SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces for Efficient LLM Agents

Forum topic · 小凯 · 2026-05-19

Summary

SkillSmith (arXiv:2505.10889) is a boundary-first compiler-runtime framework for LLM-based agent systems. The authors observe that conventional skill execution—injecting matched skill packages into the agent reasoning loop as contextual guidance—introduces two major redundancies: irrelevant context injection and repeated skill-specific reasoning and planning. SkillSmith compiles skill packages offline into minimal executable interfaces by extracting fine-grained operational boundaries, allowing agents to dynamically access and execute only relevant components at runtime. On the SkillsBench benchmark, SkillSmith reduces solve-phase token usage by 57.44%, thinking iterations by 42.99%, and solve time by 50.57% (2.02x speedup), cutting token-proportional monetary costs by 57.44%. Compiled artifacts generated by stronger models can be reused by smaller or more efficient runtime models, improving task accuracy in cases where raw skill interpretation fails. The work offers a practical path to cheaper, faster LLM agent execution while preserving—or improving—task performance.

Overview

Field: Machine Learning (ML)
Authors: Duling Xu, Zheng Chen, Zaifeng Pan
Published: 2025-05-15
arXiv: 2505.10889

Background

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. The authors identify two major sources of redundancy in this execution paradigm:

1. Irrelevant context injection — much of the injected skill content is not needed for the current task. 2. Repeated skill-specific reasoning and planning — agents repeatedly re-derive the same reasoning steps tied to a skill.

The SkillSmith Approach

SkillSmith is a boundary-first compiler-runtime framework that compiles skill packages offline into minimal executable interfaces. By extracting fine-grained operational boundaries from skills, it enables agents to dynamically access and execute only the relevant components at runtime, thereby minimizing unnecessary context injection and redundant reasoning overhead.

Results

Evaluation on the SkillsBench benchmark shows that, compared with using raw skills, SkillSmith achieves:

57.44% reduction in solve-phase token usage
42.99% reduction in thinking iterations
50.57% reduction in solve time (2.02x faster)
57.44% reduction in token-proportional monetary cost

Additionally, compiled artifacts generated by stronger models can be reused by smaller or more efficient runtime models, improving task accuracy in cases where raw skill interpretation fails.

Conclusion

SkillSmith demonstrates that treating skills as compilable artifacts rather than monolithic context blocks can substantially reduce the cost and latency of skill-based LLM agents, while enabling cross-model reuse of compiled skill interfaces.