{"ID":2921665,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-03T05:56:00.181519634Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01139","arxiv_id":"2606.01139","title":"SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision","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 systematically retains the optimal skill version. Evaluated across three benchmarks and five 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 exhibit strong cross-model transferability, capturing generalized procedural knowledge over model-specific artifacts.","short_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,...","url_abs":"https://arxiv.org/abs/2606.01139","url_pdf":"https://arxiv.org/pdf/2606.01139v1","authors":"[\"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\",\"Yangqiu Song\"]","published":"2026-05-31T10:19:13Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Large Language Model\"]","has_code":false}