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Active Mentor Finding Management

Community
MichaelRochonnn
active-mentor-finding-management

在高压企业环境中主动寻找与管理导师的策略,适用于任何高压企业员工、寻求职业提升的职场人。当需要在公司丛林中导航、寻求职业发展或适应高压工作环境(如每周工作100小时)时使用。

Overview

PublisherMichaelRochonnn
Repositorymckinsey-bcg-consulting
Skill nameactive-mentor-finding-management
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 Active Mentor Finding Management 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/active-mentor-finding-management
  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/active-mentor-finding-management .claude/skills/active-mentor-finding-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Active Mentor Finding Management 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 Active Mentor Finding Management 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 Active Mentor Finding Management 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.

主动寻找与管理导师策略

使用场景

  • 需要在公司丛林中导航
  • 寻求职业发展
  • 适应高压工作环境
  • 识别出值得钦佩的资深人士

执行流程

1. 导师选择标准

筛选潜在导师时需满足以下条件:

  • 资历要求:必须比你资历更深(资深人士)
  • 能力要求:具备你所钦佩的能力和见解
  • 品格要求:值得信赖和尊重(可充当领路人穿越公司丛林)

2. 建立联系策略

启动导师关系时:

  • 利用人性特点:"很多人都喜欢给别人提建议",主动征求意见时对方通常会坦言相待
  • 关系加成:如果与目标导师关系很好,对方会更加坦诚和投入
  • 明确目的:寻找能帮你"拨云见日"的向导,走出"弱肉强食的丛林"

3. 深度参与

工作学习阶段:

  • 尽可能与导师一起工作
  • 学习一切你能学习的东西(最大化知识转移)
  • 建立长期指导关系,而非一次性咨询

4. 频率管理

关系维护阶段:

  • 关键约束:"拜访也不要太过频繁"
  • 风险规避:避免"让人生厌"(过度索取导致关系破裂)
  • 平衡原则:保持足够接触以维持关系,但尊重对方时间

适用范围

此法则适用于任何公司("在任何公司都通用的法则"),不仅限于麦肯锡,适用于所有高压企业环境。

关键变量

  • mentor_candidate: 潜在导师对象(需满足资历、能力、品格标准)
  • relationship_strength: 与导师的关系强度(影响建议质量)
  • visit_frequency: 拜访/互动频率(需控制在不引起反感的阈值下)
  • learning_scope: 学习范围(应最大化:一切能学习的东西)

Frequently asked questions

What does the Active Mentor Finding Management AI skill do?

在高压企业环境中主动寻找与管理导师的策略,适用于任何高压企业员工、寻求职业提升的职场人。当需要在公司丛林中导航、寻求职业发展或适应高压工作环境(如每周工作100小时)时使用。

Why use Active Mentor Finding Management on TypingMind?

Because you install it once and use it with any model. Active Mentor Finding Management 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 Active Mentor Finding Management in TypingMind?

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

Which AI models can use Active Mentor Finding Management?

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 Active Mentor Finding Management?

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

Is the Active Mentor Finding Management 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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