[2603.25158] Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills

Computer Science > Artificial Intelligence arXiv:2603.25158 (cs) [Submitted on 26 Mar 2026] Title:Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills Authors:Jingwei Ni, Yihao Liu, Xinpeng Liu, Yutao Sun, Mengyu Zhou, Pengyu Cheng, Dexin Wang, Xiaoxi Jiang, Guanjun Jiang View a PDF of the paper titled Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills, by Jingwei Ni and 8 other authors View PDF HTML (experimental) Abstract:Equipping Large Language Model (LLM) agents with domain-specific skills is critical for tackling complex tasks. Yet, manual authoring creates a severe scalability bottleneck. Conversely, automated skill generation often yields fragile or fragmented results because it either relies on shallow parametric knowledge or sequentially overfits to non-generalizable trajectory-local lessons. To overcome this, we introduce Trace2Skill, a framework that mirrors how human experts author skills: by holistically analyzing broad execution experience before distilling it into a single, comprehensive guide. Instead of reacting sequentially to individual trajectories, Trace2Skill dispatches a parallel fleet of sub-agents to analyze a diverse pool of executions. It extracts trajectory-specific lessons and hierarchically consolidates them into a unified, conflict-free skill directory via inductive reasoning. Trace2Skill supports both deepening existing human-written skills and creating new ones from scratch. Experiments in ...