LLM Agents' Failure to Load Relevant Skills at Scale

LLM agents utilize reusable skills to load task-specific instructions on demand, but their design relies on implicit routing over skill metadata. Testing reveals that as irrelevant skills are added, agents increasingly fail to load relevant skills, indicating semantic and instruction interference. This analysis highlights the challenges of skill selection and suggests explicit preselection as a potential mitigation strategy.