Skill-augmented agents increasingly rely on large reusable skill libraries, but retrieving relevant skills is not the same as presenting usable context. Existing methods typically return atomic skills or dependency-aware bundles whose internal roles remain implicit, leaving the agent to infer the execution entry point, support skills, visible requirements, and failure-avoidance guidance. We introduce Group of Skills (GoSkills), an inference-time group-structured retrieval method that changes the agent-facing retrieval object from a flat skill list to a compact, role-labeled execution context. GoSkills builds anchor-centered skill groups from a typed skill graph, expands support groups through a group graph, bottlenecks the selected group plan into a bounded set of atomic skill payloads, and renders a fixed execution contract with Start, Support, Check, and Avoid fields, without changing the downstream agent, skill payloads, or execution environment. Experiments on SkillsBench and ALFWorld show that GoSkills preserves visible-requirement coverage under a small skill budget, improves over flat skill-access baselines, and often improves reward and agent-only runtime relative to structural retrieval references.
@article{arxiv.2605.06978,
title = {Group of Skills: Group-Structured Skill Retrieval for Agent Skill Libraries},
author = {Kun Zeng and Yu Huo and Siyu Zhang and Zi Ye and Yuecheng Zhuo and Haoyue Liu and Yuquan Lu and Junhao Wen and Xiaoying Tang},
journal= {arXiv preprint arXiv:2605.06978},
year = {2026}
}