SkillComp: Internalizing Skill Relations in Dense Retrievers for Complete Skill-Set Retrieval from Large-Scale Libraries

Reusable skills equip large language model agents with specialized capabilities for executing complex tasks, which often require multiple skills to work together. However, some skills needed in intermediate steps may be implicit in the task description, making it difficult for dense retrievers to retrieve the complete required skill set from a large skill library. To address this limitation, we propose SkillComp, which uses structural relations among required skills to construct additional training supervision for complete skill-set retrieval. Specifically, SkillComp selects related skills based on these relations and appends their descriptions to the query to form expanded queries, providing additional context for retrieving the remaining required skills. It then jointly trains the retriever on the plain and expanded queries using the complete required skill set as positive targets, thereby improving the alignment between the plain query and the complete required skill set. Experiments on SkillRet and SkillBench show that SkillComp improves skill retrieval, with particularly strong gains on multi-skill tasks. On the multi-skill subset of SkillRet, SkillComp improves Recall@10 and Completeness@10 over Qwen3-Embedding-0.6B by +33.08 and +48.54 points, respectively, and over the strongest baseline by +2.57 and +5.19 points. These results support that SkillComp improves complete-set retrieval, while retaining standard plain retrieval at inference.