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 beyond seed training data. Using a strong backbone agent (GLM-4.6), we automatically build a reusable skill library and evaluate its transferability on challenging long-horizon, user-interactive benchmarks, including AppWorld, BFCL-v3, and $τ^2$-Bench. Experiments show that SkillKB consistently improves task success and execution efficiency when plugged into weaker base agents, highlighting the importance of structured, hierarchical experience representations for generalizable agent learning. Our code will be publicly available soon at https://github.com/zjunlp/SkillX.

翻译：从经验中学习对于构建能力强大的大语言模型（LLM）智能体至关重要，然而当前主流的自进化范式效率低下：智能体在孤立环境中学习，反复从有限经验中重新发现相似行为，导致冗余探索与泛化能力不足。针对该问题，我们提出SkillX——一种全自动化框架，用于构建可跨智能体与环境复用的**即插即用技能知识库**。SkillX通过全自动流水线运行，其核心创新包含三个协同模块：**（i）多层级技能设计**，将原始轨迹蒸馏为战略规划、功能性技能与原子技能的三层层次结构；**（ii）迭代式技能精炼**，基于执行反馈自动修正技能以持续提升库质量；**（iii）探索性技能扩展**，主动生成并验证新型技能以扩展覆盖范围，突破种子训练数据限制。我们以强基座智能体（GLM-4.6）为基础，自动构建可复用的技能库，并在包含AppWorld、BFCL-v3及τ²-Bench等需长期规划与用户交互的挑战性基准上评估其可迁移性。实验表明，当SkillKB被植入较弱基座智能体时，能稳定提升任务成功率与执行效率，突显结构化分层经验表征对于通用化智能体学习的重要性。我们的代码将发布于https://github.com/zjunlp/SkillX。