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xuefeng-method

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雪峰式AI-Native产品开发方法论。适用于：(1) 用户行为开放、不可穷举的AI-native产品（AI日历、AI助手、AI推荐、对话式产品等），(2) 强模型依赖型场景，AI驱动核心决策而非仅辅助，(3) 多专精Agent架构设计与分工，(4) 上线后快速校准、行为审计与漂移检测，(5) 模型选择和智能路由策略，(6) 概率性输出的质量评估。触发场景包括"AI-native产品怎么做"、"用户行为不可预测怎么办"、"多agent怎么分工"、"模型漂移怎么处理"、"校准到95%太难了"、"唯快不破"、"怎么选模型"、"agent并行分工"、"AI产品上线后怎么迭代"。注意：如果产品是场景明确、边界可定义的+AI类型，请改用 keqian-method skill。即使用户没有明确说"AI-native"，但在讨论AI驱动决策、用户行为不可预测、概率性输出等话题时也应触发。

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# 雪峰方法论：AI-Native 产品开发实战体系

> 核心理念：强模型依赖 × 多专精Agent × 快速校准 × 行为审计
>
> 来源：雪峰——AI-Native连续创业者，深耕AI日历管理等AI驱动产品。
> 核心洞察：穷举是死循环，唯快不破才是AI-native的生存之道。
>
> 与克谦方法论（keqian-method）互为对偶：
> 克谦解决"如何让AI在明确边界内可靠执行"，
> 雪峰解决"当边界本身不确定时怎么办"。

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## 第零步：产品类型判断（必须先做）
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