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Compounding Thinking

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kangarooking
compounding-thinking

用户在做长期投资/储蓄/增长决策,需要理解时间和复利的力量,或被短期波动困扰时。 语言信号:"持有多久合适"/"年化X%十年后会怎样"/"为什么长期持有比频繁交易好"/"小收益怎么变成大财富" 不适用于:短期交易决策、不需要时间维度的分析。

Overview

Publisherkangarooking
Repositorybuffett-letters-skill
Skill namecompounding-thinking
Stars
109
Forks
20
Bundled files
1
LicenseMIT
Links
  • 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 Compounding Thinking AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kangarooking/buffett-letters-skill/tree/main/compounding-thinking
  4. 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.
2

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/buffett-letters-skill.git /tmp/buffett-letters-skill
mkdir -p .claude/skills
cp -r /tmp/buffett-letters-skill/compounding-thinking .claude/skills/compounding-thinking
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Compounding Thinking 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 Compounding Thinking 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 Compounding Thinking 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.

复利思维

R — 原文 (Reading)

"It is always startling to see how relatively small differences in rates add up to very significant sums over a period of years." (1962) "The weeds wither away in significance as the flowers bloom. Over time, it takes just a few winners to work wonders." (2022)

I — 方法论骨架

复利的三个关键洞察:

  1. 小差异×长时间=巨大差距:年化15%和20%在30年后差3.4倍
  2. 高增长必自毁:基数膨胀后维持同等增速需要越来越大的绝对增量——"派对结束"
  3. 少数赢家的绽放:不需要所有投资都成功——Coke $13亿→$250亿足以抵消所有错误

反面:增长可能摧毁价值(如果需要持续资本投入超过产出)

A1 — 书中的应用

案例: Coca-Cola + American Express

  • 使用: 1990年代各投$13亿,28年后Coke=$250亿+年股息$7亿,Amex=$220亿+年股息$3亿
  • 结果: 证明了"少数赢家的长期绽放"——两笔投资的总回报覆盖了所有其他错误

A2 — 触发场景

  1. 计算长期投资的预期回报
  2. 纠结是否应该卖出持仓
  3. 理解为什么"不行动"可能是最好的策略
  4. 评估短期波动是否值得担心
  5. 对比"频繁交易"vs"长期持有"的长期效果

语言信号

  • "持有多久合适?"
  • "年化X%十年后是多少?"
  • "为什么巴菲特不卖?"
  • "短期波动要不要管?"

与相邻 skill 的区分

  • 与 hold-forever 的区别:复利是数学原理,永久持有是具体策略

E — 可执行步骤

  1. 做复利计算——不要凭直觉

    • 完成标准:$1在目标回报率下5/10/20/30年的结果
  2. 识别"绽放花朵"

    • 完成标准:在投资组合中找出"少数赢家"——它们将贡献大部分回报
  3. 接受不均匀性

    • 完成标准:列出"宁愿颠簸的15%也不要平滑的12%"的理由

B — 边界

  • 复利假设回报率为正——负回报率下复利加速亏损
  • 高增长率不可持续——"增长会自毁"
  • 作者盲点:巴菲特享受了美国经济60年的顺风,其他市场可能不同

相关 skills (阶段 3 填充)

  • depends-on: hold-forever
  • composes-with: economic-moat, float-thinking

审计信息

  • 验证通过: V1 ✓ / V2 ✓ / V3 ✓
  • 蒸馏时间: 2026-04-16

Bundled files

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

Frequently asked questions

What does the Compounding Thinking AI skill do?

用户在做长期投资/储蓄/增长决策,需要理解时间和复利的力量,或被短期波动困扰时。 语言信号:"持有多久合适"/"年化X%十年后会怎样"/"为什么长期持有比频繁交易好"/"小收益怎么变成大财富" 不适用于:短期交易决策、不需要时间维度的分析。

Why use Compounding Thinking on TypingMind?

Because you install it once and use it with any model. Compounding Thinking 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 Compounding Thinking in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kangarooking/buffett-letters-skill/tree/main/compounding-thinking. 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 Compounding Thinking?

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 Compounding Thinking?

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

Is the Compounding Thinking 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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