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Quanzer Biwu

Community
ace3000chao
quanzer-biwu

当你需要评估敌我实力对比、决定打还是不打时, 当你在竞争中面临"以少敌多"的困境时, 当你需要向团队解释为什么有时候"不战"才是智慧时—— 调用此skill使用"十则围之"的兵力对比决策框架。

Overview

Publisherace3000chao
Repositorybook2startup
Skill namequanzer-biwu
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 Quanzer Biwu 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/十则围之五则攻之
  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 Quanzer Biwu 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 Quanzer Biwu 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 Quanzer Biwu 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: 十则围之框架

R — 原文引用

"故用兵之法,十则围之,五则攻之,倍则分之。敌则能战之,少则能逃之,不若则能避之。故小敌之坚,大敌之擒也。" ——孙子·谋攻第三

I — 方法论骨架

兵力对比决策矩阵:

敌我对比战略选择战术选择
十倍于敌围之瓮中捉鳖,全歼
五倍于敌攻之集中突破
两倍于敌分之分割包围
旗鼓相当战之谨慎应战
少于敌人逃之保存实力
远逊于敌避之战略撤退

核心洞察:

  • "小敌之坚,大敌之擒" — 弱势方硬抗必然失败
  • 打不过就跑不是懦弱,是理性
  • 创业者应该"打必胜之仗",不打"意志之争"

A1 — 书中案例

孙武的忠告:

  • 如果兵力远逊于敌人,还要"坚守"→ 被俘
  • 项羽"破釜沉舟"之所以成功,是因为有"巨鹿之战"的整体优势
  • 韩信背水一战成功,是用了诡道(心理战),不是纯粹的数量劣势

A2 — 未来触发情境

  • 🎯 巨头进入你的市场,你要判断打还是跑时
  • 🎯 团队扩张时评估"以少敌多"的可能性
  • 🎯 投资人在问"如果BAT做同样的事你怎么办"时
  • 🎯 个人职业选择中判断自己的竞争力时
  • 🎯 项目立项时评估胜率是否值得投入时

E — 执行步骤

步骤1:客观评估"倍数"

不要主观感觉,用数据说话:

敌我兵力比 = 敌方资源 ÷ 我方资源

资源维度:
- 资金
- 人才
- 用户基础
- 品牌认知
- 政策支持

步骤2:对应策略选择

倍数策略行动
>10x围之全面竞争,断其后路
5-10x攻之集中一点,突破
2-5x分之侧翼包抄
0.5-2x战之找差异化
<0.5x逃之/避之战略转移

步骤3:如果打不过……

  • 寻找"五间俱起"获取情报
  • 找到"避实击虚"的细分市场
  • 考虑"合并"或"被收购"
  • 最差情况:保存实力,重新开始

B — 边界(什么时候不适用)

❌ 边界1: 行业赢家通吃时

  • 互联网行业往往是"赢家通吃"
  • 此时"不若则避"可能意味着永远失去机会

❌ 边界2: 创新颠覆者

  • 如果你代表新技术,即使10x劣势也可能赢
  • 此时要看"势"而不只是"量"

❌ 边界3: 时间维度

  • "五则攻之"是静态分析
  • 如果你能"加速增长",倍数会变化

相关Skills:

  • 避实击虚框架
  • 知彼知己
  • 先为不可胜

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 Quanzer Biwu AI skill do?

当你需要评估敌我实力对比、决定打还是不打时, 当你在竞争中面临"以少敌多"的困境时, 当你需要向团队解释为什么有时候"不战"才是智慧时—— 调用此skill使用"十则围之"的兵力对比决策框架。

Why use Quanzer Biwu on TypingMind?

Because you install it once and use it with any model. Quanzer Biwu 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 Quanzer Biwu in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ace3000chao/book2startup/tree/master/孙子兵法skills/十则围之五则攻之. 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 Quanzer Biwu?

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 Quanzer Biwu?

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

Is the Quanzer Biwu 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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