AI RESEARCH
SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution
arXiv CS.CL
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ArXi:2605.18401v1 Announce Type: new Long-horizon LLM agents leave traces that could become reusable experience, but raw trajectories are noisy and hard to govern. We treat Agent Skills as an experience schema that couples executable scripts, with non-executable guidance on procedures. Yet open skill ecosystems contain redundant, uneven, environment-sensitive artifacts, and indiscriminate updates can pollute future context. We present SkillsVote, a lifecycle-governance framework for Agent Skills from collection and recommendation to evolution.