SkillClaw: Let Skills Evolve Collectively with Agentic Evolver

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 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.