SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces
SkillSmith is a new framework designed to optimize the execution of skills in large language model-based agent systems. It compiles skill packages into minimal executable interfaces, reducing redundancy in context injection and reasoning. The framework has shown significant improvements in efficiency, including faster solve times and reduced costs compared to traditional methods.
- ▪SkillSmith compiles skill packages offline into minimal executable interfaces.
- ▪The framework reduces solve-stage token usage by 57.44% and thinking iterations by 42.99%.
- ▪SkillSmith improves task accuracy by allowing compiled artifacts from stronger models to be reused by smaller runtime models.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15215 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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Computer Science > Artificial Intelligence arXiv:2605.15215 (cs) [Submitted on 12 May 2026] Title:SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces Authors:Duling Xu, Zheng Chen, Zaifeng Pan, Jiawei Guan, Dong Dong, Jialin Li, Bangzheng Pu View a PDF of the paper titled SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces, by Duling Xu and 6 other authors View PDF HTML (experimental) Abstract:Recently, skills have been widely adopted in large language model (LLM)-based agent systems across various domains. In existing frameworks, skills are typically injected into the agent reasoning loop as contextual guidance once matched to a runtime task, enabling specialized task-solving capabilities.
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