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