SkVM: Revisiting Language VM for Skills across Heterogenous LLMs and Harnesses
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 SkV...
| Authors | Le Chen, Erhu Feng, Yubin Xia et al. |
|---|---|
| Primary category | cs.SE |
| Published | 3 April 2026 |
| Last updated at source | 11 April 2026 |
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