SkillSelect-Serve: QoS-Aware Budgeted Skill Service Recommendation for LLM Agents

Jul 15, 2026

Researchers introduce SkillSelect-Serve, a framework that recommends reusable agent skills for LLM agents while respecting constraints such as token budget, risk, and tool availability. By profiling skills as structured services and applying a constrained projection method, the system achieves 100% deliverability with minimal loss in hit rate. The approach outperforms traditional Top-k retrieval methods, significantly reducing risk exposure and tool violations.

Why it matters: This work offers a practical advance for deploying LLM agents by enabling reliable, constraint-aware skill selection, which is crucial for efficient operation under real-world resource limits.

Full story at: arXiv Information Retrieval ↗