--- 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/` 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.