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Build Measure Learn

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CymChad
build-measure-learn

构建-测量-学习循环，精益创业的核心引擎。当用户想建立快速迭代的反馈循环、加速产品验证时使用此技能。

Overview

PublisherCymChad
Repositorybook-skill-generator
Skill namebuild-measure-learn
Stars
72
Forks
5
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepository
  • 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 CymChad on GitHub. Read the source before you install it.

Installation

Install the Build Measure Learn 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/CymChad/book-skill-generator/tree/main/examples/精益创业/build-measure-learn
  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.

Use it in TypingMind

Enable Build Measure Learn 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 Build Measure Learn 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 Build Measure Learn 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.

构建-测量-学习循环

精益创业的核心引擎。通过持续迭代的闭环，让产品持续逼近用户需求。

核心理念

快速迭代，持续学习。 精益创业的本质是"持续迭代的闭环"——构建(Build) MVP → 测量(Measure)数据 → 学习(Learn)验证假设 → 再构建优化后的产品，循环往复。

适用场景

  • 想建立快速迭代的反馈循环
  • 需要加速产品验证速度
  • 想持续优化产品
  • 希望降低开发风险
  • 需要快速响应市场变化

执行步骤

步骤 1: 构建 (Build)

从想法到产品:

  1. 明确想法 (Idea)

    • 你的核心假设是什么？
    • 你想验证什么？
    • 预期结果是什么？

  2. 开发 MVP

    • 只开发验证假设必需的功能
    • 使用最简单的技术实现
    • 确保可以快速发布

  3. 快速发布

    • 不要追求完美
    • 尽快推向目标用户
    • 收集真实数据

构建原则:

  • 小批量开发
  • 快速发布
  • 可测量

步骤 2: 测量 (Measure)

收集和分析数据:

  1. 定义关键指标

    • 哪些指标能验证你的假设？
    • 如何衡量成功？
    • 数据收集方式？

  2. 收集数据

    • 用户行为数据
    • 转化率
    • 留存率
    • 用户反馈

  3. 分析数据

    • 对比预期和实际
    • 识别趋势和模式
    • 发现问题和机会

测量原则:

  • 关注可执行指标
  • 避免虚荣指标
  • 使用同期群分析
  • 真实用户数据

步骤 3: 学习 (Learn)

从数据中获得认知:

  1. 验证假设

    • 假设是否成立？
    • 数据是否支持你的想法？
    • 有什么意外发现？

  2. 获得认知

    • 用户真正需要什么？
    • 产品哪里做得好？哪里不好？
    • 下一步应该做什么？

  3. 做出决策

    • 坚持 (Persevere): 假设成立，继续优化
    • 转型 (Pivot): 假设不成立，调整方向

学习原则:

  • 基于数据，而非直觉
  • 关注用户行为，而非口头反馈
  • 每次循环都要获得可验证的认知

步骤 4: 缩短循环周期

加速反馈循环:

  • 小批量开发: 每次只开发一小部分功能
  • 快速发布: 缩短从开发到发布的时间
  • 自动化测试: 建立自动化测试和部署流程
  • 实时数据: 实时收集和分析数据

目标:

  • 缩短每次循环的时间
  • 加快学习和迭代速度
  • 降低试错成本

输出格式

Build-Measure-Learn 循环记录:

markdown
## 循环 #N

### Build (构建)
**想法 (Idea):**
- 核心假设: 
- 预期结果: 

**MVP:**
- 开发内容: 
- 发布时间: 
- 目标用户: 

### Measure (测量)
**关键指标:**
- 指标 1: [名称] = [数值]
- 指标 2: [名称] = [数值]
- 指标 3: [名称] = [数值]

**数据收集:**
- 用户数: 
- 使用率: 
- 转化率: 
- 留存率: 

### Learn (学习)
**验证结果:**
- 假设是否成立: [是/否]
- 证据: 

**关键认知:**
1. [认知 1]
2. [认知 2]
3. [认知 3]

**下一步决策:**
- [ ] 坚持 (Persevere): 继续优化
- [ ] 转型 (Pivot): 调整方向
- 转型方向（如适用）: 

### 循环时间
- 构建时间: 
- 测量时间: 
- 学习时间: 
- 总循环时间:

注意事项

  • 每次循环都要有明确的学习目标
  • 不要跳过测量环节，凭直觉做决策
  • 数据要来自真实用户，而非假设
  • 循环周期越短越好
  • 失败也是学习，但要快速失败

来源

本方法论来自《精益创业》(The Lean Startup) 全书核心，作者埃里克·莱斯 (Eric Ries)。

Frequently asked questions

What does the Build Measure Learn AI skill do?

构建-测量-学习循环，精益创业的核心引擎。当用户想建立快速迭代的反馈循环、加速产品验证时使用此技能。

Why use Build Measure Learn on TypingMind?

Because you install it once and use it with any model. Build Measure Learn 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 Build Measure Learn in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/CymChad/book-skill-generator/tree/main/examples/精益创业/build-measure-learn. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Build Measure Learn?

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 Build Measure Learn?

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

Is the Build Measure Learn AI skill free?

Yes. It is published on GitHub by CymChad under the MIT license. 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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