SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
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 in...
| Authors | Ziyu Ma, Shidong Yang, Yuxiang Ji et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 April 2026 |
| Last updated at source | 10 August 2026 |
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