SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents
Large language model agents increasingly solve complex tasks by composing reusable skills from a library. To address this, the key challenge is not merely to retrieve individually relevant skills, but to identify a complete and executable skill composition. In this paper, we argue that this problem can be solved in a graph with three levels: compositional relations among skill queries, similarity between queries and candidates in the skill library, and the dependencies among the selected candidates. We introduce SkillTrace, which organizes the user query into a semantic hierarchy, matches skill queries and candidates, and propagates over the skill dependencies. Experiments on SkillsBench ...
| Authors | Yue Yao, Shengyuan Wang, Xin Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 August 2026 |
| Last updated at source | 4 August 2026 |
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