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Mckinsey Problem Solving Methodology 2

Community
MichaelRochonnn
mckinsey-problem-solving-methodology-2

应用麦肯锡核心问题解决方法论,包括MECE原则、假设驱动分析和严谨的事实验证。适用于需要系统化解决复杂商业问题、进行结构化分析和制定解决方案的场景。

Overview

PublisherMichaelRochonnn
Repositorymckinsey-bcg-consulting
Skill namemckinsey-problem-solving-methodology-2
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 2 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-2
  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-2 .claude/skills/mckinsey-problem-solving-methodology-2
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mckinsey Problem Solving Methodology 2 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 2 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 2 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

使用时机

当面临以下情况时应用此技能:

  • 需要解决复杂的商业问题
  • 需要将大问题分解为可管理的子问题
  • 需要形成清晰、完整的分析框架
  • 需要制定基于事实的解决方案

核心原则

1. MECE原则(相互独立、完全穷尽)

定义:MECE发音为"me-see",全称为Mutually Exclusive, Collectively Exhaustive

应用要求:

  • 相互独立:每个问题/条目必须独立、不同,彼此之间无重叠
  • 完全穷尽:清单必须囊括一切与问题相关的条目,解决所有子问题即解决母问题

应用场景:

  • 问题分解的第一层结构
  • 每份文件、演示文稿、电子邮件和语音留言
  • 将"大的复杂的问题"拆分成"小的容易解决的子问题"

2. 假设驱动方法

核心循环:

大胆假设 → 小心求证 → 验证结果 → 调整假设

执行要点:

  • 必须重复强调:"大胆假设,小心求证"
  • 假设作为讨论起点,而非最终结论
  • 通过事实验证或证伪假设

3. 事实与严格分析

执行标准:

  • 遵循"严格的信息标准和分析标准"
  • 对每个议题进行"求证与再求证"
  • 保持"高水准的交流"

4. 系统化思维

核心口诀:"系统、系统、系统"

执行步骤:

  1. 运用MECE原则分解问题
  2. 建立假设
  3. 收集事实验证假设
  4. 形成解决方案

执行流程

步骤1:问题定义

  • 明确需要解决的核心问题
  • 确认问题边界和范围

步骤2:MECE分解

  • 将问题分解为相互独立、完全穷尽的子问题
  • 确保第一层结构清晰完整

步骤3:形成假设

  • 基于初步理解提出假设
  • 假设应具体、可验证

步骤4:事实验证

  • 收集相关数据和信息
  • 严格验证每个假设
  • 记录数据来源

步骤5:综合结论

  • 基于验证结果形成结论
  • 确保逻辑链条完整

关键变量

  • 事实:用于分析的基础信息,需经过严格验证
  • 假设:待验证的初步判断,需大胆提出
  • MECE:相互独立、完全穷尽的分析原则

输出成果

形成既相互独立(无重叠)又完全穷尽(无遗漏)的问题分解结构,确保逻辑清晰和思维严谨,最终实现基于事实的解决方案。

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 2 AI skill do?

应用麦肯锡核心问题解决方法论,包括MECE原则、假设驱动分析和严谨的事实验证。适用于需要系统化解决复杂商业问题、进行结构化分析和制定解决方案的场景。

Why use Mckinsey Problem Solving Methodology 2 on TypingMind?

Because you install it once and use it with any model. Mckinsey Problem Solving Methodology 2 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 2 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-2. 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 2?

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 2?

As many as you like. As long as a model supports skills, you can use Mckinsey Problem Solving Methodology 2 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 2 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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