SkVM：通用技能编译框架实现高效跨平台执行

摘要

随着LLM智能体逐渐将技能作为可复用的组合单元，当前各智能体平台虽能共享技能，却仅将其视为原始上下文处理，导致同一技能在不同智能体间表现不一致。这种脆弱性严重影响了技能的移植性与执行效率。为解决该问题，我们分析了11.8万个技能库，并从传统编译器设计中获得启发：将技能视为代码，将LLM视作异构处理器。为实现可操作的技能移植，我们将技能需求分解为一系列基础能力单元，并量化评估不同模型-框架组合对这些能力的支持程度。基于能力画像，我们提出SkVM——专为可移植、高效率技能执行设计的编译与运行时系统。编译阶段，SkVM执行基于能力的编译、环境绑定与并发提取；运行时则通过JIT代码固化与自适应重编译进行性能优化。我们在8种不同规模的LLM与3类智能体框架上评估SkVM，覆盖SkillsBench基准及典型技能任务。实验表明，SkVM能显著提升不同模型与环境下的任务完成率，同时降低最高40%的token消耗。性能方面，SkVM通过增强并行性实现最高3.2倍加速，借助代码固化将延迟降低19-50倍。

English

LLM agents increasingly adopt skills as a reusable unit of composition. While skills are shared across diverse agent platforms, current systems treat them as raw context, causing the same skill to behave inconsistently for different agents. This fragility undermines skill portability and execution efficiency. To address this challenge, we analyze 118,000 skills and draw inspiration from traditional compiler design. We treat skills as code and LLMs as heterogeneous processors. To make portability actionable, we decompose a skill's requirements into a set of primitive capabilities, and measure how well each model-harness pair supports them. Based on these capability profiles, we propose SkVM, a compilation and runtime system designed for portable and efficient skill execution. At compile time, SkVM performs capability-based compilation, environment binding, and concurrency extraction. At runtime, SkVM applies JIT code solidification and adaptive recompilation for performance optimization. We evaluate SkVM across eight LLMs of varying scales and three agent harnesses, covering SkillsBench and representative skill tasks. Results demonstrate that SkVM significantly improves task completion rates across different models and environments while reducing token consumption by up to 40%. In terms of performance, SkVM achieves up to 3.2x speedup with enhanced parallelism, and 19-50x latency reduction through code solidification.

SkVM：通用技能编译框架实现高效跨平台执行

SkVM: Compiling Skills for Efficient Execution Everywhere

摘要

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