构建智能体技能全生命周期治理框架，提升代码生成能力。
SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution
- 通过结构化技能库与上下文搜索，动态推荐可执行技能。
- 在百万级开源数据上验证技能质量，提升任务成功率37%以上。
- 适合开发长周期智能体系统的研究人员与工程师使用。
长周期大语言模型智能体产生的轨迹可成为可复用经验，但原始轨迹噪声大、局部性强且难以管理。智能体技能作为结构化成果，整合了操作指引、可执行资源与适用边界。然而开放技能生态存在冗余、质量不均、环境敏感等问题，随意更新会污染后续上下文。我们提出SkillsVote，一个覆盖技能收集、推荐、归属与演化的全生命周期治理框架。SkillsVote对百万级开源语料进行环境需求、质量与可验证性分析，并合成可验证任务以评估技能。执行前，通过结构化技能文件夹进行代理式库搜索，揭示指令上下文；执行后，将轨迹分解为关联技能的子任务，分别归因于技能引导、代理探索、环境因素和结果信号，并仅允许成功可复用发现进入证据约束的更新。在Terminal-Bench 2.0与SWE-Bench Pro上的实验表明，SkillsVote显著提升智能体在复杂代理编码基准上的表现。收益来自两条互补路径：测试时任务流中的在线演化，以及基于历史轨迹或精选开源技能构建的冻结库离线迁移。
原文摘要 · Abstract (English)
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 skills. Before execution, it performs agentic library search over structured skill folders to expose instructional context. After execution, it decomposes trajectories into skill-linked subtasks, attributes outcomes to skill-guided execution, agent exploration, environment, and result signals, and admits only successful reusable discoveries to evidence-gated updates. Experiments on Terminal-Bench 2.0 and SWE-Bench Pro show that SkillsVote improves agent performance on challenging agentic coding benchmarks. The gains arise from two complementary pathways: online evolution over task streams at test time and offline transfer via frozen libraries built from either historical trajectories or curated open source skills.
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