An empirical test evaluated a "skills as semantic router" pattern for AI agents, specifically using Anthropic's Claude Code. The test indexed 686 skills into a memory system, achieving 62.5% top-1 accuracy and 87.5% top-5 accuracy with sub-second latency. This approach significantly reduced token usage compared to traditional methods, saving approximately 456 times the context window by only loading full skill bodies when invoked. AI
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IMPACT This semantic routing pattern could significantly improve the efficiency and scalability of AI agents by reducing token costs and improving skill selection accuracy.
RANK_REASON The cluster describes an empirical test and results of a specific pattern for AI agent skill selection, which constitutes research. [lever_c_demoted from research: ic=1 ai=1.0]