crawler: --skip-ext + --max-source-mb gates for batch-50 expansion

Two CLI gates needed before scaling Pro batch beyond top-5:

--skip-ext mp4,qt,mov  (attachment filter)
  Skips video extensions in attachment download. Phase 1 measurements
  showed mp4+qt occupy ~54% of attachment storage. Entry still recorded
  in metadata.json with skipped:ext:<token> so we can re-fetch later if
  the policy changes. Honors both server-declared `ext` and filename
  suffix, case-insensitively.

--max-source-mb N  (Pro source size cap)
  Trips inside the chain replay loop on encrypted-blob total. On trip:
  raise ProjectOversizeError, wipe partial source/, append a row to
  data/state/oshwhub_pro_oversize.jsonl. Lets us shortlist 50+ Pro
  projects without one X86-board-class outlier (~500 MB) blowing the
  LFS budget. Std and Pro 2.x legacy are not capped (both <2 MB in
  sample).

Verified:
  - cap=0 trips on first blob (1.2 MB), source/ wiped, state recorded
  - cap=100 runs full ESP-VoCat (7.5 MB plain, 278 docs)
  - skip-ext microtest: 8/8 cases (case-insensitive, declared/suffix
    fallback, empty-token edge cases)

Plan + frozen candidate list for the next 50 projects:
  - docs/plans/oshwhub_batch50.md
  - data/state/oshwhub_batch50_candidates.jsonl (gitignore exception added)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

.gitignore

@@ -5,6 +5,8 @@ data/state/*
 !data/state/.gitkeep
 # 例外：oshwhub 全量 listing 索引快照入库（28 MB jsonl，可重抓但要钉个版本）
 !data/state/oshwhub_listing_full.jsonl
+# 例外：扩抓批次的"冻结候选清单"——计划文档以这份为准，可重生成
+!data/state/oshwhub_batch50_candidates.jsonl
 
 # data/raw 入库（工程二进制走 LFS，见 .gitattributes）
 

crawlers/oshwhub/crawler.py

@@ -19,6 +19,7 @@ import hashlib
 import html as _html
 import json
 import re
+import shutil
 import sys
 import time
 import urllib.parse
@@ -428,11 +429,42 @@ def _order_history_chain(chain: list[dict]) -> list[dict]:
     return ordered
 
 
+class ProjectOversizeError(Exception):
+    """Raised when a Pro project's chain replay would exceed the configured cap.
+
+    `cap_mb` is the trip threshold; `bytes_so_far` is the *encrypted blob* total
+    accumulated when we tripped (pre-decompression, pre-partition).
+    """
+
+    def __init__(self, bytes_so_far: int, cap_mb: int) -> None:
+        super().__init__(
+            f"oversize: blob bytes {bytes_so_far // 1024 // 1024} MB > cap {cap_mb} MB"
+        )
+        self.bytes_so_far = bytes_so_far
+        self.cap_mb = cap_mb
+
+
+def _record_oversize(out_root: Path, uuid: str, err: ProjectOversizeError) -> None:
+    """Append one row to data/state/oshwhub_pro_oversize.jsonl for later review."""
+    state_path = Path("data/state/oshwhub_pro_oversize.jsonl")
+    state_path.parent.mkdir(parents=True, exist_ok=True)
+    row = {
+        "uuid": uuid,
+        "out_root": str(out_root),
+        "bytes_so_far": err.bytes_so_far,
+        "cap_mb": err.cap_mb,
+        "ts": datetime.now(timezone.utc).isoformat(),
+    }
+    with state_path.open("a") as f:
+        f.write(json.dumps(row, ensure_ascii=False) + "\n")
+
+
 def fetch_pro_source(
     pro_client: httpx.Client,
     project_uuid: str,
     proj_dir: Path,
     sleep: float = SLEEP_PRO,
+    max_source_mb: int | None = None,
 ) -> dict:
     """Dispatcher: pick modern (3.x branch+EPRO2) vs legacy (2.x v2/documents/lists)
     based on whether project meta contains a non-null branch_uuid.
@@ -440,11 +472,16 @@ def fetch_pro_source(
     Pro 3.x stores in git-style branch+history with AES-encrypted EPRO2 streams;
     Pro 2.x predates that and uses Std-style per-doc dataStr served from
     /api/v2/documents/lists. See docs/sources/easyeda_pro_source.md §1.1.
+
+    `max_source_mb` only gates modern-path projects (legacy is always tiny: <2 MB
+    in our 2/2 sample) and trips before any blob is written to disk past the cap.
     """
     proj = _pro_get_json(pro_client, f"{PRO_API}/projects/{project_uuid}", project_uuid)
     time.sleep(sleep)
     if proj.get("branch_uuid"):
-        return _fetch_pro_modern(pro_client, project_uuid, proj, proj_dir, sleep)
+        return _fetch_pro_modern(
+            pro_client, project_uuid, proj, proj_dir, sleep, max_source_mb=max_source_mb
+        )
     return _fetch_pro_legacy(pro_client, project_uuid, proj, proj_dir, sleep)
 
 
@@ -454,6 +491,7 @@ def _fetch_pro_modern(
     proj: dict,
     proj_dir: Path,
     sleep: float = SLEEP_PRO,
+    max_source_mb: int | None = None,
 ) -> dict:
     """Modern Pro 3.x fetcher: full history chain, AES-GCM decrypted, gunzipped,
     and partitioned into per-document EPRO2 streams.
@@ -513,11 +551,19 @@ def _fetch_pro_modern(
     cur_doc: str | None = None
     bytes_blob_total = 0
     bytes_plain_total = 0
+    cap_bytes = max_source_mb * 1024 * 1024 if max_source_mb is not None else None
     for h in ordered:
         blob_r = pro_client.get(h["dataStrUrl"], headers={"path": project_uuid})
         blob_r.raise_for_status()
         blob = blob_r.content
         bytes_blob_total += len(blob)
+        # Trip cap on the encrypted blob total. Hits *after* the offending
+        # download, but before we decrypt/gunzip/partition (those scale with
+        # plain bytes which is even larger). Wipe any partial source/ so disk
+        # doesn't accumulate junk on multi-project runs.
+        if cap_bytes is not None and bytes_blob_total > cap_bytes:
+            shutil.rmtree(src_dir, ignore_errors=True)
+            raise ProjectOversizeError(bytes_blob_total, max_source_mb)
         if len(blob) < 16:
             raise RuntimeError(f"history {h['uuid']} blob too short ({len(blob)} B)")
         ct, tag = blob[:-16], blob[-16:]
@@ -849,6 +895,8 @@ def crawl_one(
     fetch_files: bool = True,
     source_client: httpx.Client | None = None,
     pro_source_client: httpx.Client | None = None,
+    skip_exts: set[str] | None = None,
+    max_source_mb: int | None = None,
 ) -> CrawlResult:
     uuid = list_item["uuid"]
     path = list_item["path"]
@@ -916,6 +964,14 @@ def crawl_one(
             "size": a.get("size"),
             "md5": a.get("md5"),
         }
+        # ext gate: declared `ext` first, fall back to filename suffix. Lower-case
+        # compare; entry kept in metadata so we can re-fetch later if policy changes.
+        ext_token = (a.get("ext") or Path(safe_name).suffix.lstrip(".")).lower()
+        if skip_exts and ext_token in skip_exts:
+            entry["skipped"] = f"ext:{ext_token}"
+            skipped.append(f"{name}: ext:{ext_token}")
+            files_meta.append(entry)
+            continue
         if fetch_files:
             try:
                 size, sha = download_to(client, file_url, local_path)
@@ -950,11 +1006,17 @@ def crawl_one(
     origin = list_item.get("origin")
     if origin == "pro" and pro_source_client is not None:
         try:
-            src_meta = fetch_pro_source(pro_source_client, uuid, proj_dir)
+            src_meta = fetch_pro_source(
+                pro_source_client, uuid, proj_dir, max_source_mb=max_source_mb
+            )
             print(
                 f"  pro source: {len(src_meta.get('source_documents', []))} docs, "
                 f"editor={src_meta.get('editor_version')}"
             )
+        except ProjectOversizeError as e:
+            print(f"  pro source SKIPPED (oversize): {e}", file=sys.stderr)
+            skipped.append(f"pro_source: oversize ({e.bytes_so_far // 1024 // 1024} MB > {e.cap_mb} MB)")
+            _record_oversize(out_root, uuid, e)
         except Exception as e:  # noqa: BLE001
             print(f"  pro source FAIL: {e}", file=sys.stderr)
             skipped.append(f"pro_source: {e}")
@@ -1088,7 +1150,27 @@ def main(argv: list[str] | None = None) -> int:
         default=PRO_COOKIE_PATH_DEFAULT,
         help="path to file with Cookie header for pro.lceda.cn",
     )
+    ap.add_argument(
+        "--skip-ext",
+        type=str,
+        default=None,
+        help="comma-separated list of attachment extensions to skip (e.g. mp4,qt,mov). "
+             "Saves ~30-50%% LFS storage on average. Entry still recorded in metadata.json "
+             "with skipped:ext:<token> so we can re-fetch later.",
+    )
+    ap.add_argument(
+        "--max-source-mb",
+        type=int,
+        default=None,
+        help="skip Pro modern projects whose chain blob total exceeds N MB. "
+             "Trips inside the chain loop, wipes partial source/, records to "
+             "data/state/oshwhub_pro_oversize.jsonl. No effect on Std or Pro 2.x legacy.",
+    )
     args = ap.parse_args(argv)
+    skip_exts: set[str] | None = (
+        {x.strip().lower().lstrip(".") for x in args.skip_ext.split(",") if x.strip()}
+        if args.skip_ext else None
+    )
 
     n_target = args.limit if args.limit is not None else args.top
     args.out.mkdir(parents=True, exist_ok=True)
@@ -1099,7 +1181,10 @@ def main(argv: list[str] | None = None) -> int:
         return _run_backfill_source(args.out, only_uuids=args.uuids)
     if args.backfill_pro_source:
         return _run_backfill_pro_source(
-            args.out, only_uuids=args.uuids, cookie_path=args.pro_cookie
+            args.out,
+            only_uuids=args.uuids,
+            cookie_path=args.pro_cookie,
+            max_source_mb=args.max_source_mb,
         )
 
     with make_client() as client:
@@ -1149,6 +1234,8 @@ def main(argv: list[str] | None = None) -> int:
                         fetch_files=not args.no_files,
                         source_client=source_client_ctx,
                         pro_source_client=pro_source_client_ctx,
+                        skip_exts=skip_exts,
+                        max_source_mb=args.max_source_mb,
                     )
                     print(
                         f"  OK: {r.files_count} files, {r.bytes_total / 1024 / 1024:.1f} MB "
@@ -1217,6 +1304,7 @@ def _run_backfill_pro_source(
     out_root: Path,
     only_uuids: str | None = None,
     cookie_path: str = PRO_COOKIE_PATH_DEFAULT,
+    max_source_mb: int | None = None,
 ) -> int:
     """Walk per-project dirs in out_root, fetch Pro source for origin=pro projects.
 
@@ -1253,7 +1341,13 @@ def _run_backfill_pro_source(
             meta = json.loads(meta_path.read_text(encoding="utf-8"))
             print(f"[{i}/{len(targets)}] {uuid}  ({meta.get('title', '?')})")
             try:
-                src_meta = fetch_pro_source(pro_client, uuid, proj_dir)
+                src_meta = fetch_pro_source(
+                    pro_client, uuid, proj_dir, max_source_mb=max_source_mb
+                )
+            except ProjectOversizeError as e:
+                print(f"  SKIPPED (oversize): {e}", file=sys.stderr)
+                _record_oversize(out_root, uuid, e)
+                continue
             except Exception as e:  # noqa: BLE001
                 print(f"  FAIL: {e}", file=sys.stderr)
                 continue

data/raw/oshwhub/ba64bd6f1c9c467ba3b674a54943557d/source/manifest.json

@@ -2,7 +2,7 @@
   "project_uuid": "ba64bd6f1c9c467ba3b674a54943557d",
   "branch_uuid": "ef5f58bd0f1245b0a808c07e541a1b5c",
   "head_uuid": "764dd8b722a44914a915493277e204c9",
-  "fetched_at": "2026-04-28T16:06:55.434479+00:00",
+  "fetched_at": "2026-04-28T16:23:06.697278+00:00",
   "editor_version": "3.2.91",
   "chain_length": 12,
   "blob_bytes_total": 1195716,

data/state/oshwhub_batch50_candidates.jsonl

@@ -0,0 +1,50 @@
+{"owner": {"nickname": "pengzhihui", "avatar": "/image/avatar-default.png", "team": false, "uuid": "07d6c15e714c4f6aaba8aa0c3737379e", "username": "pengzhihui"}, "thumb": "//image.lceda.cn/pullimage/3X5wPID5TfY2QATyzcLAeXExhakaDHZSGnFrsTC3.jpeg", "origin": "pro", "count": {"fork": 0, "star": 3157, "like": 2137, "watch": 0, "views": 329510}, "created_at": "2022-08-20T04:18:00.000Z", "uuid": "c4aad9461c3941228a784944eb500757", "tags": [], "path": "pengzhihui/b11afae464c54a3e8d0f77e1f92dc7b7", "public": true, "updated_at": "2026-02-24T03:18:19.000Z", "members": ["07d6c15e714c4f6aaba8aa0c3737379e"], "grade": 4, "publish": true, "name": "【瀚文】HelloWord-Keyboard", "oshwhub_publish_at": "2023-08-28T08:11:34.000Z", "introduction": "`瀚文` 智能键盘是一把我为自己使用需求设计的 多功能、模块化 机械键盘。", "comments_count": 586}
+{"owner": {"nickname": "Sngels_wyh", "avatar": "//image.lceda.cn/avatars/2024/12/s9etBtOA1q39LNLVZsfmkSrhj2DlgmIL2wFnv7f1.webp", "team": false, "uuid": "f5e5983cd8e94ba3b7f01b6e21d709b8", "username": "sngelswyh"}, "thumb": "https://image.lceda.cn/oshwhub/pullImage/35077647fee7471cb3f2dacc50ec3b37.jpg", "origin": "pro", "count": {"fork": 0, "star": 2766, "like": 1525, "watch": 0, "views": 362182}, "created_at": "2024-12-15T08:48:43.151Z", "uuid": "d865cb8992f1437ca728ed11b9ec9421", "tags": [], "path": "sngelswyh/stm32-smart-desktop-pet", "public": true, "updated_at": "2026-03-29T11:08:51.451Z", "members": ["f5e5983cd8e94ba3b7f01b6e21d709b8"], "grade": 3, "publish": true, "name": "STM32智能桌面宠物", "oshwhub_publish_at": "2026-03-30T03:13:02.395Z", "introduction": "可以进行一些简单的交流互动，立正，前进，后退，左转，右转，摇尾巴，趴下，蹲下，睡觉，向前跳，打招呼，伸懒腰等，一般用于放置桌面上。语音可以自定义设置，需要到智能公元网站自行配置。", "comments_count": 698}
+{"owner": {"nickname": "不吃油炸鸡", "avatar": "//image.lceda.cn/avatars/2023/3/78VoBEi9LuHrNvVduuFevnBfZJysXc0Awt3RxNPi.png", "team": false, "uuid": "f7bcdbf6b47c41bdb9ee2f15c10033a9", "username": "no_chicken"}, "thumb": "https://image.lceda.cn/oshwhub/pullImage/532f00b2d581404bbce37aedaa31ab45.jpeg", "origin": "pro", "count": {"fork": 0, "star": 3422, "like": 1397, "watch": 0, "views": 361711}, "created_at": "2023-05-06T12:09:09.000Z", "uuid": "da55d50b394b4553bbcb799dc27a7e63", "path": "no_chicken/zhi-neng-shou-biao-OV-Watch_V2.2", "public": true, "updated_at": "2026-03-20T10:38:26.000Z", "members": ["f7bcdbf6b47c41bdb9ee2f15c10033a9"], "grade": 4, "publish": true, "name": "智能手表OV-Watch", "oshwhub_publish_at": "2025-12-29T03:42:25.000Z", "introduction": "功能包括日历、计算器、秒表、NFC、蓝牙无线升级、与手机传信息、抬腕亮屏、心率检测、环境温湿度检测、指南针、海拔测量等功能。已经尽量满足现在智能手表的功能，后期大家可以继续添加修改很多东西~", "comments_count": 374}
+{"owner": {"nickname": "小O和小Q", "avatar": "//image.lceda.cn/pullimage/vPyLIOw60BN9Qi0mLwe2Dm5rZApORWMzFKMmLfvu.jpeg", "team": false, "uuid": "9ff80e24b505422a87c592cd4c6efb69", "username": "littleoandlittleq"}, "thumb": "//image.lceda.cn/pullimage/jDqTDFPFZtipYNYB0YLY3eLCALiFStAnJA1XThW3.jpeg", "origin": "pro", "count": {"fork": 0, "star": 2257, "like": 994, "watch": 0, "views": 218248}, "created_at": "2022-09-12T11:49:10.000Z", "uuid": "977b637a85fa44fbb5d4214373576a04", "path": "littleoandlittleq/bian-xie-jia-re-tai", "public": true, "updated_at": "2026-04-03T01:41:10.000Z", "members": ["9ff80e24b505422a87c592cd4c6efb69"], "grade": 4, "publish": true, "name": "[PD协议 | 高颜值]mini加热台", "oshwhub_publish_at": "2023-07-27T01:58:31.000Z", "introduction": "超迷你的加热台，PD20V供电，加热面大小5.6*5.6cm，可以通过两个按键设置参数。", "comments_count": 728}
+{"owner": {"nickname": "乐鑫小铁匠", "avatar": "//image.lceda.cn/avatars/2023/7/DgnM9iWggXtWZa70BQwJnpb1x7P2etO5s7i6vqxp.png", "team": false, "uuid": "16555ec60452466ea4dbaa7c6b92cd89", "username": "esp-college"}, "thumb": "//image.lceda.cn/pullimage/YyPkhuGTNedxg6yM3fkJBovQB2b56pCCJb27zQjN.png", "origin": "pro", "count": {"fork": 0, "star": 2135, "like": 837, "watch": 0, "views": 304205}, "created_at": "2024-10-15T03:45:10.000Z", "uuid": "91fd41224cd74ffd8410bc77b216693b", "tags": [], "path": "esp-college/esp-sparkbot", "public": true, "updated_at": "2025-11-11T10:24:51.000Z", "members": ["f7922f3e92344fd69adf06f12263ee47", "a1aad839debe49d9b890a35890f849c6", "16555ec60452466ea4dbaa7c6b92cd89"], "grade": 3, "publish": true, "name": "ESP-SparkBot", "oshwhub_publish_at": "2025-03-12T05:42:19.000Z", "introduction": "ESP-SparkBot 基于 ESP32-S3，融合语音交互、图像识别与多媒体娱乐，可变身遥控小车、玩转本地 AI，支持大模型对话、实时视频传输和高清视频投屏，性能强大，乐趣无限！", "comments_count": 343}
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docs/plans/oshwhub_batch50.md

@@ -0,0 +1,196 @@
+# oshwhub 扩抓批 50：执行计划
+
+**创建**：2026-04-29
+**目标**：在已落库的 15 项（5 Pro + 10 Std）基础上，再抓 50 个高质量项目
+**候选清单**：`data/state/oshwhub_batch50_candidates.jsonl`（50 行）
+**承接**：`docs/sources/oshwhub_listing_full.md`（全量索引盘点）
+
+---
+
+## 1. 目标
+
+- 把 corpus 从 15 项扩到 65 项，覆盖 Pro + Std + Pro 3.x + Pro 2.x 各形态
+- 在 LFS 1 TB 红线内，先做 ~3-4 GB 量级的中等规模验证
+- 结束后用 license 白名单评估这 50 项里有多少能进 Forge 投影
+- 暴露 ≥ top-5 才会出现的 corner case（例如：超大 Pro 项目、私有附件、跨账号 fork）
+
+**显式不做**：详情页全量扫描（19h 工作量，留待后续按 A 档全池一次性做）；下游 KiCad 转换（Phase 3 还在做）。
+
+---
+
+## 2. 候选池筛选标准
+
+数据源：`data/state/oshwhub_listing_full.jsonl`（33,695 项全量 listing 索引）
+
+**硬过滤**：
+- `grade >= 3 AND like >= 10`（A 档）
+- `uuid` 不在已抓 15 项内
+
+**排序**：复合质量打分
+```
+score = like*3 + star + fork*2 + views/100 + comments_count*2 + grade*50
+```
+
+**多样性约束**：
+- 单作者上限 2 个项目（避免 `course-examples` / `45coll` 等高产账号占满）
+- Pro / Std 25:25 平衡（Pro 是新平台增长焦点，Std 是 7 年质量积累）
+
+---
+
+## 3. 选出的 50 项概况
+
+| 维度 | 值 |
+|---|---|
+| 总数 | 50（25 Pro + 25 Std）|
+| 唯一作者 | 43 |
+| Max 单作者 | 2 |
+| like 区间 | min=246 / median=554 / max=2137 |
+| view 区间 | min=54k / median=117k / max=362k |
+| grade 分布 | 4: 33 项, 3: 17 项 |
+
+**Pro 头部**（前 5）：HelloWord-Keyboard 2137 likes / STM32 桌面宠物 / OV-Watch 智能手表 / mini 加热台 / ESP-SparkBot
+
+**Std 头部**（前 5）：触摸调光雪花灯 1036 likes / 桌面可调电源 / 立创 EDA PCB 直尺 / 自平衡莱洛三角 / PN532 NFC
+
+完整名单见 `data/state/oshwhub_batch50_candidates.jsonl`。
+
+---
+
+## 4. 执行步骤
+
+### Step 0 — 候选池冻结 ✅
+
+`data/state/oshwhub_batch50_candidates.jsonl` 已生成，作为本次批的"事实档案"。后续就算重排序也以这份为准。
+
+### Step 1 — 详情页抓 license（~5 min，QPS=0.5）
+
+只对这 50 项扫详情页，提取 license / 完整 attachments 列表 / 原始描述。
+不在这一步下载附件、不下源工程，**只是获取每项的 metadata 全集**。
+
+```bash
+PYTHONUNBUFFERED=1 uv run python -u -m crawlers.oshwhub \
+  --uuids "$(jq -r .uuid data/state/oshwhub_batch50_candidates.jsonl | paste -sd,)" \
+  --no-files \
+  --out data/raw/oshwhub
+```
+
+> 目前 `crawl_one` 已经会落 metadata.json + description.md + cover；`--no-files` 跳过附件。
+
+**预期产出**：50 个 `data/raw/oshwhub/<uuid>/metadata.json`，含 license 字段。
+
+### Step 2 — license 分布盘点
+
+写个一次性脚本读 50 份 metadata.json，统计 license 分布。**这一步只做盘点不做过滤**——按 CLAUDE.md "研究用、不分发" 原则，所有 license 都入库。下游 Forge 投影时再用白名单过滤。
+
+```bash
+uv run python - <<'EOF'
+import json, glob, collections
+licenses = collections.Counter()
+for p in glob.glob("data/raw/oshwhub/*/metadata.json"):
+    m = json.load(open(p))
+    if m["uuid"] in batch_50_uuids:
+        licenses[m.get("license") or "<none>"] += 1
+print(licenses)
+EOF
+```
+
+预期会有大量 NC-SA / GPL（参考已抓 5 Pro：5/5 都是 GPL/NC-SA）。
+
+### Step 3 — Pro 子集预检 + 抓源（~30-60 min）
+
+25 个 Pro 项目里要先判 3.x vs 2.x（`branch_uuid` 是否 null），然后对应走 modern / legacy 路径：
+
+```bash
+PYTHONUNBUFFERED=1 uv run python -u -m crawlers.oshwhub \
+  --backfill-pro-source \
+  --uuids "$(jq -r 'select(.origin=="pro") | .uuid' \
+              data/state/oshwhub_batch50_candidates.jsonl | paste -sd,)" \
+  --pro-cookie ~/.secrets/pro-lceda-cookie-header.txt
+```
+
+**时间预算**（已应用 chain replay sleep 优化，commit `1e06ba6`）：
+- 小型项目（chain ~10）：~30s 各
+- 中型（chain ~30-50）：~1-2 min 各
+- 大型（chain ~100+）：~3-5 min 各
+- 超大（X86 量级 chain ~700）：~3-5 min 各（CDN 部分降到 0.2s/req）
+
+按已抓 5 项观察，预期 25 项里：
+- ~22 个 Pro 3.x（chain replay）
+- ~3 个 Pro 2.x（plaintext dataStr）
+
+### Step 4 — Std 子集抓源（~30 min）
+
+```bash
+PYTHONUNBUFFERED=1 uv run python -u -m crawlers.oshwhub \
+  --backfill-source \
+  --uuids "$(jq -r 'select(.origin=="std") | .uuid' \
+              data/state/oshwhub_batch50_candidates.jsonl | paste -sd,)"
+```
+
+每项 ~30s（QPS=0.2 / 5s sleep × 几个 API 调用），25 项 ≈ 12-15 min。
+
+### Step 5 — 附件下载（按需，可选）
+
+附件（Gerber / STEP / PDF / 视频）默认存 LFS，但视频占大头（之前 Phase 1 实测 mp4+qt 占 54%）。
+**建议加 `--skip-ext mp4,qt,mov`** 节省 30-50% 体积。
+
+```bash
+PYTHONUNBUFFERED=1 uv run python -u -m crawlers.oshwhub \
+  --uuids "$(jq -r .uuid data/state/oshwhub_batch50_candidates.jsonl | paste -sd,)" \
+  --out data/raw/oshwhub
+  # 注：当前 crawler 没有 --skip-ext 选项；要加得改 crawl_one；如果偷懒就先全抓再人工删
+```
+
+> 这一步没那么紧迫，可以挪到 Step 3/4 完成后单独跑。
+
+### Step 6 — 验收 + 记录
+
+跑 `scripts/build_index.py` 重建 `projects.md`，更新 `log.md`，把统计信息写到 `docs/plans/oshwhub_batch50.md` 末尾"实施结果"段。
+
+---
+
+## 5. 资源预算
+
+| 项 | 估算 | 备注 |
+|---|---:|---|
+| API 调用 | ~600 次 | 50 详情 + 25×3 Pro meta + 25 Std API + chain blobs |
+| 时间 | **~1.5-2 小时** | 远低于之前估算的 19h（因为只 50 项 + sleep 优化）|
+| 存储（源工程）| ~1.5 GB | Pro 平均 30 MB、Std 平均 15 MB |
+| 存储（附件，估）| 2-3 GB | 含视频；不含视频估 1 GB |
+| **总 LFS 增量** | **~3-4 GB** | 远低于现有 LFS 余量 |
+| 网络下行 | ~4 GB | 大部分走 LFS |
+
+距离 200 GB"对象存储迁移决策点"还很远。
+
+---
+
+## 6. 风险与预案
+
+| 风险 | 概率 | 影响 | 预案 |
+|---|---|---|---|
+| Pro cookie 过期半路 | 中 | 卡住 Pro 抓取 | 重抓 cookie；爬虫已有 `oshwhub_pro_failed.jsonl` 重试机制（**未实现**，现在失败只 stderr）|
+| 单个 Pro 项目超大（X86 量级 ~500 MB） | 中 | LFS 单项膨胀 | 设置 size cap，超过 200 MB 跳过 + 记 state |
+| license 全是 NC-SA → 下游 Forge 投影门槛低 | 高 | Forge 阶段才发现 | 提前在 Step 2 盘点；**本批不做过滤**，只入库 |
+| 某个项目附件含 mp4 视频 → 附件总量爆炸 | 中 | 多消耗 1-2 GB LFS | 加 `--skip-ext mp4,qt,mov` 选项（需小改 crawler）|
+| 抓 50 项过程中触发反爬 / 限流 | 低 | 中途中断 | 已有 5s/req QPS；如果触发，加 jitter；从中断点续 |
+
+---
+
+## 7. 验收标准
+
+- [ ] `data/raw/oshwhub/` 下新增 ≥ 45 个项目目录（允许 ≤ 5 项失败）
+- [ ] 每个新增项目的 `metadata.json` 含 `license`、`source_format`、`source_path`
+- [ ] Pro 25 项里 3.x / 2.x 分类正确（看 `source_format` 字段）
+- [ ] LFS 增量 ≤ 5 GB
+- [ ] `projects.md` 自动重建后包含新 50 项
+- [ ] log.md 顶部一条总结性日志
+
+---
+
+## 8. 后续
+
+成功结束后的下一步候选：
+1. **license 详情扫描全 A 档** —— 把 license 信息覆盖到 2,806 项 A 档候选池，为下一次扩抓提供过滤条件
+2. **扩抓批 200**（按 license 白名单 + 同样多样性策略）
+3. **Pro 工程二进制图（IMAGE/）补抓** —— 当前的真实数据 gap，要不要补取决于下游需求
+4. **风控压测** —— 在某一批做并发提速试验，找 oshwhub / pro.lceda.cn 的限流真上限