高风险决策支持系统

构建一个名为“Pivot”的高风险决策支持系统——一个用于重大人生和商业决策的结构化思考工具。 这不同于简单的利弊清单。其价值在于结构化的分析过程，而非输出文档。 核心功能：

• 决策录入：用户描述决策（他们在哪些选项之间做选择）、他们的约束条件（时间、金钱、人际关系、义务）、他们明确表述的价值观（前3项）、他们当前的倾向，以及他们的截止期限
• 强制澄清问题：[LLM API] 生成5个问题，旨在揭示用户具体决策中隐藏的假设和未言明的权衡。用户必须回答全部5个问题后才能继续。这些问题的质量就是产品的质量
• 六个分析框架（每个作为单独的API调用运行，显示在标签页中）： (1) 期望值——每个选项下按概率加权的各种结果 (2) 遗憾最小化——哪个选项是你在80岁时最不可能后悔的 (3) 价值观一致性——哪个选项与你明确表述的价值观最一致，并附具体证据 (4) 可逆性指数——如果选错了，每个选项有多容易被撤销 (5) 二阶效应——每个选项在6个月和3年后会带来什么后续影响 (6) 给朋友的忠告——如果一位值得信赖的朋友描述了完全相同的情况，你会告诉他什么？
• 魔鬼代言人简报：一份单独的分析，尽可能强烈地论证反对用户当前的倾向——在6个框架之后显示
• 决策记录：连同所有分析和最终做出的决定一起存储。用户在90天和1年时更新实际结果

技术栈：React、[LLM API]，每个分析框架配一个精心设计的提示词，localStorage。专注、严肃的设计——没有游戏化，没有鼓励。这处理的是真实决策。

Build a high-stakes decision support system called "Pivot" — a structured thinking tool for major life and business decisions. This is distinct from a simple pros/cons list. The value is in the structured analytical process, not the output document. Core features:

• Decision intake: user describes the decision (what they're choosing between), their constraints (time, money, relationships, obligations), their stated values (top 3), their current leaning, and their deadline
• Mandatory clarifying questions: [LLM API] generates 5 questions designed to surface hidden assumptions and unstated trade-offs in the user's specific decision. User must answer all 5 before proceeding. The quality of these questions is the quality of the product
• Six analytical frames (each run as a separate API call, shown in tabs): (1) Expected value — probability-weighted outcomes under each option (2) Regret minimization — which option you're least likely to regret at age 80 (3) Values coherence — which option is most consistent with stated values, with specific evidence (4) Reversibility index — how easily each option can be undone if it's wrong (5) Second-order effects — what follows from each option in 6 months and 3 years (6) Advice to a friend — if a trusted friend described this exact situation, what would you tell them?
• Devil's advocate brief: a separate analysis arguing as strongly as possible against the user's current leaning — shown after the 6 frames
• Decision record: stored with all analysis and the final decision made. User updates with actual outcome at 90 days and 1 year

Stack: React, [LLM API] with one carefully crafted prompt per analytical frame, localStorage. Focused, serious design — no gamification, no encouragement. This handles real decisions.