Read-only mirror. The primary source for these tasks is GitHub at benchflow-ai/skillsbench. Open issues and pull requests there.

Leaderboard / trajectories: benchflow/skillsbench-leaderboard.

SkillsBench

Agent Skills are structured packages of procedural knowledge that augment LLM agents at inference time. SkillsBench measures how well agents put those skills to work on realistic professional tasks across many domains.

• Paper: arXiv:2602.12670
• Code & tasks: github.com/benchflow-ai/skillsbench
• Runner (BenchFlow SDK): github.com/benchflow-ai/benchflow
• Discord: discord.gg/G9dg3EfSva

Quickstart

The canonical install + run instructions live in the github repo's README and CONTRIBUTING.md. At the time of this snapshot the runner is invoked as bench eval create; check the github repo for the current install command before copying any line below.

# Run the oracle agent (no API key required) to confirm tasks load:
bench eval create -t tasks --agent oracle

# Run a real agent with skills:
bench eval create \
  -t tasks --agent claude-agent-acp -m claude-opus-4-7 \
  -s tasks/<task-id>/environment/skills \
  -o jobs/with-skills

# … and again without skills, to measure the skill delta:
bench eval create \
  -t tasks --agent claude-agent-acp -m claude-opus-4-7 \
  -o jobs/no-skills

To submit a run to the leaderboard, copy jobs/<run-timestamp>/ into a fork of benchflow/skillsbench-leaderboard under submissions/skillsbench/v0.1/<agent>__<model>/ and open a PR. See that repo's README for the full submission protocol.

What's in this dataset

A single parquet bundling the full task corpus from upstream main at commit 05f263429c5033604da64e8de568ce654fcbe6e6 (91 tasks). One row per task with task.toml metadata flattened to top-level columns, key text files inlined (instruction, dockerfile, solve_sh, test_sh, test_outputs), and every other file (environment data, solution artefacts, test fixtures, skill assets) in files: list[struct{path, size_bytes, sha256, is_text, content}] with binary content as parquet binary.

Field	Type	Description
task_id	string	directory name under tasks/
category, difficulty	string	from task.toml [metadata]
difficulty_explanation	string	author's prose on why the task is hard
tags	list[string]	[metadata].tags
allow_internet	bool	[environment].allow_internet
instruction, task_toml, dockerfile, solve_sh, test_sh, test_outputs	string	inlined source files
skills	list[struct]	{name, description, skill_md} per skill in environment/skills/
files	list[struct]	every other file: {path, size_bytes, sha256, is_text, content}

from huggingface_hub import hf_hub_download
import pyarrow.parquet as pq, pyarrow.compute as pc

path = hf_hub_download("benchflow/skillsbench",
                       "skillsbench-tasks.parquet", repo_type="dataset")
t = pq.read_table(path)
row = t.filter(pc.equal(t["task_id"], "shock-analysis-demand")).to_pylist()[0]
for f in row["files"]:
    ...  # write f["content"] to f["path"]

Anti-contamination

Do not train models on this dataset. Solutions and ground-truth tests are inlined for reproducibility. Contamination on those will void any benchmark numbers reported against SkillsBench.

Citing SkillsBench

@misc{skillsbench_2026,
      title={SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks},
      author={Xiangyi Li and Wenbo Chen and Yimin Liu and Shenghan Zheng and Xiaokun Chen and Yifeng He and Yubo Li and Bingran You and Haotian Shen and Jiankai Sun and Shuyi Wang and Binxu Li and Qunhong Zeng and Di Wang and Xuandong Zhao and Yuanli Wang and Roey Ben Chaim and Zonglin Di and Yipeng Gao and Junwei He and Yizhuo He and Liqiang Jing and Luyang Kong and Xin Lan and Jiachen Li and Songlin Li and Yijiang Li and Yueqian Lin and Xinyi Liu and Xuanqing Liu and Haoran Lyu and Ze Ma and Bowei Wang and Runhui Wang and Tianyu Wang and Wengao Ye and Yue Zhang and Hanwen Xing and Yiqi Xue and Steven Dillmann and Han-chung Lee},
      year={2026},
      eprint={2602.12670},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2602.12670}}