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/score.py

@@ -0,0 +1,73 @@
+"""Scorecard aggregation + workflow-weighted model picker.
+
+The harness emits one LangResult per (model, language). This module:
+
+  • persists/loads them as a flat scorecard JSON (the durable artifact a future
+    model-picker UI would read), and
+  • collapses a scorecard into a per-model ranking under a chosen workflow
+    profile (weights from workflows.json), so "which model for my work?" becomes
+    a single sorted list.
+
+Keeping scoring separate from running means the same captured results can be
+re-ranked for any workflow without re-executing a single model.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+_WORKFLOWS = Path(__file__).resolve().parent / "workflows.json"
+
+
+def load_workflows() -> dict:
+    data = json.loads(_WORKFLOWS.read_text())
+    return {k: v for k, v in data.items() if not k.startswith("_")}
+
+
+def save_scorecard(results: list[dict], path: Path) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps({"version": 1, "results": results}, indent=2))
+
+
+def load_scorecard(path: Path) -> list[dict]:
+    return json.loads(path.read_text()).get("results", [])
+
+
+def _matrix(results: list[dict], metric: str) -> dict[str, dict[str, float]]:
+    """{model: {language: metric}} from a flat results list."""
+    m: dict[str, dict[str, float]] = {}
+    for r in results:
+        val = r.get(metric)
+        if val is None:
+            continue
+        m.setdefault(r["model"], {})[r["language"]] = val
+    return m
+
+
+def rank(results: list[dict], workflow: str = "balanced",
+         metric: str = "pass@1") -> list[dict]:
+    """Return models ranked by workflow-weighted score (desc).
+
+    Each row: {model, score, per_language, covered}. A model is only scored on
+    languages it has results for; `covered` flags whether it has all the weighted
+    languages (a partial run still ranks, but the gap is visible)."""
+    profiles = load_workflows()
+    if workflow not in profiles:
+        raise KeyError(f"unknown workflow {workflow!r}; "
+                       f"known: {', '.join(profiles)}")
+    weights = profiles[workflow]["weights"]
+    matrix = _matrix(results, metric)
+    rows = []
+    for model, per_lang in matrix.items():
+        num = den = 0.0
+        for lang, w in weights.items():
+            if lang in per_lang:
+                num += w * per_lang[lang]
+                den += w
+        score = num / den if den else 0.0
+        covered = all(lang in per_lang for lang, w in weights.items() if w > 0)
+        rows.append({"model": model, "score": round(score, 4),
+                     "per_language": per_lang, "covered": covered})
+    rows.sort(key=lambda r: r["score"], reverse=True)
+    return rows