[2604.04804] SkillX: Automatically Constructing Skill Knowledge Bases for Agents

Computer Science > Computation and Language arXiv:2604.04804 (cs) [Submitted on 6 Apr 2026] Title:SkillX: Automatically Constructing Skill Knowledge Bases for Agents Authors:Chenxi Wang, Zhuoyun Yu, Xin Xie, Wuguannan Yao, Runnan Fang, Shuofei Qiao, Kexin Cao, Guozhou Zheng, Xiang Qi, Peng Zhang, Shumin Deng View a PDF of the paper titled SkillX: Automatically Constructing Skill Knowledge Bases for Agents, by Chenxi Wang and 10 other authors View PDF HTML (experimental) Abstract:Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, repeatedly rediscover similar behaviors from limited experience, resulting in redundant exploration and poor generalization. To address this problem, we propose SkillX, a fully automated framework for constructing a \textbf{plug-and-play skill knowledge base} that can be reused across agents and environments. SkillX operates through a fully automated pipeline built on three synergistic innovations: \textit{(i) Multi-Level Skills Design}, which distills raw trajectories into three-tiered hierarchy of strategic plans, functional skills, and atomic skills; \textit{(ii) Iterative Skills Refinement}, which automatically revises skills based on execution feedback to continuously improve library quality; and \textit{(iii) Exploratory Skills Expansion}, which proactively generates and validates novel skills to expand coverage beyo...