![[2602.16653] Agent Skill Framework: Perspectives on the Potential of Small Language Models in Industrial Environments](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.16653] Agent Skill Framework: Perspectives on the Potential of Small Language Models in Industrial Environments
Summary
The article explores the Agent Skill Framework, assessing its effectiveness in enhancing small language models (SLMs) for industrial applications, particularly in data-sensitive environments.
Why It Matters
As industries increasingly rely on AI, understanding how small language models can be optimized for specific tasks is crucial. This research highlights the potential of the Agent Skill Framework to improve model performance while addressing security and budget constraints, making it relevant for businesses looking to implement AI solutions effectively.
Key Takeaways
- The Agent Skill Framework enhances task accuracy and reduces hallucinations in SLMs.
- Moderately sized SLMs (12B-30B parameters) significantly benefit from the Agent Skill approach.
- Tiny models struggle with skill selection, highlighting the need for larger models in complex tasks.
- Code-specialized variants around 80B parameters can match closed-source models while improving GPU efficiency.
- The findings provide actionable insights for deploying Agent Skills in industrial settings.
Computer Science > Artificial Intelligence arXiv:2602.16653 (cs) [Submitted on 18 Feb 2026] Title:Agent Skill Framework: Perspectives on the Potential of Small Language Models in Industrial Environments Authors:Yangjie Xu, Lujun Li, Lama Sleem, Niccolo Gentile, Yewei Song, Yiqun Wang, Siming Ji, Wenbo Wu, Radu State View a PDF of the paper titled Agent Skill Framework: Perspectives on the Potential of Small Language Models in Industrial Environments, by Yangjie Xu and 8 other authors View PDF HTML (experimental) Abstract:Agent Skill framework, now widely and officially supported by major players such as GitHub Copilot, LangChain, and OpenAI, performs especially well with proprietary models by improving context engineering, reducing hallucinations, and boosting task accuracy. Based on these observations, an investigation is conducted to determine whether the Agent Skill paradigm provides similar benefits to small language models (SLMs). This question matters in industrial scenarios where continuous reliance on public APIs is infeasible due to data-security and budget constraints requirements, and where SLMs often show limited generalization in highly customized scenarios. This work introduces a formal mathematical definition of the Agent Skill process, followed by a systematic evaluation of language models of varying sizes across multiple use cases. The evaluation encompasses two open-source tasks and a real-world insurance claims data set. The results show that tiny models...
