@inproceedings{liu-etal-2026-skillrevise,
title = "{SKILLREVISE}: Improving {LLM}-Authored Agent Skills via Trace-Conditioned Skill Revision",
author = "Liu, Yuxuan and
Su, Zhaochen and
Xie, Lingyun and
Zhang, Yuhao and
Zong, Qing and
Guo, Jiahe and
Xie, Zhongwei and
Ji, Yiyan and
Yim, Yauwai and
Luo, Hongyu and
Ren, Xiyu and
Chenyu, Ruan and
Li, Haoran and
Song, Yangqiu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.991/",
pages = "20923--20946",
ISBN = "979-8-89176-462-0",
abstract = "Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures. Existing self-evolving methods refine skills using accumulated trajectories. However, they struggle in cold-start settings, where only an initial, imperfect skill is available. Consequently, skill construction defaults to expert authoring or one-shot LLM generation. Expert-authored skills are costly and may not align with how LLM agents actually execute tasks, while one-shot generated skills can be syntactically well formed yet behaviorally weak. To bridge this gap, we propose SkillRevise, an execution-grounded framework designed to iteratively refine these initial skills. SkillRevise diagnoses skill defects from execution evidence, retrieves relevant repair principles from a general memory, and applies execution-anchored edits. By re-executing candidates and measuring empirical utility, it retains the best observed skill within the revision budget. Evaluated across three main benchmarks, two domain-specific studies, and six LLMs, SkillRevise substantially outperforms one-shot baselines{---}improving the base agent{'}s success rate on SkillsBench from 36.05{\%} to 61.63{\%}. Furthermore, the revised skills transfer across both executors and task environments, suggesting that SkillRevise captures reusable procedural knowledge beyond any single executor. Our code is available at https://github.com/HKUST-KnowComp/skillrevise."
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<abstract>Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures. Existing self-evolving methods refine skills using accumulated trajectories. However, they struggle in cold-start settings, where only an initial, imperfect skill is available. Consequently, skill construction defaults to expert authoring or one-shot LLM generation. Expert-authored skills are costly and may not align with how LLM agents actually execute tasks, while one-shot generated skills can be syntactically well formed yet behaviorally weak. To bridge this gap, we propose SkillRevise, an execution-grounded framework designed to iteratively refine these initial skills. SkillRevise diagnoses skill defects from execution evidence, retrieves relevant repair principles from a general memory, and applies execution-anchored edits. By re-executing candidates and measuring empirical utility, it retains the best observed skill within the revision budget. Evaluated across three main benchmarks, two domain-specific studies, and six LLMs, SkillRevise substantially outperforms one-shot baselines—improving the base agent’s success rate on SkillsBench from 36.05% to 61.63%. Furthermore, the revised skills transfer across both executors and task environments, suggesting that SkillRevise captures reusable procedural knowledge beyond any single executor. Our code is available at https://github.com/HKUST-KnowComp/skillrevise.</abstract>
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%0 Conference Proceedings
%T SKILLREVISE: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision
%A Liu, Yuxuan
%A Su, Zhaochen
%A Xie, Lingyun
%A Zhang, Yuhao
%A Zong, Qing
%A Guo, Jiahe
%A Xie, Zhongwei
%A Ji, Yiyan
%A Yim, Yauwai
%A Luo, Hongyu
%A Ren, Xiyu
%A Chenyu, Ruan
%A Li, Haoran
%A Song, Yangqiu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-skillrevise
%X Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures. Existing self-evolving methods refine skills using accumulated trajectories. However, they struggle in cold-start settings, where only an initial, imperfect skill is available. Consequently, skill construction defaults to expert authoring or one-shot LLM generation. Expert-authored skills are costly and may not align with how LLM agents actually execute tasks, while one-shot generated skills can be syntactically well formed yet behaviorally weak. To bridge this gap, we propose SkillRevise, an execution-grounded framework designed to iteratively refine these initial skills. SkillRevise diagnoses skill defects from execution evidence, retrieves relevant repair principles from a general memory, and applies execution-anchored edits. By re-executing candidates and measuring empirical utility, it retains the best observed skill within the revision budget. Evaluated across three main benchmarks, two domain-specific studies, and six LLMs, SkillRevise substantially outperforms one-shot baselines—improving the base agent’s success rate on SkillsBench from 36.05% to 61.63%. Furthermore, the revised skills transfer across both executors and task environments, suggesting that SkillRevise captures reusable procedural knowledge beyond any single executor. Our code is available at https://github.com/HKUST-KnowComp/skillrevise.
%U https://aclanthology.org/2026.findings-emnlp.991/
%P 20923-20946
Markdown (Informal)
[SKILLREVISE: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision](https://aclanthology.org/2026.findings-emnlp.991/) (Liu et al., Findings 2026)
ACL
- Yuxuan Liu, Zhaochen Su, Lingyun Xie, Yuhao Zhang, Qing Zong, Jiahe Guo, Zhongwei Xie, Yiyan Ji, Yauwai Yim, Hongyu Luo, Xiyu Ren, Ruan Chenyu, Haoran Li, and Yangqiu Song. 2026. SKILLREVISE: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 20923–20946, Budapest, Hungary. Association for Computational Linguistics.