SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution
Summary
SkillsVote is a governance framework for long-horizon LLM agents that manages reusable skills through structured collection, recommendation, and evolution, improving performance on Terminal-Bench 2.0 and SWE-Bench Pro without model updates.
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Paper page - SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution
Source: https://huggingface.co/papers/2605.18401
Abstract
SkillsVote is a governance framework for long-horizon LLM agents that manages reusable skills through structured collection, recommendation, and evolution processes.
Long-horizon LLM agents leave traces that could become reusable experience, but raw trajectories are noisy and hard to govern. We treatAgent Skillsas anexperience schemathat couplesexecutable scripts, with non-executable guidance on procedures. Yet openskill ecosystemscontain redundant, uneven, environment-sensitive artifacts, and indiscriminate updates can pollute future context. We present SkillsVote, alifecycle-governance frameworkforAgent Skillsfrom collection and recommendation to evolution. SkillsVote profiles a million-scale open-source corpus forenvironment requirements, quality, andverifiability, then synthesizes tasks for verifiable skills. Before execution, SkillsVote performsagentic library searchover structured skill library to expose instructional skill context. After execution, it decomposes trajectories into skill-linked subtasks, attributes outcomes to skill use, agent exploration, environment, and result signals, and admits only successful reusable discoveries toevidence-gated updates. In our evaluation, offline evolution improves GPT-5.2 onTerminal-Bench 2.0by up to 7.9 pp, while online evolution improvesSWE-Bench Proby up to 2.6 pp. Overall, governed external skill libraries can improve frozen agents without model updates when systems control exposure, credit, and preservation.
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