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arXiv · 2602.16653

Agent Skill Framework: Perspectives on the Potential of Small to Medium Language Models in Industrial Environments

Yangjie Xu·Lujun Li·Lama Sleem·Niccolo Gentile·Yewei Song·Yiqun Wang·Siming Ji·Wenbo Wu·Radu State
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Abstract

Agent skills are widely supported by major agentic frameworks and perform well with proprietary models, yet their effectiveness for small and medium-sized open source language models (270 M-80B) remains underexplored. We systematically study the Skill paradigm in resource-constrained industrial settings, where reliance on proprietary APIs is impractical due to data security and budget constraints. Across two open-source tasks and a real-world insurance claims classification task, we find that very small models struggle with reliable skill selection, while models around 30B-80B benefit substantially. Thinking variants do not show major levels of improvement from skills, also considering GPU usage increases due to overthinking. These findings reveal a trade-off between GPU cost and agent performance, and provide actionable insights for effective Skill configuration and SLM deployment in real world settings.

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Yangjie Xu, Lujun Li, Lama Sleem, Niccolo Gentile, Yewei Song, Yiqun Wang, Siming Ji, Wenbo Wu, Radu State. 2026-02-18. Agent Skill Framework: Perspectives on the Potential of Small to Medium Language Models in Industrial Environments. https://arxiv.org/abs/2602.16653

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