Scientific Decision

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kangarooking

scientific-decision

用四步流程做出理性客观的决策：紧盯目标过滤小事→找第三选择突破选项限制→绕开认知偏误→量化打分做最终选择。适用于面临重大选择纠结不已、选项看似非此即彼、或担心被直觉误导的场景。不适用于日常低风险小决策（过度量化反而浪费时间）。关键触发信号："纠结选A还是B"、"不知道怎么选"、"两个都不满意但必须选一个"、"帮我做个决定"。

Overview

Publisher	kangarooking
Repository	`cognitive-dividend-skill`
Skill name	`scientific-decision`
Stars	54
Forks	13
Bundled files	1
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by kangarooking on GitHub. Read the source before you install it.

Installation

Install the Scientific Decision AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/kangarooking/cognitive-dividend-skill/tree/main/scientific-decision
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/kangarooking/cognitive-dividend-skill.git /tmp/cognitive-dividend-skill
mkdir -p .claude/skills
cp -r /tmp/cognitive-dividend-skill/scientific-decision .claude/skills/scientific-decision

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scientific Decision in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Scientific Decision on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Scientific Decision is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

科学决策与第三选择

R — 原文 (Reading)

第一步：紧盯核心目标，过滤大多数问题，不在小事上纠缠；第二步：面对关键问题，不要纠结于仅有的选项，要找到核心目标，将问题转化成简答题，并寻找第三选择；第三步：绕开认知偏误，开启系统2，进入慢思考；第四步：制定标准，给所有的选项打分，量化结果比大小。 —— 第21章《如何做出科学的决策》

I — 方法论骨架 (Interpretation)

科学决策四步是从粗到细的漏斗：先砍掉不重要的事，再突破选项框架，再排除偏误，最后量化
第一步：紧盯目标，过滤小事。大部分纠结不值得纠结，聚焦核心目标
第二步：找第三选择。面对选择题时，不要只看选项本身——还原目标→把选择题变简答题→找双赢方案
第三步：绕开认知偏误。三大偏误：反射效应（面对损失时赌更大）、确定效应（面对收益时求安稳）、锚定效应（被先入为主的数值锚定）
开启"系统2"（慢思考），用理性分析替代直觉反应
第四步：量化决策。分配权重→逐项打分→加权求和→数字说话
37%法则（麦穗理论）：对于不可回头的一次性选择，前37%时间只观察不选建立基准，之后遇到超过基准的立即下手
第三选择的本质是跳出"非此即彼"的框架，找到让双方利益最大化的新方案

A1 — 书中应用 (Past Application)

选老公案例：用量化决策表，将"过去、当下、未来"三大类分配权重（如30%/50%/20%），每类下再分子项目打分，最终用数字替代纠结
麦穗理论择偶：22-30岁择偶，前3年（37%）只观察不选，建立最高分基准，后5年遇到超过基准的立刻下手
第三选择——两把宝剑：选锋利还是好看的？还原目标（上战场还是当装饰），目标确定后答案自动出现
认知偏误——购物：两件同价399元的衣服，一件原价399，一件原价3999打一折，人们觉得第二件便宜——这就是锚定效应

A2 — 触发场景 (Future Trigger)

何时使用：

面临重大人生/商业选择，纠结不已
两个选项看似都不满意，但感觉"必须选一个"
担心自己的判断被情绪或直觉误导
需要向他人证明决策的合理性（而非拍脑袋）

语言信号：

"纠结选A还是B"
"两个都不好，但必须选一个"
"帮我做个决定"
"怎么选才不会后悔？"
"我觉得这个选择很重要"

与相邻skill区分：

vs. structured-thinking：结构化是"怎么拆解问题"，科学决策是"怎么从选项中做出最优选择"；结构化是决策的前置步骤
vs. systems-thinking：系统性思维分析"为什么会这样"，科学决策决定"接下来怎么办"

E — 可执行步骤 (Execution)

步骤1：还原目标，判断问题等级

写下你真正要达成的核心目标（不是选项本身）
判断这个问题是否值得花时间决策——80%的小事直接过滤
如果是关键问题，进入下一步
完成标准：能用一句话写出核心目标，并确认"这个问题值得深度决策"

步骤2：突破选项框架，寻找第三选择

列出你目前面临的选项，问自己："这些选项是谁给我的？"
回到核心目标，把选择题转化成简答题："为了达成目标，我还能怎么做？"
寻找能让相关方利益最大化的第三方案
如果涉及不可回头的选择，应用37%法则：前37%时间只观察建基准
完成标准：至少产出一个超出原有选项的新方案，或确认已用37%法则建立了基准线

步骤3：绕开偏误，量化打分

自检三大偏误：我是否因害怕损失而冒险（反射效应）？是否因贪图确定而放弃更优选项（确定效应）？是否被某个锚定值影响（锚定效应）？
建立量化表格：列出评估维度 → 分配权重 → 对每个选项逐项打分（0-100）
加权求和，用数字比大小
完成标准：每个选项都有一个加权总分，最高分即为理性最优选

B — 边界 (Boundary)

不要用的场景：

日常低风险小决策（吃啥、穿啥），量化决策浪费时间和精力
需要极速反应的场景（危机处理），来不及走四步流程
纯主观审美的个人偏好问题

失败模式：

过度量化：给本该凭直觉的维度强行打分，数字看似精确实则虚假
权重设置偏差：权重分配本身就反映了偏好，如果偏好被偏误影响，量化也无意义
分析瘫痪：四步走完还是不敢选，陷入无限循环

作者盲点：

对"如何确定权重分配是否合理"缺乏自检机制，权重本身就是最大的主观判断点
37%法则假设评估对象是随机出现的，但现实中人可以通过主动搜索改变概率分布

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

test-prompts.json2 KB

Frequently asked questions

What does the Scientific Decision AI skill do?

Why use Scientific Decision on TypingMind?

Because you install it once and use it with any model. Scientific Decision is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Scientific Decision in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kangarooking/cognitive-dividend-skill/tree/main/scientific-decision. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Scientific Decision?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Scientific Decision?

As many as you like. As long as a model supports skills, you can use Scientific Decision with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Scientific Decision AI skill free?

Yes. It is published on GitHub by kangarooking under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Scientific Decision

Overview