oshwhub: dump full listing index (33,695 projects) for batch sizing

Probed listing API and learned: total field is exposed (Pro=21,202 / Std=12,493),
pageSize accepts >=1000 (full corpus = 35 requests / 71s), sort param is silently
ignored. Dump all listings via scripts/dump_listing_index.py to local jsonl so
downstream batch-selection no longer hits the API.

Why: needed quantitative anchors before scaling Pro batch beyond top-5. License
is detail-page only (~19h serial scan), so we want to filter on grade/like
*locally* first to shortlist before paying that cost. Quality-tier counts now
known: A-tier (grade>=3 & like>=10) = 2,806 across both origins.

- scripts/dump_listing_index.py: one-shot scraper, polite QPS, streams to jsonl
- docs/sources/oshwhub_listing_full.md: human-readable report with growth
  trends, quality tiers, owner concentration, and storage-budget anchors

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

scripts/dump_listing_index.py

@@ -0,0 +1,126 @@
+"""Dump full oshwhub listing index for both origins to a local jsonl.
+
+只抓 listing API，不抓详情页、不抓附件、不抓工程源。结果落
+`data/state/oshwhub_listing_full.jsonl`，每行一条 listing 项。
+
+Usage:
+    uv run python scripts/dump_listing_index.py
+    uv run python scripts/dump_listing_index.py --page-size 500 --sleep 0.5
+
+API 注意：
+- `sort` 参数被服务端静默忽略，无论传啥都返回隐式排序（grade desc → 质量 desc）
+- 默认 `origin` 是 std；要 Pro 必须显式 `origin=pro`
+- `pageSize` 实测 ≥1000 都接受，单次响应体 ~1 MB
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import statistics as st
+import sys
+import time
+from collections import Counter
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
+from crawlers.oshwhub.crawler import list_projects, make_client  # noqa: E402
+
+ORIGINS = ("pro", "std")
+DEFAULT_OUT = Path("data/state/oshwhub_listing_full.jsonl")
+
+
+def dump_origin(client, origin: str, page_size: int, sleep: float, sink) -> int:
+    # First call to learn `total` / `totalPage`.
+    res = list_projects(client, page=1, page_size=page_size, origin=origin)
+    total = res["total"]
+    n_pages = math.ceil(total / page_size)
+    written = 0
+    for it in res["lists"]:
+        sink.write(json.dumps(it, ensure_ascii=False) + "\n")
+        written += 1
+    print(
+        f"[{origin}] total={total}  pages={n_pages}  pageSize={page_size}",
+        flush=True,
+    )
+    print(f"  page  1/{n_pages}: {len(res['lists'])} items", flush=True)
+    for p in range(2, n_pages + 1):
+        time.sleep(sleep)
+        res = list_projects(client, page=p, page_size=page_size, origin=origin)
+        for it in res["lists"]:
+            sink.write(json.dumps(it, ensure_ascii=False) + "\n")
+            written += 1
+        print(f"  page {p:>2}/{n_pages}: {len(res['lists'])} items", flush=True)
+    if written != total:
+        print(
+            f"  WARN: wrote {written} but server said total={total} "
+            f"(diff={total - written})",
+            file=sys.stderr,
+        )
+    return written
+
+
+def summarize(path: Path) -> None:
+    by_origin: dict[str, list[dict]] = {}
+    with path.open() as f:
+        for ln in f:
+            it = json.loads(ln)
+            by_origin.setdefault(it.get("origin") or "?", []).append(it)
+    print("\n===== summary =====")
+    print(f"file: {path}  size={path.stat().st_size / 1024 / 1024:.1f} MB")
+    for origin, items in sorted(by_origin.items()):
+        if not items:
+            continue
+        likes = sorted(((i.get("count") or {}).get("like") or 0) for i in items)
+        views = sorted(((i.get("count") or {}).get("views") or 0) for i in items)
+        grades = Counter(i.get("grade") for i in items)
+        # date range from created_at
+        dates = sorted(i.get("created_at") or "" for i in items if i.get("created_at"))
+        date_min = dates[0][:10] if dates else "?"
+        date_max = dates[-1][:10] if dates else "?"
+        print(f"\n[{origin}] n={len(items)}")
+        print(
+            f"  likes:  median={likes[len(likes) // 2]}  "
+            f"p90={likes[int(len(likes) * 0.9)]}  "
+            f"p99={likes[int(len(likes) * 0.99)]}  max={likes[-1]}"
+        )
+        print(
+            f"  views:  median={views[len(views) // 2]}  "
+            f"p90={views[int(len(views) * 0.9)]}  max={views[-1]}"
+        )
+        print(f"  grade:  {dict(sorted(grades.items(), key=lambda kv: -(kv[0] or 0)))}")
+        print(f"  dates:  {date_min} .. {date_max}")
+        # Quality tiers (handy for batch sizing)
+        tier_grade3 = sum(1 for i in items if (i.get("grade") or 0) >= 3)
+        tier_like10 = sum(
+            1
+            for i in items
+            if ((i.get("count") or {}).get("like") or 0) >= 10
+            and (i.get("grade") or 0) >= 3
+        )
+        print(f"  tier:   grade>=3: {tier_grade3}   grade>=3 & like>=10: {tier_like10}")
+
+
+def main(argv: list[str] | None = None) -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--out", type=Path, default=DEFAULT_OUT)
+    ap.add_argument("--page-size", type=int, default=1000)
+    ap.add_argument("--sleep", type=float, default=1.0, help="seconds between page calls")
+    args = ap.parse_args(argv)
+
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    print(f"writing -> {args.out}")
+    t0 = time.time()
+    with args.out.open("w") as sink, make_client() as client:
+        total = 0
+        for origin in ORIGINS:
+            total += dump_origin(client, origin, args.page_size, args.sleep, sink)
+            sink.flush()
+    print(f"\ndone: {total} items in {time.time() - t0:.1f}s")
+    summarize(args.out)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())