Author

Haoran Li

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#artificial intelligence Review Oct 2026

SkillScriptBench: Benchmarking Self-Evolution of Executable Agent Skill Packages Beyond Markdown

Executable Agent Skills combine natural-language instructions and scripts into reusable packages for LLM agents, and revising them requires fixing errors without breaking correct behavior. Existing benchmarks do not systematically distinguish documentation repair, script repair, and preservation when evaluating skill s...

Yuxuan Liu, Hao-Ran Li, Yu-Hao Zhang et al. · 0 citations

#artificial intelligence May 2026

SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision

Evaluated across three main benchmarks, two domain-specific studies, and six LLMs, SkillRevise substantially outperforms one-shot baselines, and the revised skills transfer across both executors and task environments, suggesting that SkillRevise captures reusable procedural knowledge beyond any single executor.

Yuxuan Liu, Zhao-Chen Su, Lin Xie et al. · arXiv.org · 17 citations

Preprint Jul 2026

Rethinking Self-Evolving Agent Skills: Feedback Dynamics over Multiple Rounds

Overall, persistent skill self-evolution is better understood as sparse, validation-filtered search with model- and benchmark-dependent returns, rather than steady improvement from additional rounds.

Yuxuan Liu, Zhaochen Su, Yuhao Zhang et al. · 2 citations