| title | SkCC: Portable and Secure Skill Compilation for Cross-Framework LLM Agents |
|---|---|
| authors | Yipeng Ouyang, Yi Xiao, Yuhao Gu, Xianwei Zhang |
| date | 2026-05-05 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.03353 |
| affiliation | Sun Yat-Sen University |
| upvotes | 4 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | LLM-Agents have evolved into autonomous systems for complex task execution, with the SKILL.md specification emerging as a de facto standard for encapsulating agent capabilities. However, a critical bottleneck remains: different agent frameworks exhibit starkly different sensitivities to prompt formatting, causing up to 40% performance variation, yet nearly all skills exist as a single, format-agnostic Markdown version. Manual per-platform rewriting creates an unsustainable maintenance burden, while prior audits have found that over one third of community skills contain security vulnerabilities. To address this, we present SkCC, a compilation framework that introduces classical compiler design into agent skill development. At its core, SkIR - a strongly-typed intermediate representation - decouples skill semantics from platform-specific formatting, enabling portable deployment across heterogeneous agent frameworks. Around this IR, a compile-time Analyzer enforces security constraints via Anti-Skill Injection before deployment. Through a four-phase pipeline, SkCC reduces adaptation complexity from O(m times n) to O(m + n). Experiments on SkillsBench demonstrate that compiled skills consistently outperform their original counterparts, improving pass rates from 21.1% to 33.3% on Claude Code and from 35.1% to 48.7% on Kimi CLI, while achieving sub-10ms compilation latency, a 94.8% proactive security trigger rate, and 10-46% runtime token savings across platforms. |
cite
@misc{ouyang2026skcc,
title={SkCC: Portable and Secure Skill Compilation for Cross-Framework LLM Agents},
author={Yipeng Ouyang and Yi Xiao and Yuhao Gu and Xianwei Zhang},
year={2026},
eprint={2605.03353},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.03353}
}