[2603.22455] SkillRouter: Retrieve-and-Rerank Skill Selection for LLM Agents at Scale

Computer Science > Machine Learning arXiv:2603.22455 (cs) [Submitted on 23 Mar 2026] Title:SkillRouter: Retrieve-and-Rerank Skill Selection for LLM Agents at Scale Authors:YanZhao Zheng, ZhenTao Zhang, Chao Ma, YuanQiang Yu, JiHuan Zhu, Baohua Dong, Hangcheng Zhu View a PDF of the paper titled SkillRouter: Retrieve-and-Rerank Skill Selection for LLM Agents at Scale, by YanZhao Zheng and 6 other authors View PDF HTML (experimental) Abstract:As LLM agent ecosystems grow, the number of available skills (tools, plugins) has reached tens of thousands, making it infeasible to inject all skills into an agent's context. This creates a need for skill routing -- retrieving the most relevant skills from a large pool given a user task. The problem is compounded by pervasive functional overlap in community skill repositories, where many skills share similar names and purposes yet differ in implementation details. Despite its practical importance, skill routing remains under-explored. Current agent architectures adopt a progressive disclosure design -- exposing only skill names and descriptions to the agent while keeping the full implementation body hidden -- implicitly treating metadata as sufficient for selection. We challenge this assumption through a systematic empirical study on a benchmark of ~$80K skills and 75 expert-verified queries. Our key finding is that the skill body (full implementation text) is the decisive signal: removing it causes 29--44 percentage point degradation a...