functional-area-resolver

To optimize AI agent routing files and reduce context budget waste, compress granular skill-per-row tables into functional-area dispatchers. This transformation maintains routing accuracy while significantly reducing file sizes for better context management.

A functional-area dispatcher in LLM routing groups granular sub-skills into broader functional areas. It helps language models recognize and route to the correct functional area based on intent, providing visibility into available sub-skills for easy access.

Compress an AGENTS.md or RESOLVER.md routing file by running the functional-area-resolver skill on it. This transforms skill-per-row tables into functional-area dispatchers, optimizing the file for improved context management and routing accuracy.

Compressing routing tables does not negatively affect LLM routing accuracy. The process ensures that routing accuracy is maintained even after compression, while improving area recognition and providing dispatcher visibility for sub-skills.

You need to compress routing files for AI agent workflows when file size and complexity threaten to consume too much context budget. Compression optimizes routing files, ensuring context budget is used more efficiently for actual work.

Managing AI agent workflows without reducing routing file sizes is possible but inefficient. Large, granular routing tables waste context budget, whereas compressing them into functional-area dispatchers ensures better context management and routing accuracy.