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/completion.py

@@ -0,0 +1,60 @@
+"""Model completions via Ollama's raw /api/generate endpoint.
+
+We deliberately do *not* go through the agent's chat provider here: the
+capability benchmark wants a raw HumanEval-style completion of the function
+prefix (not a chat turn), and we want full control of the read timeout — the
+agent's OllamaProvider hard-codes 120s, which throttles reasoning models. This
+module owns its own timeout knob.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import requests
+
+
+@dataclass
+class Completion:
+    text: str
+    ok: bool
+    error: str | None = None
+    elapsed: float = 0.0
+
+
+def complete(model: str, prompt: str, stop: list[str] | None = None,
+             *, host: str = "http://127.0.0.1:11434", temperature: float = 0.2,
+             num_predict: int = 512, timeout: float = 300.0) -> Completion:
+    """Ask the model to continue `prompt`. Stop tokens are passed to Ollama and
+    re-applied client-side (Ollama strips the stop string, which is what we want
+    — the assembled program must not contain the test's leading token twice)."""
+    import time
+    t0 = time.time()
+    options = {"temperature": temperature, "num_predict": num_predict}
+    if stop:
+        options["stop"] = stop
+    try:
+        # raw=True bypasses the chat template so an instruct model *continues*
+        # the code (HumanEval-style) instead of replying conversationally with
+        # prose + markdown fences, which is what MultiPL-E's assembly expects.
+        r = requests.post(f"{host}/api/generate", json={
+            "model": model, "prompt": prompt, "stream": False,
+            "options": options, "raw": True}, timeout=timeout)
+        r.raise_for_status()
+        text = r.json().get("response", "")
+    except Exception as e:  # noqa: BLE001 — surface as a failed completion row
+        return Completion("", False, str(e), time.time() - t0)
+    return Completion(_truncate(text, stop), True, None, time.time() - t0)
+
+
+def _truncate(text: str, stop: list[str] | None) -> str:
+    """Defensive client-side stop truncation (covers the no-stop / streamed
+    cases and any model that ignores the option)."""
+    if not stop:
+        return text
+    cut = len(text)
+    for s in stop:
+        i = text.find(s)
+        if i != -1:
+            cut = min(cut, i)
+    return text[:cut]