How I turned 18% skill hit rate into 95% — without calling an embedding API once

To improve a language model's skill hit rate from 18% to 95%, an engineer developed a custom solution without using an embedding API. The solution involved fusing multiple signals via Reciprocal Rank Fusion. This architecture has been independently verified by multiple projects. It can be useful for agents with 50+ skills experiencing keyword matching issues. Consider implementing this solution if you're experiencing similar problems.