Beyond Functional Relevance: Benchmarking Cost-Aware Skill Retrieval for Efficient Agent Execution

As the scale of reusable Skills continues to grow, Skill Retrieval has become an important mechanism for LLM Agents to access task-relevant knowledge and operational guidance. Existing retrieval methods primarily rely on functional relevance, focusing on identifying task-supporting Skills. However, functional relevance alone may be insufficient for selecting Skills that support efficient downstream execution: viable Skills for the same task may guide Agents toward different technical approaches, resulting in substantially different downstream execution costs. To investigate this issue, we introduce **Cost-Aware Skill Retrieval** and construct the **Cost-Aware Skill Retrieval Benchmark**. Experiments on this benchmark support this concern, showing that strong recall of task-supporting Skills does not necessarily translate into a consistent preference for more execution-efficient Skills. Motivated by this finding, we develop a lightweight **Cost-Aware Reference Method** that uses a two-stage process to improve cost-aware Skill selection. Experimental results indicate that the method can favor lower-cost Skills and translate this preference into actual downstream savings, reducing Agent execution time by up to 51.55% and context-adjusted token consumption (excluding repeated preloaded-context tokens) by up to 46.37%, while maintaining comparable task performance. These findings provide a useful reference for future research in this direction.