| --- |
| 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. |
|
|
| ## Responsible AI (RAI) Considerations |
|
|
| This section summarizes the Responsible AI information also encoded in the Croissant metadata under |
| `croissant/openreview_croissant.json`. |
|
|
| ### Synthetic Data Generation |
|
|
| ClawBenchPro is designed as a synthetic workplace-agent benchmark. Tasks were generated, staged, |
| imported into Nanoclaw task YAML format, validated with task-local `env_builder.py` builders, grouped |
| into aligned benchmark subsets, and exported in compact builder-only form for hosting. The benchmark |
| includes fictional workplace scenarios, synthetic personas, synthetic internal services, synthetic |
| records, task-local skills, and generated workspace fixtures. The package is not intended to contain |
| real user records or operational credentials. |
|
|
| ### Sandbox Fixtures and Real-World Safety |
|
|
| Some tasks intentionally contain strings that look like API keys, internal URLs, secrets, policy-sensitive |
| phrases, corrupted files, or broken logs. These are sandbox benchmark fixtures used to test whether agents |
| can reason about noisy workplace environments. They are not real credentials, do not connect to production |
| systems, and are meant to be executed in local or containerized workspaces. Prebuilt `assets/` are not |
| published; environments are materialized locally from `env_builder.py` when needed. |
|
|
| ### Environment Validation Limitations |
|
|
| Environment builders were checked for the ability to materialize task workspaces, but this does not prove |
| that every possible runner, model, operating system, dependency version, or execution policy will behave |
| identically. The benchmark measures performance in controlled Nanoclaw-compatible environments and should |
| not be interpreted as a complete certification of real-world workplace automation reliability. |
|
|
| ### Biases and Scope Limitations |
|
|
| The dataset is selected to stress agentic workflows such as state tracking, tool use, multi-turn reasoning, |
| file manipulation, and skill invocation. This creates selection bias toward tasks that can be packaged as |
| local workspaces. The benchmark may underrepresent low-resource languages, non-technical occupations, |
| accessibility-specific workflows, embodied tasks, and domains requiring direct human interaction. |
|
|
| ### Intended and Non-Recommended Uses |
|
|
| Intended uses include comparative evaluation of language-agent runners, category-level benchmark analysis, |
| reproducibility studies, and diagnosis of tool-use or multi-turn failure modes. The dataset is not validated |
| for model fine-tuning, safety certification, demographic fairness auditing, hiring decisions, medical, |
| legal, financial, or other safety-critical deployment decisions. |
|
|
| ### Social Impact |
|
|
| ClawBenchPro may help make agent evaluation more reproducible and transparent by providing local synthetic |
| workspaces, explicit task categories, and machine-readable metadata. Potential risks include overgeneralizing |
| benchmark scores, optimizing narrowly to synthetic tasks, or presenting benchmark performance as evidence of |
| real-world reliability. We mitigate these risks by documenting limitations, keeping tasks sandboxed, and |
| publishing Croissant metadata with RAI fields. |
|
|
| ## 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. |
|
|
