feat(bench): multi-language capability benchmark + model picker

Add bench-lang.py + bench/ package: a third benchmark axis answering
"which open-source model is best for my workflow?" across Python,
JavaScript, Go, Rust and Bash.

- MultiPL-E (Go/Rust/JS/Bash) + original HumanEval (Python), loaded via
  the HF datasets-server REST API with on-disk cache — no datasets/
  pyarrow dependency.
- Completions go straight to Ollama /api/generate with raw=True so
  instruct models continue the code instead of replying with prose.
- Code runs in rootless, network-less podman (safe default) with a
  host-toolchain fallback; pass@1/pass@k via the HumanEval estimator.
- run/score separation: results persist to a scorecard JSON, then
  `pick --workflow ops` re-ranks without re-running any model.
- Extensible: a new language is one Lang entry; a new workflow is one
  block in workflows.json.

Also fix a --runs grant-persistence bug in bench-sandbox.py: the grant
leaked across runs, invalidating the L0-nogrant refusal test on runs 2+.
Each run now revokes the ACL and starts ungranted.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

hh/scripts/bench/datasets.py

@@ -0,0 +1,64 @@
+"""Dependency-free problem loader.
+
+Pulls rows from the Hugging Face datasets-server REST API (plain `requests`, no
+`datasets`/`pyarrow`) and caches them on disk so repeated benchmark runs are
+offline and fast. One JSON file per (dataset, config), under ~/.cache.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import requests
+
+_API = "https://datasets-server.huggingface.co/rows"
+_CACHE = Path.home() / ".cache" / "hh-bench" / "datasets"
+_PAGE = 100  # datasets-server caps `length` at 100 rows per call
+
+
+def _cache_path(dataset: str, config: str, split: str) -> Path:
+    safe = f"{dataset}__{config}__{split}".replace("/", "_")
+    return _CACHE / f"{safe}.json"
+
+
+def load(dataset: str, config: str, split: str = "test",
+         limit: int | None = None, refresh: bool = False) -> list[dict]:
+    """Return a list of row dicts for one dataset config.
+
+    Cached after first fetch. `limit` slices the returned list (the full set is
+    still cached). `refresh` forces a re-download.
+    """
+    cp = _cache_path(dataset, config, split)
+    if cp.exists() and not refresh:
+        rows = json.loads(cp.read_text())
+    else:
+        rows = _download(dataset, config, split)
+        cp.parent.mkdir(parents=True, exist_ok=True)
+        cp.write_text(json.dumps(rows))
+    return rows[:limit] if limit else rows
+
+
+def _download(dataset: str, config: str, split: str) -> list[dict]:
+    rows: list[dict] = []
+    offset = 0
+    while True:
+        r = requests.get(_API, params={
+            "dataset": dataset, "config": config, "split": split,
+            "offset": offset, "length": _PAGE}, timeout=60)
+        r.raise_for_status()
+        payload = r.json()
+        batch = payload.get("rows", [])
+        if not batch:
+            break
+        rows.extend(item["row"] for item in batch)
+        total = payload.get("num_rows_total")
+        offset += len(batch)
+        if total is not None and offset >= total:
+            break
+        if len(batch) < _PAGE:
+            break
+    if not rows:
+        raise RuntimeError(
+            f"no rows for {dataset}/{config}/{split} — check the config name")
+    return rows