[2605.06978] Group of Skills: Group-Structured Skill Retrieval for Agent Skill Libraries
[Submitted on 7 May 2026]
Title:Group of Skills: Group-Structured Skill Retrieval for Agent Skill Libraries
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Abstract: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.
Comments: 30 pages, 4 figures, 24 tables
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.06978 [cs.CL]
(or arXiv:2605.06978v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.06978
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yu Huo [view email] [v1] Thu, 7 May 2026 21:51:59 UTC (6,114 KB)
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