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, recommendation, attribution, and evolution. SkillsVote profiles a million-scale open source corpus for environment requirements, quality, and verifiability, and synthesizes tasks for verifiable skil...
| Authors | Hongyi Liu, Haoyan Yang, Tao Jiang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 May 2026 |
| Last updated at source | 14 June 2026 |
Models and datasets referencing this paper
- IAAR-Shanghai/SkillsVoteDataset
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