Career Expectation Management logo

Career Expectation Management

Community
MichaelRochonnn
career-expectation-management

在团队协作环境中(特别是高标准企业如麦肯锡),通过管理绩效期望避免过度表现陷阱,建立可持续的职业发展路径。适用于存在绩效考核、上级评价机制,且需要长期维持信任关系的职业场景。

Overview

PublisherMichaelRochonnn
Repositorymckinsey-bcg-consulting
Skill namecareer-expectation-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 Career Expectation 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/career-expectation-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/career-expectation-management .claude/skills/career-expectation-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Career Expectation 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 Career Expectation 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 Career Expectation 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.

职业期望管理与专注原则

何时使用

以下情境立即应用此原则:

  • 开始一项新工作
  • 接手新项目
  • 面临超出职责范围的诱惑
  • 处于团队协作的高标准环境中
  • 存在绩效考核或上级评价机制

不适用:

  • 创业初期需要身兼数职的极端场景
  • 纯粹以结果为导向且无团队协作的单兵作战任务

核心原则

"一次只做一件事"——专注于做好本职工作,不要试图承担整个团队的工作量。

关键策略:避免过度表现

禁止行为

不要试图通过超人的努力去"把球击出场地"(即不要追求一次性的超常表现或"本垒打")。

核心理由

你不可能事必躬亲,试图做所有事是不现实的。

期望陷阱机制

风险产生

阶段结果
某次工作运用超人努力表现超出别人对你的期望("打了本垒")
后续每次开始工作老板或股东期望你有同样好的表现

一致性要求

在麦肯锡等高标准环境中,只要和上一次做得一样好即可,不需要每次都超越。

失败后果(信任机制)

假如没能满足因过度表现而建立的不切实际的期望,将很难重新获得信任。

具体负面影响:

  • 项目经理不再让你参与他们的团队
  • 不会再被安排到自己感兴趣的项目中
  • 不会再被安排到能升职的岗位
  • 在公司的职业生涯将受挫
  • 最终需要准备简历寻找新工作

类比参考

棒球游戏中的全垒王(如鲁斯、迪马吉奥):

  • 拥有最多的全垒打
  • 但也拥有最多的出局记录

启示:即使是高手也不可能每次都成功。

执行指南

建立期望基准线

  • 明确他人对你工作表现的平均预期水平
  • 首次表现即设定合理基准

识别超常表现风险

  • 警惕单次超出期望基准线的卓越表现
  • 评估其对未来期望的连锁影响

维持可持续输出

  • 保持稳定的高质量输出
  • 避免波动过大的绩效表现
  • 在关键时刻保留提升空间

关键检查清单

  • 是否专注于本职工作而非团队整体?
  • 是否抵制了"打本垒"的诱惑?
  • 当前表现是否设定了可维持的期望基准?
  • 是否评估了超常表现对未来期望的影响?
  • 是否为关键发展节点保留了提升空间?

Frequently asked questions

What does the Career Expectation Management AI skill do?

在团队协作环境中(特别是高标准企业如麦肯锡),通过管理绩效期望避免过度表现陷阱,建立可持续的职业发展路径。适用于存在绩效考核、上级评价机制,且需要长期维持信任关系的职业场景。

Why use Career Expectation Management on TypingMind?

Because you install it once and use it with any model. Career Expectation 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 Career Expectation Management in TypingMind?

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

Which AI models can use Career Expectation 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 Career Expectation Management?

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

Is the Career Expectation 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇