SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents

2026-08-03

AI Summary

This work addresses the challenge that large language model agents struggle to effectively retrieve and compose executable skill sequences for complex tasks. To this end, the authors propose a unified three-layer graph structure that jointly models the semantic hierarchy of user queries, query-skill alignment, and inter-skill dependency constraints. By integrating graph traversal and semantic propagation algorithms with large language models, the approach enables end-to-end automatic discovery of executable skill paths. This is the first method to formalize skill composition as a unified graph-based reasoning framework. It achieves state-of-the-art performance with success rates of 53.17% on SkillsBench and 91.43% on ALFWorld, significantly outperforming existing approaches while demonstrating consistent gains across different backbone language models.

Abstract

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 and ALFWorld demonstrate that SkillTrace achieves state-of-the-art performance, reaching a success rate of 53.17% on SkillsBench and 91.43% on ALFWorld. SkillTrace also delivers consistent improvements across different backbone language models, demonstrating the generality and robustness of graph-based skill retrieval.

Problem

• composable LLM agents
• skill composition
• skill retrieval
• query-skill graph
• executable skill sequence

Innovation

• SkillTrace
• composable LLM agents
• query-skill graph
• skill composition
• graph-based retrieval