AGENTS.md outperforms skills in our agent evals

This post introduces a new approach for providing framework-specific knowledge to AI coding agents by embedding a compressed documentation index directly into `AGENTS.md`. This method achieved a 100% pass rate in evaluations for Next.js 16 APIs, significantly outperforming the use of 'skills' which only achieved a 79% pass rate even with explicit instructions. The key advantages identified are the elimination of agent decision points for knowledge retrieval, consistent availability of context, and avoidance of ordering issues inherent in active retrieval systems. The post also details the compression techniques used to keep the `AGENTS.md` file size minimal (8KB) and provides a codemod for easy implementation.