{
  "id": 16102,
  "url": "https://arxiv.org/abs/2608.02356v1",
  "title": "SkillTrace: Traversing a Query-Skill Graph for Composable LLM Agents",
  "summary": "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 candida",
  "authors": "Yue Yao, Shengyuan Wang, Xin Chen, Minke Zhang, Jia He, Bingjun Luo, Tom Gedeon",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T15:07:11.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16102",
  "original_url": "https://arxiv.org/abs/2608.02356v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}