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Mckinsey Problem Solving Methodology

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MichaelRochonnn
mckinsey-problem-solving-methodology

应用麦肯锡问题解决方法论解决商业问题。适用于: - 需要系统化解决复杂商业问题 - 项目启动阶段需要构建解决方案框架 - 面对模糊问题需要重新定义和验证 - 需要避免确认偏误保持客观分析 - 无法形成初始假设时需要事实驱动的方法

Overview

PublisherMichaelRochonnn
Repositorymckinsey-bcg-consulting
Skill namemckinsey-problem-solving-methodology
Stars
77
Forks
16
Bundled files
1
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 MichaelRochonnn on GitHub. Read the source before you install it.

Installation

Install the Mckinsey Problem Solving Methodology 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/MichaelRochonnn/mckinsey-bcg-consulting/tree/main/mckinsey-problem-solving-methodology
  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/MichaelRochonnn/mckinsey-bcg-consulting.git /tmp/mckinsey-bcg-consulting
mkdir -p .claude/skills
cp -r /tmp/mckinsey-bcg-consulting/mckinsey-problem-solving-methodology .claude/skills/mckinsey-problem-solving-methodology
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mckinsey Problem Solving Methodology 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 Mckinsey Problem Solving Methodology 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 Mckinsey Problem Solving Methodology 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.

麦肯锡问题解决方法论

核心理念

麦肯锡问题解决基于三大核心理念:

  1. 以事实为基础 - 事实是砖,铺成通往解决方案的康庄大道
  2. 系统化 - 强调结构化、有条理的分析方法
  3. 大胆假设,小心求证 - 提出初始假设后用事实验证

何时使用

  • 项目首次会面讨论客户问题
  • 接到商业问题且问题界定模糊
  • 客户已做自我诊断但未经验证
  • 无法生成初始假设或客户不知道问题根源
  • 项目范围过大或模糊
  • 需要避免确认偏误保持思想开放

执行流程

阶段一:问题验证与重新定义

  1. 暂停立即行动

    • 即使问题看似清楚,不要急于从四面八方展开行动
    • 沿着正确的方向前行通常不会花很多时间,方向不清走冤枉路是白费工夫
  2. 识别客户自我诊断信号

    • 警惕模糊表述如"提高利润率"
    • 客户直接给出解决方案而非问题描述
    • 未经数据验证的假设性结论
  3. 深入探究获取事实

    • 提出问题(probe with questions)
    • 四处试探(test around)
    • 采集数据(data collection)
    • 进行分析(analysis)
  4. 问题重新定义决策

    • 若发现真正问题≠最初定义的问题:
      • 回到客户或上司那里
      • 沟通话术:"您要求研究问题X,但真正的影响来自问题Y。如果问题X是您真正想要解决的,我现在就可以解决它,但为了彼此的利益,我们应该关注Y。"
      • 以事实为基础呈现客观见解
    • 若确认最初问题正确:继续按原计划执行

阶段二:构建解决方案框架

  1. 收集充足事实

    • 项目第一天,团队所有成员要对成堆的外部资料和公司内部文献详查搜遍
    • 目的是在第一次团队会议中能够阐述问题的每个部分
  2. 形成初始假设

    • 基于现有信息提出初始假设
    • 马不停蹄地收集必要的事实来证实或证伪假设
  3. 无初始假设时的替代方法

    • 承认初始假设并非成功解决问题的先决条件
    • 主动与关键利益相关者面谈
    • 即使面对抵触情绪,仍坚持获取所需数据
    • 对收集到的事实进行系统性分析
    • 寻找数据中的异常模式或关键洞察
    • 用创造性思维将事实结合形成解决方案

阶段三:避免确认偏误

  1. 保持开放心态

    • 抵制将初始假设直接当作最终答案的诱惑
    • 抵制将问题解决流程降格为仅用于证明初始假设的工具
    • 保持开放与灵活,绝不让强有力的初始假设成为思想僵化的借口
  2. 实事求是地看待数据

    • 不论初始假设多么精彩,都必须时刻准备接受证明错误的事实
    • 若事实证明初始假设错误,必须根据事实做出调整
    • 绝对禁止:"将事实捣碎硬塞进你的结构框架"
  3. 定期反思验证

    • 每周或每两周进行一次假设验证反思
    • 核心问题:"在过去的一周里你有哪些收获?"
    • 核心问题:"新信息是否适用于你的初始假设?"

关键原则

  • 事实弥补直觉缺陷:麦肯锡顾问大多是通才,经验有限,必须依赖事实
  • 事实提高可信度:27岁的MBA向《财富》50强CEO提建议,必须有事实支撑
  • 不要畏惧事实:隐瞒事实预示着失败,真相最终会大白
  • 方向正确效率优先:验证方向通常不会花很多时间,方向不清是最大浪费

输出成果

  • 经过验证的问题定义或问题重新定义建议书
  • 经事实验证的假设
  • 系统化的分析结构
  • 可置信的建议方案

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 Mckinsey Problem Solving Methodology AI skill do?

应用麦肯锡问题解决方法论解决商业问题。适用于: - 需要系统化解决复杂商业问题 - 项目启动阶段需要构建解决方案框架 - 面对模糊问题需要重新定义和验证 - 需要避免确认偏误保持客观分析 - 无法形成初始假设时需要事实驱动的方法

Why use Mckinsey Problem Solving Methodology on TypingMind?

Because you install it once and use it with any model. Mckinsey Problem Solving Methodology 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 Mckinsey Problem Solving Methodology in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MichaelRochonnn/mckinsey-bcg-consulting/tree/main/mckinsey-problem-solving-methodology. 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 Mckinsey Problem Solving Methodology?

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 Mckinsey Problem Solving Methodology?

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

Is the Mckinsey Problem Solving Methodology AI skill free?

It is published on GitHub by MichaelRochonnn. Check the repository for licensing terms. 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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