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

Jingyuan Zheng; Dongjing Wang; Xin Zhang; Hao Chen; Youhuizi Li; Xudong Shen; Haiping Zhang; Butian Huang; Dongjin Yu; Guandong Xu

arXiv · 2607.00011

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

Jingyuan Zheng·Dongjing Wang·Xin Zhang·Hao Chen·Youhuizi Li·Xudong Shen·Haiping Zhang·Butian Huang·Dongjin Yu·Guandong Xu

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Abstract

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 are profiled as structured Skill Services, the task is converted into a structured requirement object, and candidates discovered from a large-scale registry are ranked by a calibrated task-conditioned suitability estimator and packed by a constrained projection enforcing token-budget, aggregated-risk, and tool-availability constraints, using only deployment-observable features. On a registry of 35,353 skills with pooled multi-positive relevance judgments verified by two independent assessors, the unconstrained top-5 recommendation fits a realistic 4,000-token context for only 9.1% of tasks; the constrained projection restores 100% deliverability at a cost of only 1.14 points of hit rate, outperforming retrieve-and-rerank, budget truncation, and diversity-based selection under identical budgets. The same mechanism halves delivered risk exposure and eliminates the 44-81% tool-violation rates of tool-agnostic recommendation. At an identical three-service budget, hit rate improves from 0.8864 to 0.9091 over fixed Top-3 retrieval. The results support managing reusable agent skills as discoverable, comparable, and constraint-aware service units instead of plain retrievable documents.

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Jingyuan Zheng, Dongjing Wang, Xin Zhang, Hao Chen, Youhuizi Li, Xudong Shen, Haiping Zhang, Butian Huang, Dongjin Yu, Guandong Xu. 2026-05-08. SkillSelect-Serve: QoS-Aware Budgeted Skill Service Recommendation for LLM Agents. https://arxiv.org/abs/2607.00011

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