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Sangjun Keduoqi

Community
ace3000chao
sangjun-keduoqi

当你需要提升团队士气、打破沉闷局面时, 当竞争对手士气正旺、咄咄逼人时, 当你想知道如何"攻心"而不只是"攻业绩"时—— 调用此skill使用"三军可夺气,将军可夺心"的攻心法则。

Overview

Publisherace3000chao
Repositorybook2startup
Skill namesangjun-keduoqi
Stars
93
Forks
14
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 ace3000chao on GitHub. Read the source before you install it.

Installation

Install the Sangjun Keduoqi 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/ace3000chao/book2startup/tree/master/孙子兵法skills/三军夺气
  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.

Use it in TypingMind

Enable Sangjun Keduoqi 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 Sangjun Keduoqi 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 Sangjun Keduoqi 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.

Skill: 三军夺气框架

R — 原文引用

"故三军可夺气,将军可夺心。是故朝气锐,昼气惰,暮气归。善用兵者,避其锐气,击其惰归,此治气者也。" ——孙子·军争第七

I — 方法论骨架

治气四阶段:

阶段时间士气特征策略
朝初期锐利避之
昼中期懈怠击之
暮后期衰竭待之

夺心 vs 夺气:

  • 夺气:打击敌军整体的士气
  • 夺心:动摇将领的判断和决心

夺心四法:

方法适用场景手段
夺气敌军士气正旺避锐击惰
夺心将领心理造势威慑
激励己方士气愿景+利益

A1 — 书中案例

曹刿论战:

  • 齐军三次击鼓进攻
  • 曹刿:"一鼓作气,再而衰,三而竭"
  • 等齐军"惰归"时再出击,大胜

商业案例: 华为的"冬天":

  • 任正非多次说"华为的冬天要来了"
  • 让团队保持危机感,不松懈
  • 等竞争对手松懈时出击

A2 — 未来触发情境

  • 🎯 竞品刚融了大钱、士气正旺时
  • 🎯 团队连续成功、开始骄傲自满时
  • 🎯 重要谈判前需要为己方造势时
  • 🎯 对手挑衅、团队情绪激动可能犯错时
  • 🎯 你需要在竞争中"以弱胜强"时

E — 执行步骤

步骤1:诊断"气"的状态

  • 对手处于哪个阶段?(朝/昼/暮)
  • 我方处于哪个阶段?
  • 谁有"锐气",谁有"惰归"?

步骤2:制定"治气"策略

己方状态策略
有锐气速战,不要延误
有惰归调整激励,重新激发
无气休整,等待时机
对手状态策略
有锐气避其锋芒,等待惰归
有惰归主动出击
刚换将士气动摇,立即进攻

步骤3:执行"夺心"

  • 对对手:将你的"势"展示给他看,让他知难而退
  • 对己方:用"愿景"和"利益"重新激发

B — 边界(什么时候不适用)

❌ 边界1: 实力悬殊时

  • 如果对方实力碾压,"夺气"无效
  • 此时应该战略转移而非战术周旋

❌ 边界2: 诚信重要的关系

  • 过度"夺心"手段会损害长期信任
  • 合作伙伴关系需要真诚

❌ 边界3: 己方内部

  • 对员工"夺气"会破坏心理安全感
  • 长期而言,文化建设比"夺气"更重要

相关Skills:

  • 治气治心治力治变框架
  • 杂于利害框架
  • 五危框架

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 Sangjun Keduoqi AI skill do?

当你需要提升团队士气、打破沉闷局面时, 当竞争对手士气正旺、咄咄逼人时, 当你想知道如何"攻心"而不只是"攻业绩"时—— 调用此skill使用"三军可夺气,将军可夺心"的攻心法则。

Why use Sangjun Keduoqi on TypingMind?

Because you install it once and use it with any model. Sangjun Keduoqi 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 Sangjun Keduoqi in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ace3000chao/book2startup/tree/master/孙子兵法skills/三军夺气. 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 Sangjun Keduoqi?

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 Sangjun Keduoqi?

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

Is the Sangjun Keduoqi AI skill free?

Yes. It is published on GitHub by ace3000chao 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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