {"path": "p1.s1.inputs[0].type", "value": "工具选型标准"},
  {"path": "p1.type_registry.工具配置.extends", "value": "评语"},
  {"path": "p1.type_registry.工具配置.desc", "value": "工具选型依据..."}
]
Do not output raw dictionary structure or any other nesting. Do not touch or modify other files.""",
            tools=["Read", "Grep", "Glob", "Write", "Edit", "Bash"],
            model="sonnet",
        ),
        "phase-2b-matcher": AgentDefinition(
            description="Expert in Phase 2B (substance/form taxonomy matching). Use this agent to read task_2b.json, invoke taxonomy-lookup.py to query substance and form paths for each variable, and generate outputs/case-N/_scratch/patch_2b.json.",
            prompt="""You are a dedicated Phase 2B taxonomy matching sub-agent.
Your goal is to query and match the substance and form for each workflow IO variable:
1. Read the outputs/case-N/_scratch/task_2b.json file to get the variables to match.
2. For each variable, run `python spec/tools/taxonomy-lookup.py --dim 实质 --match "..."` and `--dim 形式 --match "..."` to search for the most precise taxonomy paths matching the variable's value, name, and related_images.
3. Output a standard `patch_2b.json` JSON file under outputs/case-N/_scratch/.
IMPORTANT: The format of `patch_2b.json` MUST be a flat JSON array of objects, where each object has a "path" and a "value" key (exactly conforming to the `wf-patch.py` tool contract). Substance and form values can be single string paths, multiple paths separated by ' + ', or JSON arrays of strings for multi-path matching.
Example format:
[
  {"path": "p1.s1.inputs[0].substance", "value": "/理念/知识/思想/概念范畴/性质属性/功能效用"},
  {"path": "p1.s1.inputs[0].form", "value": "/呈现/视觉/视觉制作/构图编排/版面设计/版面结构"},
  {"path": "p1.s2.inputs[0].substance", "value": ["/理念/知识/商业/前沿技术/AI智能/AI应用", "/理念/知识/思想"]}
]
Do not output raw dictionary structure or any other nesting. Do not touch or modify other files.""",
            tools=["Read", "Grep", "Glob", "Write", "Edit", "Bash"],
            model="sonnet",
        )
    }

    options = ClaudeAgentOptions(
        model=args.model,
        cwd=str(workdir),
        resume=resume_sid,
        # ⚠️ 支持旧版 "Task" 与新版 "Agent" 标识符以确保在所有 Claude Code / SDK 版本中均可激活子 agent
        allowed_tools=["Read", "Write", "Edit", "Bash", "Glob", "Grep", "Task", "Agent"],
        agents=agents,
        max_turns=args.max_turns,
        permission_mode="bypassPermissions",
        setting_sources=[],
        env={
            "ANTHROPIC_API_KEY": "",
            "ANTHROPIC_BASE_URL": "",
            "ANTHROPIC_AUTH_TOKEN": "",
            # 多图帖 (几十张图) 的提取响应容易顶破默认 32000 output token 上限 →
            # 调到 sonnet 4.x 的出参天花板 64000。外部已设同名环境变量则以这里为准。
            "CLAUDE_CODE_MAX_OUTPUT_TOKENS": "64000",
        },
        stderr=_capture_stderr,
    )

    turn = 0
    usage: Dict[str, Any] = {}
    t0 = time.time()

    try:
        async with ClaudeSDKClient(options=options) as client:
            async def _input_stream():
                yield {
                    "type": "user",
                    "message": {"role": "user", "content": blocks},
                    "parent_tool_use_id": None,
                    "session_id": "default",
                }
            await client.query(_input_stream())

            async for msg in client.receive_response():
                if isinstance(msg, AssistantMessage):
                    turn += 1
                    for block in msg.content:
                        if isinstance(block, TextBlock):
                            print(f"\n[turn {turn} · text]\n{block.text}\n", flush=True)
                        elif hasattr(block, "name") and hasattr(block, "input"):
                            preview = str(block.input)
                            if len(preview) > 200:
                                preview = preview[:200] + "..."
                            print(f"[turn {turn} · tool_use] {block.name}({preview})", flush=True)
                        elif hasattr(block, "thinking"):
                            pass  # thinking 太长, 跳过
                        else:
                            print(f"[turn {turn} · {type(block).__name__}] {block!r}", flush=True)
                    _trace_turn(trace_path, turn, msg)
                elif isinstance(msg, ResultMessage):
                    if msg.usage:
                        usage = dict(msg.usage)
                    elapsed = time.time() - t0
                    sdk_cost = msg.total_cost_usd
                    sdk_cost_str = (
                        f"${sdk_cost:.4f}" if sdk_cost is not None
                        else "None (OAuth Max — not metered as $)"
                    )
                    est_cost = _estimate_cost_usd(usage)
                    print(
                        f"\n[result] subtype={msg.subtype} is_error={msg.is_error} "
                        f"turns={msg.num_turns} duration={msg.duration_ms}ms wall={elapsed:.1f}s\n"
                        f"  tokens: in={usage.get('input_tokens', 0):,} "
                        f"out={usage.get('output_tokens', 0):,} "
                        f"cache_w={usage.get('cache_creation_input_tokens', 0):,} "
                        f"cache_r={usage.get('cache_read_input_tokens', 0):,}\n"
                        f"  cost  : sdk={sdk_cost_str}, est_if_api=${est_cost:.4f} "
                        f"(Sonnet 4.6 价目)",
                        flush=True,
                    )
                    _trace_result(trace_path, msg, elapsed, usage, est_cost)
                    if msg.is_error:
                        print(f"❌ result is_error=True", file=sys.stderr)
                        sys.exit(2)
                elif isinstance(msg, RateLimitEvent):
                    info = getattr(msg, "rate_limit_info", None)
                    info_status = getattr(info, "status", None) if info else None
                    if info_status == "allowed_warning":
                        print(f"⚠️  [rate_limit_warning] Max 订阅 5h 窗口余额较少, `claude /status` 看余量", file=sys.stderr)
                    elif info_status and info_status not in ("allowed", "allowed_warning"):
                        print(f"❌ [rate_limit_blocked] {info_status!r} — Max 订阅 5h 窗口已耗尽, `claude /status` 看余量", file=sys.stderr)
                        sys.exit(3)
                else:
                    name = type(msg).__name__
                    if name == "SystemMessage":
                        data = getattr(msg, "data", {}) or {}
                        subtype = getattr(msg, "subtype", "?")
                        if subtype == "init":
                            sid = data.get('session_id', '') or ''
                            if sid:
                                # 早写: 一拿到 sid 就落盘, Ctrl-C 再早也能 resume
                                sid_file.write_text(sid, encoding="utf-8")
                                _trace_init(trace_path, sid, data)
                            print(
                                f"[init] model={data.get('model')!r} "
                                f"apiKeySource={data.get('apiKeySource')!r} "
                                f"session={sid[:8]}",
                                flush=True,
                            )

    except ClaudeSDKError as e:
        tail = "\n".join(stderr_buf[-20:])
        print(
            f"❌ SDK error: {type(e).__name__}: {e}\n"
            f"--- CLI stderr (last 20 lines) ---\n{tail}",
            file=sys.stderr,
        )
        sys.exit(1)


# ──── CLI ─────────────────────────────────────────────────────────────────────

def _parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(
        description="跑 procedure-dsl 提取流程 (Claude Agent SDK, OAuth Max)",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog=__doc__,
    )
    p.add_argument("source", help="原始 post 文件 (input/case-N-raw.json; 也接受 .md/.txt)")
    p.add_argument("--extra-image", action="append", default=[],
                   help="额外配图: 本地路径 or http(s) URL. 加在 image_url_list "
                        "自动抽出的图后面. 可多次传.")
    p.add_argument("--out-dir", type=str, required=True,
                   help="输出工作目录名, 落在 outputs/ 下. e.g. --out-dir case-5-newflow → 实际 outputs/case-5-newflow/ . "
                        "Agent 所有产物 (understanding.md / workflow.json / case-X.html / .md) 都落在这. "
                        "case_id 自动从 basename 推 (e.g. --out-dir case-5-newflow → case_id='5-newflow'). "
                        "已带 outputs/ 前缀会被容忍 (不重复嵌套); 传绝对路径则原样用.")
    p.add_argument("--model", default="claude-sonnet-4-6",
                   help="Claude 模型名 (default: claude-sonnet-4-6)")
    p.add_argument("--workdir", default=None,
                   help=f"SDK cwd. 默认 {DSL_ROOT} (即本脚本所在 procedure-dsl/)")
    p.add_argument("--version", default=None, metavar="SUFFIX",
                   help="spec 版本: 跑 workdir 下的 spec-<SUFFIX>/ 目录. "
                        "e.g. --version backup → spec-backup/, --version test → spec-test/. "
                        "不传则默认 spec/.")
    p.add_argument("--max-turns", type=int, default=300,
                   help="Agent 最大回合数 (default: 300, 跑完整三阶段 + lint 留足余量)")
    p.add_argument("--resume", action="store_true",
                   help="恢复中断的 session: 从 outputs/case-N/.session_id 读 sid, "
                        "Agent 拿上次完整历史接着跑. 中断期间可手改任何中间产物, "
                        "Agent 会重新 Read 磁盘版本而不是凭记忆.")
    p.add_argument("--max-retries", type=int, default=3,
                   help="Stream idle / 临时错误自动 --resume 重试次数 (default: 3). "
                        "退避 10s/20s/40s, 不重试 RateLimit (exit 3) 和 Ctrl-C.")
    return p.parse_args()


def main() -> None:
    # Windows 默认控制台编码 (cp1252/gbk) 撑不住 spec.md / Agent 输出里的中文 +
    # emoji, 第一次打印就 UnicodeEncodeError. 在 main 入口统一切到 UTF-8.
    for stream in (sys.stdout, sys.stderr):
        if hasattr(stream, "reconfigure"):
            stream.reconfigure(encoding="utf-8", errors="replace")
    logging.basicConfig(level=logging.WARNING)
    args = _parse_args()
    # 注入派生属性: case_id 自动从 --out-dir basename 算 (去掉 case- 前缀).
    # 下游 prompt / banner / type_suggestions record 都用这个 args.case_id.
    args.case_id = _derive_case_id(args.out_dir)
    # spec 目录名: --version backup → "spec-backup", 不传 → "spec".
    args.spec_name = "spec" if not args.version else f"spec-{args.version}"

    # ──── 自动重试循环 ──────────────────────────────────────────────────────
    # exit code 约定:
    #   0  = 成功
    #   1  = ClaudeSDKError (含 setup 错, 一般非 transient, 不重试)
    #   2  = ResultMessage is_error=True (含 stream idle timeout, **可重试**)
    #   3  = RateLimitEvent (配额耗尽, 不重试 — 等 5h window 滑动)
    #   130 = KeyboardInterrupt (用户主动中断, 不重试)
    # 重试退避: 10s → 20s → 40s, 都在 prompt cache TTL (5min) 内
    max_retries = args.max_retries
    workdir_check = Path(args.workdir or DSL_ROOT).resolve()
    out_dir_check = Path(args.out_dir)
    sid_file_check = (out_dir_check if out_dir_check.is_absolute() else (workdir_check / out_dir_check)).resolve() / ".session_id"

    # --version 选定的 spec 目录必须存在. 在进重试循环前 fail-fast:
    # 注意 run() 里 sys.exit(字符串) 的消息会被下面 except SystemExit 吞掉,
    # 所以这里在 main 早校验, 直接 print 到 stderr 再 exit(1).
    spec_dir_check = workdir_check / args.spec_name
    if not spec_dir_check.is_dir():
        avail = sorted(p.name for p in workdir_check.glob("spec*") if p.is_dir())
        print(
            f"❌ spec dir not found: {spec_dir_check}\n"
            f"   (--version {args.version!r} → 期望目录 '{args.spec_name}/')\n"
            f"   可用版本: {avail}",
            file=sys.stderr,
        )
        sys.exit(1)

    for attempt in range(max_retries + 1):
        try:
            asyncio.run(run(args))
            return  # 成功完成
        except KeyboardInterrupt:
            # session_id 在 init 时就落盘了, 已写到磁盘的产物全部保留.
            # 加 --resume 重启即可接着跑.
            print(
                f"\n⚠️  中断 (Ctrl-C). {args.out_dir}/ 产物已保留.\n"
                f"   恢复: python run_procedure_dsl.py {args.source} "
                f"--out-dir {args.out_dir} --resume",
                file=sys.stderr,
            )
            sys.exit(130)
        except SystemExit as e:
            code = e.code if isinstance(e.code, int) else (1 if e.code else 0)
            # exit 2 = is_error (stream timeout 之类 transient), 可重试
            if code == 2 and attempt < max_retries:
                wait_s = 10 * (2 ** attempt)   # 10s / 20s / 40s
                print(
                    f"\n⚠️  attempt {attempt+1}/{max_retries+1} exit={code} "
                    f"(可能 stream idle timeout). 等 {wait_s}s 后 --resume 重试...",
                    file=sys.stderr,
                )
                time.sleep(wait_s)
                # 强制 --resume (前提是 session_id 已落盘 — init 阶段就写了, 通常都在)
                if sid_file_check.exists():
                    args.resume = True
                else:
                    print(f"⚠️  {sid_file_check} 不存在, fresh restart 而非 resume",
                          file=sys.stderr)
                continue
            # 不重试: exit 0 (成功) / exit 1 (SDK 错) / exit 3 (RateLimit) / 用尽 retries
            sys.exit(code)

    # 用尽 max_retries 后还到这里 (按 for-else 语义实际不会, 但保险)
    print(f"\n❌ 用尽 {max_retries} 次重试.", file=sys.stderr)
    sys.exit(2)


if __name__ == "__main__":
    main()