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Principal Agent

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kangarooking
principal-agent

当用户选择职业/组织/合作方式、困惑「为什么大公司磨洋工/小公司拼命」「该不该自己干」时调用。 核心理念: 委托人(主人)会把事做好, 代理人会为自己利益优化; 收益与创造价值绑得越紧, 越像委托人; 别让媒体洗脑你需要代理人。 不适用于: 具体薪酬谈判数字、组织架构设计细节。 Triggers: 激励/代理/主人/打工 vs 创业/利益绑定/principal/agent/incentive

Overview

Publisherkangarooking
Repositorycangjie-skill
Skill nameprincipal-agent
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 Principal Agent 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/principal-agent .claude/skills/principal-agent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Principal Agent 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 Principal Agent 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 Principal Agent 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.

委托-代理识别

R — 原文 (Reading)

如果你想做好，那你必须亲自去做。当你是委托人，那你就是主人你就会关心并且会把工作做得很好。当你是代理人，你是在为别人的利益做事，你可能会把事情做得很糟糕。……你能把某个人的收益和他所创造的价值联系得越紧密，就越能把他们变成委托人。

— 纳瓦尔·拉维坎特, 《纳瓦尔宝典》 第一章·财富

I — 方法论骨架 (Interpretation)

委托-代理问题是微观经济学最基本的问题：利益归属决定行为质量。 当你是委托人（自己的钱/自己的事），你会做好；当你是代理人（为别人利益做事），你会为自己优化。 推论三条： ① 选择位置时，优先选「每个人都能感到自己是委托人」的小组织； ② 设计合作时，把收益与创造的价值绑定得越紧越好（股权、分成、责任到人）； ③ 警惕「代理人叙事」——媒体和现代社会长期洗脑你「需要一个知识渊博的代理人」，而委托人直觉其实人人都有。 用在职业上：为别人打工=代理人拿最低报酬；拥有产权和判断=委托人拿剩余。

A1 — 书中的应用 (Past Application)

案例 1: 为别人工作拿最低报酬

  • 问题: 为什么高薪打工者无法致富
  • 方法论的使用: 雇主是委托人（承担风险/责任/知识产权），雇员是代理人拿「最低限度的报酬」
  • 结论: 工资再高也不是财富
  • 结果: 医生开私人诊所（品牌）后才成为委托人

案例 2: 小公司人人像委托人

  • 问题: 如何让团队用心做事
  • 方法论的使用: 越小的公司，每个人越能感觉到自己是委托人
  • 结论: 「你越觉得自己不是代理人，你将会把工作做得更好」
  • 结果: 组织结构越扁平、收益越绑定，行为质量越高

A2 — 触发场景 (Future Trigger) ★

用户会在什么情境下需要这个 skill?

  1. 职业选择：「去大公司还是小创业公司」「打工还是自己干」
  2. 合作设计：「怎么设计分成/激励让合伙人真的上心」
  3. 困惑现象：「为什么同事都在磨洋工/外包总出问题」
  4. 自我诊断：「我是在为谁的利益工作」

语言信号

  • "大公司 vs 小公司怎么选"
  • "怎么让团队/合伙人更用心"
  • "为什么没人对结果负责"
  • "principal-agent / ownership / who benefits"

与相邻 skill 的区分

  • 与 wealth-structure 的区别: 财富结构是个人资产框架；本 skill 是组织/合作中的激励透镜
  • 与 judgment-training 的区别: 委托代理是判断力心智模型库里的一个模型

E — 可执行步骤 (Execution)

  1. 诊断你当前的角色

    • 完成标准: 回答「我的收益与创造的价值绑定程度（低/中/高）」「我是委托人还是代理人」

  2. 寻找或创建委托人位置

    • 完成标准: 列出 1–2 个具体动作（要求股权/分成、做有产权的事、加入小组织）

  3. 检查合作方的激励结构

    • 完成标准: 对每个合作者问「他赢的判定标准是什么」，确认利益是否一致
    • 判停条件: 若发现纯代理结构（收益完全脱钩）且无法改变，标记为高风险合作

  4. 为未来决策保留记录

    • 完成标准: 记录本次诊断结论，作为下次职业/合作选择的输入

B — 边界 (Boundary) ★

不要在以下情况使用此 skill

  • 用户要具体薪酬谈判话术（本 skill 给结构不给话术）
  • 管理细节（如何监督/考核）不是本 skill 的重点——先改结构再谈管理

作者在书中警告的失败模式

  • 依赖代理人: 「媒体和现代社会花了大量时间来洗脑你：让你认为需要一个代理人」
  • 收益脱钩: 收益与价值脱钩时，代理人必然为自己优化

作者的盲点 / 时代局限

  • 「小公司人人像委托人」在规模化后不成立，作者未给大组织化解决方案
  • 委托-代理只是心智模型之一，不能解释所有组织问题

容易混淆的邻近方法论

  • wealth-structure: 两者的交集是「股权绑定」，但视角不同（组织 vs 个人）

相关 skills (阶段 3 定稿)

  • composes-with: wealth-structure（股权绑定是共同解法）、judgment-training

审计信息

  • 验证通过: V1 ✓ / V2 ✓ / V3 ✓ (v07)
  • 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 Principal Agent AI skill do?

当用户选择职业/组织/合作方式、困惑「为什么大公司磨洋工/小公司拼命」「该不该自己干」时调用。 核心理念: 委托人(主人)会把事做好, 代理人会为自己利益优化; 收益与创造价值绑得越紧, 越像委托人; 别让媒体洗脑你需要代理人。 不适用于: 具体薪酬谈判数字、组织架构设计细节。 Triggers: 激励/代理/主人/打工 vs 创业/利益绑定/principal/agent/incentive

Why use Principal Agent on TypingMind?

Because you install it once and use it with any model. Principal Agent 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 Principal Agent in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kangarooking/cangjie-skill/tree/main/books/naval-almanack-skill/principal-agent. 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 Principal Agent?

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 Principal Agent?

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

Is the Principal Agent 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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