SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
Summary
SkillClaw introduces a framework for collective skill evolution in multi-user LLM agent systems, enabling autonomous updates and cross-user knowledge transfer by aggregating interactions and feedback to improve performance across the ecosystem.
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Paper page - SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
Source: https://huggingface.co/papers/2604.08377
Abstract
SkillClaw enables collective skill evolution in multi-user LLM agent systems by aggregating user interactions to autonomously update and improve reusable skills across the ecosystem.
Large language model (LLM) agents such as OpenClaw rely onreusable skillsto 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 inmulti-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 anautonomous 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 ashared repositoryand 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 enablescross-user knowledge transferandcumulative 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.
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