SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution
Long-horizon LLM agents generate traces that could become reusable experience, but raw trajectories are noisy, local, and hard to govern. Agent Skills offer a structured artifact for combining procedural guidance, executable resources, and applicability boundaries. 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 across collection, r
Record details
Published: 18 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (18 May 2026), “SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution,” evidence record 4117, https://ethics.ai/record/4117 (originally published by arXiv).
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