什么是 curiosity-loop-decision-making?
一种结构化方法,用于从可信网络收集高信号、上下文反馈,以验证想法或指导复杂的职业决策。当面临“岔路口”职业选择、优先考虑产品功能或缩小公开演讲或内容的主题时,请使用此选项。 来源:samarv/shanon。
一种结构化方法,用于从可信网络收集高信号、上下文反馈,以验证想法或指导复杂的职业决策。当面临“岔路口”职业选择、优先考虑产品功能或缩小公开演讲或内容的主题时,请使用此选项。
通过命令行快速安装 curiosity-loop-decision-making AI 技能到你的开发环境
来源:samarv/shanon。
A Curiosity Loop is a lightweight, structured process for de-risking decisions by soliciting targeted input from a curated group of peers. Unlike generic "advice-seeking," which often results in non-contextual or biased suggestions, this framework forces specificity and reveals "surprises" you might have missed.
Formulate a Specific Question A good question must be specific, solicit rationale, and remain unbiased. Avoid "garbage in, garbage out" by giving respondents a concrete anchor.
Curate the Loop Select 5–10 people to ensure you receive at least 3–5 high-quality responses. Balance your list across two dimensions:
一种结构化方法,用于从可信网络收集高信号、上下文反馈,以验证想法或指导复杂的职业决策。当面临“岔路口”职业选择、优先考虑产品功能或缩小公开演讲或内容的主题时,请使用此选项。 来源:samarv/shanon。
为搜索与 AI 引用准备的稳定字段与命令。
npx skills add https://github.com/samarv/shanon --skill curiosity-loop-decision-making一种结构化方法,用于从可信网络收集高信号、上下文反馈,以验证想法或指导复杂的职业决策。当面临“岔路口”职业选择、优先考虑产品功能或缩小公开演讲或内容的主题时,请使用此选项。 来源:samarv/shanon。
打开你的终端或命令行工具(如 Terminal、iTerm、Windows Terminal 等) 复制并运行以下命令:npx skills add https://github.com/samarv/shanon --skill curiosity-loop-decision-making 安装完成后,技能将自动配置到你的 AI 编程环境中,可以在 Claude Code、Cursor 或 OpenClaw 中使用
https://github.com/samarv/shanon