# SkillsVote：面向智能体技能收集、推荐与演进的全周期治理框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-18 08:00
- AIHOT 分数：60
- AIHOT 链接：https://aihot.virxact.com/items/cmpcaa48e00nhslaetgxwfxri
- 原文链接：https://arxiv.org/abs/2605.18401

## AI 摘要

针对大语言模型智能体在生成可复用经验轨迹时面临的噪声与治理难题，本文提出了SkillsVote框架。该框架将智能体技能定义为可执行脚本与流程指导的结合，并对百万规模的开源技能库进行环境、质量与可验证性评估。在技能执行前后，框架分别通过结构化检索与轨迹分解归因，仅将成功且可复用的发现纳入基于证据的更新。实验表明，该框架能在不更新模型本身的情况下，显著提升固定模型智能体的性能。

## 正文

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. SkillsVote profiles a million-scale open-source corpus for environment requirements, quality, and verifiability, then synthesizes tasks for verifiable skills. Before execution, SkillsVote performs agentic library search over 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 to evidence-gated updates. In our evaluation, offline evolution improves GPT-5.2 on Terminal-Bench 2.0 by up to 7.9 pp, while online evolution improves SWE-Bench Pro by up to 2.6 pp. Overall, governed external skill libraries can improve frozen agents without model updates when systems control exposure, credit, and preservation.