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About this paper
Yipeng Ouyang, Yi Xiao, Yuhao Gu, Xianwei Zhang
SKCC, developed by Sun Yat-sen University, introduces a compilation framework for LLM agents that enhances skill portability across diverse agent frameworks and proactively enforces security. The system improves task pass rates by an average of 7.0 percentage points, reduces dynamic token consumption by 10-23%, and cuts execution time by 23-43% by generating framework-optimized skill formats and integrating compile-time security checks.
Key insights
- Applying classical compiler principles, particularly using an intermediate representation, systematically addresses both skill portability across diverse LLM agent frameworks and compile-time security enforcement.
- Optimal LLM agent performance necessitates framework-specific formatting, as no single skill format performs best across all models (e.g., Claude benefits from XML, Gemini from conditional YAML for nesting).
- Proactive, compile-time security analysis (Anti-Skill Injection) provides a more robust "fail-fast" defense against malicious or insecure skills compared to reactive runtime guardrails.
Summary by alphaXiv.
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