Agent Skill Framework: Perspectives on the Potential of Small Language Models in Industrial Environments
2026-02-18 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors studied how the Agent Skill framework, which helps large AI models work better and avoid mistakes, performs when used with smaller language models (SLMs). They found that very small models had trouble choosing the right skills, but medium-sized models improved a lot with this approach. Larger, code-focused models performed almost as well as big commercial models while using less computing power. This work helps understand when and how to use Agent Skills effectively with different sized AI models in practical settings.
Agent Skill frameworksmall language modelscontext engineeringhallucinationsmodel generalizationparameter sizeopen-source tasksGPU efficiencyskill selectioninsurance claims dataset
Authors
Yangjie Xu, Lujun Li, Lama Sleem, Niccolo Gentile, Yewei Song, Yiqun Wang, Siming Ji, Wenbo Wu, Radu State
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 struggle with reliable skill selection, while moderately sized SLMs (approximately 12B - 30B) parameters) benefit substantially from the Agent Skill approach. Moreover, code-specialized variants at around 80B parameters achieve performance comparable to closed-source baselines while improving GPU efficiency. Collectively, these findings provide a comprehensive and nuanced characterization of the capabilities and constraints of the framework, while providing actionable insights for the effective deployment of Agent Skills in SLM-centered environments.