SkillSelect-Serve: QoS-Aware Budgeted Skill Service Recommendation for LLM Agents
Reusable agent skills are emerging as a service-oriented capability layer for Large Language Model (LLM) agents. Unlike plain retrieval items, a skill exposes functional capabilities, input-output assumptions, tool dependencies, context cost, and risk metadata. Selecting skills is particularly challenging for small LLM agents, which can load only a few capability units under restricted context, tool availability, and risk tolerance. Existing fixed Top-k methods rank skills by textual relevance and overlook requirement satisfaction, deliverability, and operational constraints. We present SkillSelect-Serve, a QoS-aware, budget-constrained Skill Service recommendation framework. Raw skills a...
| Authors | Jingyuan Zheng, Dongjing Wang, Xin Zhang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 8 May 2026 |
| Last updated at source | 14 July 2026 |
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