diff --git a/website/fetch_pypi_downloads.py b/website/fetch_pypi_downloads.py
new file mode 100644
index 00000000..e79c3620
--- /dev/null
+++ b/website/fetch_pypi_downloads.py
@@ -0,0 +1,94 @@
+#!/usr/bin/env python3
+"""Fetch last-30-day PyPI download counts for README entries via BigQuery.
+
+Shells out to the `bq` CLI (Google Cloud SDK) against the public
+`bigquery-public-data.pypi.file_downloads` table. The table is clustered on
+file.project, so scanned bytes grow with the IN-list: a few dozen names
+prune to tens of GB while the full README (~530 names) balloons to over
+1 TB — past the free tier. Fetch per curation sitting with --names-file
+instead of all at once. Results are cached to data/pypi_downloads.tsv
+(merged with any existing rows); names with no PyPI rows are written as
+NOT_FOUND. Always --dry-run first to check the scan estimate.
+
+Usage: python fetch_pypi_downloads.py [--dry-run] [--names-file FILE]
+"""
+
+import re
+import subprocess
+import sys
+from json import loads
+from pathlib import Path
+
+from readme_parser import parse_readme
+
+DATA_DIR = Path(__file__).parent / "data"
+OUT_FILE = DATA_DIR / "pypi_downloads.tsv"
+README_PATH = Path(__file__).parent.parent / "README.md"
+
+# PyPI normalizes names to lowercase with runs of -, _, . collapsed to -.
+PYPI_NAME_RE = re.compile(r"^[a-z0-9]([a-z0-9-]*[a-z0-9])?$")
+
+
+def normalize(name: str) -> str:
+    return re.sub(r"[-_.]+", "-", name.lower())
+
+
+def collect_names(readme_text: str) -> list[str]:
+    names = set()
+    for group in parse_readme(readme_text):
+        for section in group["categories"]:
+            for entry in section["entries"]:
+                normalized = normalize(entry["name"])
+                if PYPI_NAME_RE.match(normalized):
+                    names.add(normalized)
+    return sorted(names)
+
+
+def fetch(names: list[str], dry_run: bool) -> dict[str, int]:
+    in_list = ", ".join(f"'{name}'" for name in names)
+    query = (
+        "SELECT file.project AS project, COUNT(*) AS downloads "
+        "FROM `bigquery-public-data.pypi.file_downloads` "
+        "WHERE DATE(timestamp) BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) "
+        "AND CURRENT_DATE() "
+        f"AND file.project IN ({in_list}) "
+        "GROUP BY file.project"
+    )
+    cmd = ["bq", "query", "--use_legacy_sql=false", "--format=json"]
+    if dry_run:
+        cmd.append("--dry_run")
+    cmd.append(query)
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    if result.returncode != 0:
+        print(result.stderr, file=sys.stderr)
+        sys.exit(1)
+    if dry_run:
+        print(result.stdout.strip() or result.stderr.strip())
+        sys.exit(0)
+    rows = loads(result.stdout)
+    return {row["project"]: int(row["downloads"]) for row in rows}
+
+
+def main() -> None:
+    dry_run = "--dry-run" in sys.argv
+    if "--names-file" in sys.argv:
+        names_path = Path(sys.argv[sys.argv.index("--names-file") + 1])
+        names = sorted({normalize(line) for line in names_path.read_text().split() if line})
+    else:
+        names = collect_names(README_PATH.read_text())
+    print(f"Querying {len(names)} package names...")
+    counts = fetch(names, dry_run)
+    merged: dict[str, str] = {}
+    if OUT_FILE.exists():
+        for line in OUT_FILE.read_text().splitlines():
+            name, _, value = line.partition("\t")
+            merged[name] = value
+    for name in names:
+        merged[name] = str(counts.get(name, "NOT_FOUND"))
+    OUT_FILE.write_text("\n".join(f"{name}\t{value}" for name, value in sorted(merged.items())) + "\n")
+    found = sum(1 for name in names if name in counts)
+    print(f"Done. {found}/{len(names)} names found on PyPI. Cached to {OUT_FILE}")
+
+
+if __name__ == "__main__":
+    main()