SkillX: Automatically Constructing Skill Knowledge Bases for Agents
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 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: (i) Multi-Level Skills Design, which distills raw trajectories into three-tiered hierarchy of strategic plans, functional skills, and atomic skills; (ii) Iterative Skills Refinement, which automatically revises skills based on execution feedback to continuously improve library quality; and (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.
69d473a92c523d36431fb108
Chenxi Wang
69d473a92c523d36431fb109
Zhuoyun Yu
69d473a92c523d36431fb10a
Xin Xie
69d473a92c523d36431fb10b
Wuguannan Yao
69d473a92c523d36431fb10c
Runnan Fang
69d473a92c523d36431fb10d
Shuofei Qiao
69d473a92c523d36431fb10e
Kexin Cao
69d473a92c523d36431fb10f
Guozhou Zheng
69d473a92c523d36431fb110
Xiang Qi
69d473a92c523d36431fb111
Peng Zhang
69d473a92c523d36431fb112
Shumin Deng
admin_assigned
2026-04-07T08:58:11.963Z
6441f1d2603214724ec0c1c2
/avatars/d3c4b759e6a5635e37ff715fae52e5ba.svg
Shumin Deng
231sm
user
231sm
SkillX: Automatically Constructing Skill Knowledge Bases for Agents
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 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: (i) Multi-Level Skills Design, which distills raw trajectories into three-tiered hierarchy of strategic plans, functional skills, and atomic skills; (ii) Iterative Skills Refinement, which automatically revises skills based on execution feedback to continuously improve library quality; and (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.
SkillX is an automated framework that creates reusable skill libraries for LLM agents through hierarchical skill design, iterative refinement, and exploratory expansion to improve generalization and efficiency across different environments.
self-evolving paradigms skill knowledge base multi-level skills design iterative skills refinement exploratory skills expansion hierarchical experience representations long-horizon benchmarks user-interactive benchmarks GLM-4.6 skill transferability