Add nearai-bench flat packaging (one row per task)

README.md

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+---
+license: apache-2.0
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- agents
+- agentic-benchmark
+- evaluation
+- clawbench
+- tool-use
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*.parquet
+---
+
+# ClawBench (nearai-bench packaging)
+
+A **flat, self-contained repackaging** of [ClawBench](https://github.com/claw-bench/claw-bench)
+— 319 agent tasks across 35 domains, difficulty levels L1–L4. Task
+content, environments and verifiers are **unmodified**, so scores stay
+comparable to the upstream ClawBench leaderboard.
+
+## Why this exists
+
+Upstream ships a git repo of nested task directories
+(`tasks/<domain>/<task>/{task.toml,instruction.md,environment/,verifier/,solution/}`).
+Cloning that per worker is wasteful for an eval/RL harness. Here each task is
+**one row**, with its three directory payloads as deterministic base64 `tar.gz`
+blobs.
+
+```python
+from datasets import load_dataset
+ds = load_dataset("NEAR-AI/clawbench", split="train")
+```
+
+## Columns
+
+| Column | Type | Notes |
+|---|---|---|
+| `task_id` | string | Stable task id = the upstream task **directory** name (e.g. `acct-001-journal-entries`) |
+| `upstream_id` | string | The `id` field inside `task.toml`. Often a short form (`sec-001`) that is **not** unique across domains — prefer `task_id` |
+| `task_path` | string | `<domain>/<task_id>`, the task's path under upstream `tasks/` |
+| `title` | string | Human-readable title |
+| `domain` | string | One of 34 domains (`email`, `security`, `multi-agent`, …) |
+| `level` | string | `L1`–`L4` difficulty |
+| `track` | string | `foundation` \| `subject-matter`; empty when upstream omits it |
+| `description` | string | One-line task description, when upstream supplies one |
+| `timeout` | int64 | Upstream per-task budget (seconds) |
+| `skills_allowed` | bool | Whether the task permits skill creation/reuse |
+| `tags` | string (JSON) | Upstream tag list |
+| `capabilities` | string (JSON) | e.g. `["tool-use"]` or `["file-read","file-write"]` — see note below |
+| `capability_types` | string (JSON) | e.g. `["reasoning","tool-use"]` |
+| `required_actions` | string (JSON) | e.g. `["file-read","data-processing","file-write"]` |
+| `instruction` | string | **Verbatim `instruction.md`** — the agent-facing prompt |
+| `task_toml` | string | **Verbatim `task.toml`** |
+| `environment_tar` | string | base64(tar.gz) of `environment/` — `setup.sh` plus any `data/` seed files |
+| `verifier_tar` | string | base64(tar.gz) of `verifier/` (pytest `test_output.py`) **plus a bundled `conftest.py`** |
+| `solution_tar` | string | base64(tar.gz) of `solution/` — the reference `solve.sh` |
+
+### Note on the two upstream `task.toml` shapes
+
+Upstream is not uniform: 65 tasks nest their metadata under a `[task]` table,
+the other 254 put the same keys at the top level — and the two shapes carve up
+capabilities differently (`capabilities` + `required_actions` vs.
+`capabilities` + `capability_types`). The flattened columns above normalize
+both, and `task_toml` always holds the verbatim original. If you parse
+`task_toml` yourself, handle both shapes or you will silently blank the
+metadata of 80% of the suite.
+
+## Running a task
+
+```python
+import base64, io, subprocess, tarfile, tempfile, pathlib
+
+def untar(b64, dest):
+    if not b64: return
+    dest.mkdir(parents=True, exist_ok=True)
+    with tarfile.open(fileobj=io.BytesIO(base64.b64decode(b64)), mode="r:gz") as t:
+        t.extractall(dest)
+
+row = ds[0]
+tmp = pathlib.Path(tempfile.mkdtemp())
+untar(row["environment_tar"], tmp / "environment")
+untar(row["verifier_tar"],   tmp / "verifier")
+workspace = tmp / "workspace"; workspace.mkdir()
+
+# 1. seed the workspace
+subprocess.run(["bash", str(tmp / "environment/setup.sh"), str(workspace)], check=True)
+# 2. give row["instruction"] to the agent, let it work in `workspace`
+# 3. score with the upstream pytest verifier
+subprocess.run(
+    ["python", "-m", "pytest", "verifier/test_output.py", "--workspace", str(workspace), "-q"],
+    cwd=tmp,
+)
+```
+
+`setup.sh` takes the workspace directory as `$1`. Pass an **absolute** path —
+several scripts interpolate `$1` into a heredoc that runs with a different cwd,
+so a relative path silently produces an empty workspace.
+
+## Scoring
+
+The verifier is pytest. Each test carries an `@pytest.mark.weight(n)` marker
+(default `2.0`); the task score is
+`sum(weight of passing tests) / sum(all weights)`. `conftest.py` — bundled into
+every `verifier_tar` — provides both that marker and the `--workspace` option,
+so a verifier run needs nothing else from the upstream repo.
+
+Two things worth knowing if you compare numbers:
+
+- The verifier imports `numpy`/`pandas` for some domains. A verifier
+  environment missing them yields false zeros rather than errors.
+- The upstream leaderboard metric is a difficulty-weighted aggregate over a
+  5-dimension composite, not a flat mean of per-task scores.
+
+A reference implementation lives in
+[`nearai/benchmarks`](https://github.com/nearai/benchmarks) at
+`src/adapters/clawbench.rs`.
+
+## ⚠️ Contamination warning
+
+`solution_tar` contains **reference solutions**. They are published upstream
+too, and they are needed for golden-validation (a correct harness must score
+1.0 when the solution is applied and ~0.0 on an empty workspace) — but do not
+train on this column, and drop it before handing any of this to a model.
+
+## Provenance & license
+
+- **Upstream**: <https://github.com/claw-bench/claw-bench> — Apache-2.0.
+  Pinned commit `1fc25add8fe77aa498d58fb564ea91a87307da76`.
+- **This repackaging**: Apache-2.0, same terms. Task content unmodified; only
+  the container format changed, plus a `conftest.py` copy bundled into each
+  `verifier_tar` for self-containment.
+- **Packaged by**: [NEAR AI](https://near.ai) for
+  [nearai-bench](https://github.com/nearai/benchmarks).

data/train-00000-of-00001.parquet

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+size 1603892