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Work Prioritization

Community
MichaelRochonnn
work-prioritization

应用以大局为重的工作优先级判断方法。适用于: - 感到被工作淹没/迷失方向/不确定工作价值 - 长时间无最终产出 - 面临多个并行工作任务 - 有大量数据需要分析 - 需要避免分析陷入无限链条

Overview

PublisherMichaelRochonnn
Repositorymckinsey-bcg-consulting
Skill namework-prioritization
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 Work Prioritization 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/work-prioritization
  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/work-prioritization .claude/skills/work-prioritization
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Work Prioritization 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 Work Prioritization 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 Work Prioritization 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.

工作优先级判断方法

何时使用

  • 感到被工作淹没
  • 迷失方向
  • 不确定工作价值
  • 长时间无最终产出
  • 面临多个并行工作任务
  • 有大量数据需要分析

执行流程

步骤一:触发暂停机制

  • 当感觉自己已经被工作淹没时,主动后退一步
  • 停止当前的具体执行工作

步骤二:明确核心问题

  • 搞清楚你要设法解决的问题是什么
  • 支持你的基本假设的核心议题("大局")

步骤三:执行四问检验法

对当前正在进行的每一项工作,依次回答以下四个基本问题:

  1. 问题一:你现在所做的工作是如何帮助解决问题的?
  2. 问题二:它是如何推进思考的?
  3. 问题三:眼下做的事情是不是最重要的?
  4. 问题四:如果所做的工作对解决问题没有帮助,为何还要继续?

步骤四:价值判断标准

  • 特定的分析或许在思维层面上是正确的,甚至是有趣的
  • 但假如它不能使你离解决方案更进一步,那就是浪费时间

步骤五:优先级决策

  • 基于上述检验,确定要优先解决的事情
  • 你一天只能做这么多事
  • 必须选择真正最重要的工作

步骤六:避免陷阱

陷阱一:无限链条

  • "分析A后要分析B,似乎紧跟着就要分析C"
  • 不断有新数据出现,要做更多的分析
  • 结果:你将日夜不停

陷阱二:无最终产品

  • 定期回顾一天或一周的工作
  • 确认是否有"最终产品"产出
  • 警惕:只有"毫无价值的待解决的问题"

输出成果

  • 优先级排序后的工作任务清单
  • 剔除对解决问题无帮助的工作
  • 聚焦能推进解决方案的核心任务

关键原则

  • 以大局为重——后退一步,搞清楚要解决的问题
  • 然后看看手头的工作,问自己"这些工作真的最重要吗?"
  • 避免陷入分析A→分析B→分析C的无限链条

Frequently asked questions

What does the Work Prioritization AI skill do?

应用以大局为重的工作优先级判断方法。适用于: - 感到被工作淹没/迷失方向/不确定工作价值 - 长时间无最终产出 - 面临多个并行工作任务 - 有大量数据需要分析 - 需要避免分析陷入无限链条

Why use Work Prioritization on TypingMind?

Because you install it once and use it with any model. Work Prioritization 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 Work Prioritization in TypingMind?

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

Which AI models can use Work Prioritization?

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 Work Prioritization?

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

Is the Work Prioritization 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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