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Mckinsey Issue Tree Structuring

Community
MichaelRochonnn
mckinsey-issue-tree-structuring

构建符合麦肯锡标准的问题清单与议题树结构,设置2-5个一级标题(最佳为3个),并通过"其他问题"类别妥善处理异常见解。适用于构建咨询汇报大纲、MECE合规分析框架,或组织需要遵循麦肯锡结构化标准的战略演示内容。

Overview

PublisherMichaelRochonnn
Repositorymckinsey-bcg-consulting
Skill namemckinsey-issue-tree-structuring
Stars
77
Forks
16
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by MichaelRochonnn on GitHub. Read the source before you install it.

Installation

Install the Mckinsey Issue Tree Structuring 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-issue-tree-structuring
  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-issue-tree-structuring .claude/skills/mckinsey-issue-tree-structuring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mckinsey Issue Tree Structuring 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 Issue Tree Structuring 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 Issue Tree Structuring 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.

麦肯锡议题树结构规范

适用场景

  • 构建麦肯锡风格的问题清单或议题树(Issue Tree)
  • 准备大型战略汇报的演示结构
  • 需要处理无法归入主要类别的闪光思想/异常见解
  • 已完成MECE逻辑验证但需要最终结构化输出

执行步骤

1. 设定一级标题数量

严格控制一级标题(First-Level Headings)数量:

  • 下限:不少于2个(确保有足够维度展开分析)
  • 上限:不多于5个(避免结构过于复杂)
  • 最佳实践:设置为3个(对应麦肯锡文化中的"这个问题我们有三点看法")

2. 识别并处理闪光思想

当团队提出有价值但难以归入主要类别的见解时:

  • 禁止忽略:不能因不符合主要分类而舍弃这些观点(无益于客户)
  • 禁止强行单列:不能随意将其列为独立的一级标题(会破坏2-5规则)
  • 收容策略:设立"其他问题"(Other Issues)作为收容类别

3. 放置"其他问题"类别

遵循位置规则确保专业性和得体性:

  • 绝对禁止:将"其他问题"放在清单头条(不得体且突兀)
  • 推荐位置:置于诸多子问题之中,保持低调不突兀
  • 演示禁忌:避免让"其他问题"出现在大型汇报的第一张幻灯片上
  • 优先尝试:尽量使闪光思想与现有的一级标题相符合,只有在确实无法归纳进已有标题时才使用"其他问题"类别

4. 数量控制与MECE验证

  • 当异常见解数量为2-3个时,适合放入"其他问题"
  • 若异常见解数量过多,需重新审视主要分类是否足够全面,而非简单堆砌到"其他问题"中
  • 确保最终结构符合MECE原则(相互独立,完全穷尽)

关键约束

  • 遵循麦肯锡内部汇报标准
  • 适用于大型汇报场景
  • 前置条件:已完成MECE逻辑验证的问题清单

文化提示

麦肯锡文化中常以"这个问题我们有三点看法"自嘲,体现了对"3个一级标题"这一黄金标准的偏好。保持这一结构有助于符合咨询行业的沟通惯例。

Frequently asked questions

What does the Mckinsey Issue Tree Structuring AI skill do?

构建符合麦肯锡标准的问题清单与议题树结构,设置2-5个一级标题(最佳为3个),并通过"其他问题"类别妥善处理异常见解。适用于构建咨询汇报大纲、MECE合规分析框架,或组织需要遵循麦肯锡结构化标准的战略演示内容。

Why use Mckinsey Issue Tree Structuring on TypingMind?

Because you install it once and use it with any model. Mckinsey Issue Tree Structuring 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 Issue Tree Structuring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MichaelRochonnn/mckinsey-bcg-consulting/tree/main/mckinsey-issue-tree-structuring. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Mckinsey Issue Tree Structuring?

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 Issue Tree Structuring?

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

Is the Mckinsey Issue Tree Structuring 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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