AI RESEARCH
SkillRT: Compiling Skills for Efficient Execution Everywhere
arXiv CS.LG
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ArXi:2604.03088v1 Announce Type: cross 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.