SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces
SkillSmith is a boundary-first compiler-runtime framework that compiles skill packages offline into minimal executable interfaces by extracting fine-grained operational boundaries. This reduces unnecessary context injection and redundant reasoning in LLM-based agent systems. Evaluated on SkillsBench, it achieves 57.44% reduction in token usage, 42.99% fewer thinking iterations, 50.57% faster solve time (2.02x), and 57.44% lower cost. Additionally, compiled artifacts from stronger models can be reused by smaller models, improving accuracy.
[2605.15215] SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces
[Submitted on 12 May 2026]
Title:SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces
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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. We find that this execution paradigm introduces two major sources of redundancy: irrelevant context injection and repeated skill-specific reasoning and planning. To this end, we propose SkillSmith, a boundary-first compiler-runtime framework that compiles skill packages offline into minimal executable interfaces. By extracting fine-grained operational boundaries from skills, SkillSmith enables agents to dynamically access and execute only the relevant components at runtime, thereby minimizing unnecessary context injection and redundant reasoning overhead. In the evaluation on SkillsBench benchmark, SkillSmith reduces solve-stage token usage by 57.44%, thinking iterations by 42.99%, solve time by 50.57% (2.02x faster), and token-proportional monetary cost by 57.44% compared with using raw-skills. Moreover, compiled artifacts produced by a stronger model can be reused by a smaller or more efficient runtime model, improving task accuracy in cases where raw skill interpretation fails. The source code and data are available at this https URL.
Subjects:
Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
Cite as: arXiv:2605.15215 [cs.AI]
(or arXiv:2605.15215v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.15215
arXiv-issued DOI via DataCite
Submission history
From: Zaifeng Pan [view email] [v1] Tue, 12 May 2026 09:25:25 UTC (464 KB)
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