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Long Term Compounding

CommunityPopular
kangarooking
long-term-compounding

当用户在选择合作者/生意模式/人生策略、问「要不要长期投入这段关系/这个项目」「如何积累声誉」时调用。 核心理念: 财富、知识、声誉、关系都遵循复利; 只玩长期正和游戏, 与能想象共事一辈子的人合作, 拒绝短期思维交易。 不适用于: 紧急止损、短期现金周转等必须立即决策的场景。 Triggers: 长期/复利/声誉/合作/信任/compounding/long-term/trust

Overview

Publisherkangarooking
Repositorycangjie-skill
Skill namelong-term-compounding
Stars
10.2K
Forks
1.2K
Bundled files
2
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 2 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 Long Term Compounding 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.
  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/cangjie-skill.git /tmp/cangjie-skill
mkdir -p .claude/skills
cp -r /tmp/cangjie-skill/books/naval-almanack-skill/long-term-compounding .claude/skills/long-term-compounding
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Long Term Compounding 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 Long Term Compounding 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 Long Term Compounding 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.

长期复利游戏

R — 原文 (Reading)

玩复利游戏。无论是财富，人际关系或是知识，所有你人生里获得的回报，都来自于复利。……我的联合创始人Nivi说，“在一个长期游戏里，好像每个人都在让彼此发财，而在一个短期游戏里，好像每个人都在让自己发财。”

— 纳瓦尔·拉维坎特, 《纳瓦尔宝典》 第一章·财富

I — 方法论骨架 (Interpretation)

把复利从金融概念升级为通用人生规律：财富、知识、声誉、关系都以指数方式积累， 所以选择的判据不是「现在能拿多少」，而是「这件事在十年尺度上是否复利」。 两个推论：① 只与「能想象共事一辈子」的长期伙伴合作——信任让谈判成本趋近于零； ② 只玩长期正和游戏——长期游戏里人人把饼做大，短期游戏里人人抢饼。 声誉是最典型的复利资产：持续几十年维护诚信，最终价值远超有才华但无声誉积累的人。 反面筛选信号：愤世嫉俗者、悲观主义者、短期思维者——他们会破坏复利结构。

A1 — 书中的应用 (Past Application)

案例 1: 与 Elad Gil 的交易

  • 问题: 商业谈判成本高、信任难建立
  • 方法论的使用: 与 Elad Gil 长期交易，对方主动多给好处、差额自掏腰包
  • 结论: 信任让常规谈判极简，彼此愿意让利
  • 结果: 作者几乎每笔交易都优先拉对方入局，关系进入复利循环

案例 2: 声誉换来别人做不了的交易

  • 问题: 为什么巴菲特能买到别人买不到的公司
  • 方法论的使用: 长期诚信+可靠+长期思维建立的声誉品牌
  • 结论: 「你的性格和你的声誉是可以建立的……你知道这不是运气」
  • 结果: 别人把「运气」当机会时，声誉者把机会变成确定收益

A2 — 触发场景 (Future Trigger) ★

用户会在什么情境下需要这个 skill?

  1. 评估合作/合伙/签约对象：「这个人能合作五年十年吗」
  2. 犹豫是否接受短期高回报交易：「这单来钱快但伤口碑」
  3. 想积累声誉/复利资产：「怎么让机会主动来找我」
  4. 关系决策：「这段关系值得长期投入吗」

语言信号

  • "长期 vs 短期怎么选"
  • "这个人靠谱吗/值得长期合作吗"
  • "怎么建立信任/声誉"
  • "compounding / long-term game / is this person trustworthy"

与相邻 skill 的区分

  • 与 game-selection 的区别: 本 skill 关注时间尺度（长期/短期）；game-selection 关注博弈结构（零和/正和/单人）
  • 与 peer-selection 的区别: 本 skill 的伙伴筛选服务于复利收益；peer-selection 服务于幸福与行为塑造

E — 可执行步骤 (Execution)

  1. 对候选机会跑复利测试

    • 完成标准: 回答「十年后它还值多少？现在投入的信任/时间/钱会不会指数增长？」
    • 判停条件: 若答案是「不可复利且只是快钱」，标记为短期游戏，慎重

  2. 对合作者跑『一辈子』测试

    • 完成标准: 问「我能想象和这个人共事/生活一辈子吗？」；不能，则一天也别开始

  3. 检查负向信号

    • 完成标准: 确认对方不是愤世嫉俗者/悲观主义者/短期思维者（他们要证明自己负面看法正确）

  4. 为声誉做一笔复利存款

    • 完成标准: 本周期内做一件「对方会记得的好事」，不记账、不估量

B — 边界 (Boundary) ★

不要在以下情况使用此 skill

  • 对方已在诈骗/违法边缘（先止损，不要用长期主义自我麻痹）
  • 用户急需短期现金流救急（先解决生存，再谈复利）

作者在书中警告的失败模式

  • 与愤世嫉俗者合作: 「他们会任由坏事发生，以证明他们负面看法是正确的」
  • 估量付出: 「不要去估量——一旦开始计较，你的耐性就会耗尽」

作者的盲点 / 时代局限

  • 长期游戏假设环境稳定可预期；在剧变行业/强监管环境，长期承诺也有风险
  • 「所有好处都来自复利」是强断言，未考虑不可复利的必要止损

容易混淆的邻近方法论

  • game-selection: 先识别博弈结构，再决定玩长期还是短期

相关 skills (阶段 3 定稿)

  • composes-with: wealth-structure（复利结构）、peer-selection（长期伙伴）
  • contrasts-with: game-selection（时间尺度 vs 博弈结构）

审计信息

  • 验证通过: V1 ✓ / V2 ✓ / V3 ✓ (v03)
  • 测试通过率: 见 test-results.md
  • 蒸馏时间: 2026-08-01

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 Long Term Compounding AI skill do?

当用户在选择合作者/生意模式/人生策略、问「要不要长期投入这段关系/这个项目」「如何积累声誉」时调用。 核心理念: 财富、知识、声誉、关系都遵循复利; 只玩长期正和游戏, 与能想象共事一辈子的人合作, 拒绝短期思维交易。 不适用于: 紧急止损、短期现金周转等必须立即决策的场景。 Triggers: 长期/复利/声誉/合作/信任/compounding/long-term/trust

Why use Long Term Compounding on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kangarooking/cangjie-skill/tree/main/books/naval-almanack-skill/long-term-compounding. 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 Long Term Compounding?

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 Long Term Compounding?

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

Is the Long Term Compounding 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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