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Solution Feasibility Assessment

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MichaelRochonnn
solution-feasibility-assessment

评估客户解决方案的可行性。适用于: - 准备向客户提出解决方案或建议 - 需要评估方案是否与客户能力匹配 - 需要识别实施障碍与局限 - 需要在学术理想与商业现实之间平衡

Overview

PublisherMichaelRochonnn
Repositorymckinsey-bcg-consulting
Skill namesolution-feasibility-assessment
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 Solution Feasibility Assessment 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/solution-feasibility-assessment
  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/solution-feasibility-assessment .claude/skills/solution-feasibility-assessment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Solution Feasibility Assessment 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 Solution Feasibility Assessment 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 Solution Feasibility Assessment 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.

客户解决方案可行性评估

何时使用

  • 准备向客户提出解决方案或建议
  • 需要评估方案是否与客户能力匹配
  • 需要识别实施障碍与局限
  • 需要在学术理想与商业现实之间平衡

执行流程

步骤一:深度剖析客户能力

  • 全面了解客户组织的优势、劣势以及核心能力
  • 明确界定管理层"做得到的事"和"做不到的事"

步骤二:识别实施障碍与局限

政治风险

  • 评估方案是否涉及推翻已进行中的项目
  • 示例:若客户已投入大量资源(如完成一半的卫星连接项目),中途更换方案可能带来巨大的政治风险

资源与能力边界

  • 认识到企业是由现实的人组成的
  • 只能运用现有资源做有限的事
  • 客户可能因资源不足或无能为力而无法完成某些任务

步骤三:评估方案适配度(核心原则)

  • 匹配性检查:牢记解决方案必须与客户的优势、劣势及能力相符
  • 实用性优先:即使方案有大量数据支撑、前景巨大(如节省1.7亿美元),如果客户无法实施,该方案也是毫无用途的

步骤四:优先级排序与影响力最大化

  • 聚焦大事:考虑到客户能力有限,应优先处理影响力最大的事项
  • 决策逻辑:如果客户只能做3件事,那就做3件最大的事(如寻找节省5亿或10亿美元的机会),而不是为了节省1.7亿美元而耗费精力

步骤五:平衡学术理想与商业现实

  • 避免单纯追求"学术上的最佳解决方案"(这是麦肯锡等咨询公司新员工的常见倾向)
  • 接受"当学术理想与商业现实相撞时,后者是赢家"的事实

步骤六:最终责任确认

  • 咨询顾问有责任了解客户的局限
  • 若客户是雇主,责任加倍
  • 确保提出的任何建议都在客户力所能及的范围内

输出成果

  • 经过筛选和调整的、符合客户实际能力的最终建议列表

关键原则

  • 解决方案必须与客户的优势、劣势及能力相符
  • 实用性优先于理论完美
  • 聚焦影响力最大的事项
  • 当学术理想与商业现实相撞时,后者是赢家

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 Solution Feasibility Assessment AI skill do?

评估客户解决方案的可行性。适用于: - 准备向客户提出解决方案或建议 - 需要评估方案是否与客户能力匹配 - 需要识别实施障碍与局限 - 需要在学术理想与商业现实之间平衡

Why use Solution Feasibility Assessment on TypingMind?

Because you install it once and use it with any model. Solution Feasibility Assessment 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 Solution Feasibility Assessment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MichaelRochonnn/mckinsey-bcg-consulting/tree/main/solution-feasibility-assessment. 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 Solution Feasibility Assessment?

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 Solution Feasibility Assessment?

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

Is the Solution Feasibility Assessment 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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