SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
Ziyu Ma, Shidong Yang, Yuxiang Ji, Xucong Wang, Yong Wang, Yiming Hu, Tongwen Huang, Xiangxiang Chu

TL;DR
SkillClaw introduces a framework for collective skill evolution in multi-user LLM agent systems, enabling continuous improvement by aggregating and processing user interactions to refine and extend skills.
Contribution
It presents an autonomous evolver that leverages cross-user interactions to update skills dynamically, facilitating shared knowledge transfer and cumulative capability growth.
Findings
Significant performance improvements in Qwen3-Max with limited interactions.
Effective aggregation of heterogeneous user experiences for skill refinement.
Demonstrated system-wide skill enhancement through shared repositories.
Abstract
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…
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