WikiSkill让AI智能体把经验变成可复用的知识库，小模型靠技能能打败大模型，技能还能跨模型使用。
WikiSkill框架通过将执行经验、积累知识和可执行技能分离，持续将经验整合到知识库中。该框架在多个基准测试和模型上表现优于最先进的技能进化方法，在大多数模型-基准设置中优于无技能基线。研究发现技能进化与模型扩展互补：大模型通常从进化技能中获益更多，而有技能的小模型可以显著超越无技能的大模型。技能在不同模型和模型家族间能有效转移，其他模型进化的技能甚至可以超越自我进化的技能。
WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution
Agent skills package specialized knowledge and workflows into reusable resources that extend AI agent capabilities. Recent work automatically discovers such skills from agent experience, which enables agents to progressively adapt through interaction. However, the insights that guide skill development typically remain scattered across optimization histories, limiting their systematic reuse across iterations. We introduce WikiSkill, a framework that co-evolves agent skills with a persistent knowledge base (wiki). At a high level, WikiSkill separates raw execution experience, accumulated knowledge, and executable skills, while continuously consolidating experience into the wiki, which subsequent skill updates can build on. Across diverse benchmarks and models, WikiSkill consistently outperforms state-of-the-art skill-evolution methods and improves over no-skill baselines in most model-benchmark settings. We find that skill evolution complements model scaling: larger models generally benefit more from evolved skills, while smaller models with skills can outperform substantially larger models without them. We also find that evolved skills transfer effectively across models and model families, and skills evolved by other models can outperform self-evolved skills. Finally, our ablation studies confirm that persistent knowledge accumulation in the wiki is critical for effective skill evolution. These results demonstrate the benefits of systematically accumulating and refining agent experience for developing reusable and transferable skills.