LLM Agent Skill Retrieval

Agent skill retrieval studies how an agent can find and use procedural guidance—such as coding workflows or web-task instructions—from a reusable library. Methods include lexical and dense search with reranking, structured or graph-based organization, query decomposition, and context-budgeted selection and adaptation. Evaluations use interactive environments such as ALFWorld and WebShop alongside coding and web benchmarks, measuring both retrieval quality and whether retrieved skills improve task completion. A central challenge is that relevant-looking skills can be mismatched, conflicting, or ignored, so larger libraries require better tests of actual usefulness, safe selection, and performance across models and agent harnesses.