---
license: mit
language:
- zh
- en
task_categories:
- text-generation
- question-answering
pretty_name: ClawBenchPro
tags:
- agent-benchmark
- workplace
- tool-use
- multi-turn
- skills
- nanoclaw
size_categories:
- 1K<n<10K
configs:
- config_name: default
  default: true
  data_files:
  - split: test
    path: dataset_index.csv
- config_name: round_01_aligned_mix_800
  data_files:
  - split: test
    path: round_01_aligned_mix_800/dataset_index.csv
- config_name: persona_aligned_mix_200
  data_files:
  - split: test
    path: persona_aligned_mix_200/dataset_index.csv
---

# ClawBenchPro

ClawBenchPro is a compact, builder-based workplace-agent benchmark package exported from Nanoclaw.
It contains task YAML files, prompts, task-local environment builders, skills, evaluation manifests,
provenance metadata, and checksums.

## Included Splits

| Dataset | Tasks | Groups |
|---|---:|---|
| `round_01_aligned_mix_800` | 800 | `base`, `hard_aligned`, `multi_turn_aligned`, `skills_aligned` |
| `persona_aligned_mix_200` | 200 | `base`, `hard`, `multi_turn`, `skills` |

## Directory Layout

```text
ClawBenchPro/
├── README.md
├── LICENSE
├── dataset_index.jsonl
├── manifest.json
├── checksums.sha256
├── round_01_aligned_mix_800/
└── persona_aligned_mix_200/
```

Each dataset directory contains:

- `tasks/`: task YAML files, prompts, and task-local `env_builder.py` builders.
- `skills/`: packaged skills referenced by task YAML files.
- `eval_manifests/`: group-level manifests and task id lists.
- `provenance/`: sanitized construction metadata.
- `manifest.json`: dataset-level metadata.
- `checksums.sha256`: dataset-level file checksums.

Prebuilt `assets/` directories are intentionally not included to keep the Hugging Face repository
compact. Each task includes an `env_builder.py` that can materialize `assets/<task_id>/` on demand.

## Usage

After downloading the dataset, point Nanoclaw or compatible runners at the YAML tasks under:

```text
round_01_aligned_mix_800/tasks/*.yaml
persona_aligned_mix_200/tasks/*.yaml
```

Group manifests are available under `eval_manifests/` for category-level analysis.

To materialize one task environment manually:

```bash
cd round_01_aligned_mix_800
python tasks/data_round_01_aligned_mix_800_0001/env_builder.py
```

This creates:

```text
round_01_aligned_mix_800/assets/data_round_01_aligned_mix_800_0001/
```

Nanoclaw's batch runner can also invoke these builders automatically before each task run.

To materialize assets in batches from the repository root:

```bash
python materialize_assets.py --dataset round_01_aligned_mix_800 --workers 8
python materialize_assets.py --dataset persona_aligned_mix_200 --workers 8
```

## Dataset Index

`dataset_index.jsonl` provides one row per task with:

```text
dataset, task_id, category, task_file, asset_dir, prompt_files, skill_count
```

The full task definitions remain in the YAML files.

## License

This package is released under the MIT License. See `LICENSE`.

## Notes

- The package is intended as a benchmark artifact rather than a tabular training dataset.
- Some tasks intentionally contain synthetic keys, internal URLs, noisy logs, broken files, or
  policy-sensitive strings as part of the benchmark environment. These are benchmark fixtures,
  not operational credentials.
- Build-time local absolute paths have been removed from the Hugging Face-ready package.