SkCC: Portable and Secure Skill Compilation for Cross-Framework LLM Agents
Researchers have introduced SkCC, a novel compiler designed to enhance the portability and security of skills used by Large Language Model (LLM) agents. Currently, agent frameworks suffer from significant performance variations due to sensitivity to prompt formatting, requiring costly rewrites for each framework and leaving security vulnerabilities unaddressed. SkCC addresses these issues by introducing SkIR, a strongly-typed intermediate representation that decouples skill semantics from framework-specific formatting. This approach allows for portable deployment across different agent frameworks. Additionally, a static optimizer within SkCC enforces security constraints to block vulnerabilities before deployment. The system operates via a four-phase pipeline, reducing adaptation complexity significantly. Experimental results on SkillsBench show substantial improvements, with pass rates increasing notably on Claude Code and Kimi CLI. The design also achieves sub-10ms compilation latency, a high proactive security trigger rate, and significant runtime token savings, marking a major advancement in efficient and secure LLM agent development.
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SkCC: Portable and Secure Skill Compilation for Cross-Framework LLM Agents
Researchers have introduced SkCC, a novel compiler designed to enhance the portability and security of skills used by Large Language Model (LLM) agents. Currently, agent frameworks suffer from significant performance variations due to sensitivity to prompt formatting, requiring costly rewrites for each framework and leaving security vulnerabilities unaddressed. SkCC addresses these issues by introducing SkIR, a strongly-typed intermediate representation that decouples skill semantics from framework-specific formatting. This approach allows for portable deployment across different agent frameworks. Additionally, a static optimizer within SkCC enforces security constraints to block vulnerabilities before deployment. The system operates via a four-phase pipeline, reducing adaptation complexity significantly. Experimental results on SkillsBench show substantial improvements, with pass rates increasing notably on Claude Code and Kimi CLI. The design also achieves sub-10ms compilation latency, a high proactive security trigger rate, and significant runtime token savings, marking a major advancement in efficient and secure LLM agent development.
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