SkillClaw：讓技能在智能演進體的集體協作中共同進化

SkillClaw: Let Skills Evolve Collectively with Agentic Evolver

April 9, 2026

作者: Ziyu Ma, Shidong Yang, Yuxiang Ji, Xucong Wang, Yong Wang, Yiming Hu, Tongwen Huang, Xiangxiang Chu

cs.AI

摘要

諸如OpenClaw之類的大型語言模型（LLM）智能體依賴可複用技能執行複雜任務，但這些技能在部署後大多保持靜態。這導致相似的工作流程、工具使用模式及故障模式在不同用戶間被反覆重新發現，阻礙系統基於經驗實現自我提升。儘管不同用戶的互動行為能為技能何時有效或失效提供互補性信號，現有系統仍缺乏將此類異質經驗轉化為可靠技能更新的機制。為解決這些問題，我們提出SkillClaw——一個面向多用戶智能體生態系統的集體技能演化框架，其將跨用戶、跨時間的互動視作改進技能的核心信號。SkillClaw持續聚合使用過程中產生的行為軌跡，並通過自主演化器進行處理：該組件能識別重複出現的行為模式，將其轉化為技能集的更新，包括精煉現有技能或擴展新功能。演化後的技能將存儲於共享知識庫並跨用戶同步，使得單一場景中發現的改進能無需用戶額外操作即可在全系統傳播。通過將多用戶經驗整合至持續的技能更新中，SkillClaw實現了跨用戶知識傳遞與累積性能力提升。在WildClawBench上的實驗表明，僅需有限互動與反饋，該框架便能顯著提升Qwen3-Max在真實智能體場景中的表現。

English

Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment. As a result, similar workflows, tool usage patterns, and failure modes are repeatedly rediscovered across users, preventing the system from improving with experience. While interactions from different users provide complementary signals about when a skill works or fails, existing systems lack a mechanism to convert such heterogeneous experiences into reliable skill updates. To address these issues, we present SkillClaw, a framework for collective skill evolution in multi-user agent ecosystems, which treats cross-user and over-time interactions as the primary signal for improving skills. SkillClaw continuously aggregates trajectories generated during use and processes them with an autonomous evolver, which identifies recurring behavioral patterns and translates them into updates to the skill set by refining existing skills or extending them with new capabilities. The resulting skills are maintained in a shared repository and synchronized across users, allowing improvements discovered in one context to propagate system-wide while requiring no additional effort from users. By integrating multi-user experience into ongoing skill updates, SkillClaw enables cross-user knowledge transfer and cumulative capability improvement, and experiments on WildClawBench show that limited interaction and feedback, it significantly improves the performance of Qwen3-Max in real-world agent scenarios.