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Small Batch

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ace3000chao
small-batch

用小批量方式减少浪费、加速反馈。当产品开发周期过长、团队陷入"憋大招"困境、错误发现太晚导致返工量大时,使用此Skill。

Overview

Publisherace3000chao
Repositorybook2startup
Skill namesmall-batch
Stars
93
Forks
14
Bundled files
1
LicenseMIT
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 ace3000chao on GitHub. Read the source before you install it.

Installation

Install the Small Batch 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/ace3000chao/book2startup/tree/master/精益创业skills/skills/007-small-batch
  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 Small Batch 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 Small Batch 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 Small Batch 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.

Skill: 小批量原则

Metadata

  • ID: leanstartup-007
  • 类型: framework
  • 来源: 《精益创业》第九章
  • 验证状态: ✅ 三重验证通过(V1: 9章完整+多案例, V2: 开发效率核心, V3: 反直觉)

R — Reading(原文引用)

"用大批量的话,我们一直要到接近流程终点才会发现问题。而用小批量的话,我们几乎能马上发现问题。"

"这个结果在很多研究中已经得到证实:一次封装一个信封看似较慢,却能更快地完成工作。"

I — Interpretation(方法论骨架)

小批量的反直觉洞察:慢即是快,少即是多。

大批量的诱惑:看起来效率高——可以专注做一件事,不用反复切换。

大批量的代价:

  • 反馈周期拉长到几周甚至几个月
  • 错误在后期才暴露,修正成本成倍增加
  • 团队在"错误方向"上投入了大量资源

小批量的本质:

  • 把大工作拆成小块,每完成一小块就获取一次反馈
  • 不是"把事情做完才交付",而是"持续交付,持续验证"

小批量的极致 = 持续部署/持续交付。

A1 — Past Application(书中案例)

信封实验:父亲一次装一个信封赢了比赛,因为孩子们大批量处理(折100个→封信口100个→贴邮票100个),但中途发现问题只能最后返工;父亲发现问题立刻调整,总时间反而更短。

丰田的精益生产:丰田面对资源匮乏,无法像美国工厂那样大批量生产。被逼走上小批量道路,最终发现这种方法比大批量更高效——因为能快速发现问题,不用在错误的基础上继续投入。

财捷TurboTax的快速实验:每周进行70项不同的测试,每次测试周期只有几天。每个测试完成后立即分析结果,周四部署新测试,周一看数据,周二决策。传统软件公司的产品开发周期是季度或年度,财捷是每天。

A2 — Future Trigger(何时调用)

当你听到以下问题时,就应该调用这个Skill:

  • "产品开发了好几个月,但还没有给用户看过"
  • "开发团队说'等发布再看看市场反应'"
  • "我们花了半年做的功能,用户反馈完全不是我们预期的"
  • "团队感觉工作很忙,但产品好像没有进展"
  • "每次发布都像一场战役,因为积累了大量改动"

E — Execution(可执行步骤)

步骤1:识别大批量行为

信号:

  • 产品开发的某个阶段超过2周没有任何用户接触
  • 团队说"功能做完了,等发布日期"
  • 一次性发布大量功能,而不是逐个验证
  • 错误/问题总是在发布前一周才集中爆发

步骤2:拆解大批量为小批量

原则:每个批量的完成标志是获得了关于这个批量的用户反馈,而不是"代码写完了"。

方法:

  • 产品功能:拆成"核心功能+次要功能",先发布核心,验证后再加
  • 开发任务:把"开发→测试→发布"的大循环,变成每个功能的小循环
  • 会议/决策:把大议题拆成小议题,逐个解决,逐个确认

步骤3:测量批量大小的影响

问题:当前从"想法"到"用户反馈"需要多长时间?

  • 目标:越短越好,核心功能≤1周
  • 如果>2周,问:"能否拆成更小的部分发布?"

步骤4:建立持续反馈机制

  • 持续部署:代码合并后自动发布到测试环境
  • A/B测试:每次发布都是一个实验
  • 快速回顾:每1-2周回顾"这批工作和预期相比如何"

B — Boundary(何时不适用)

不适用场景原因
硬件/制造业物理产品的小批量成本远高于软件
合规要求严格的产品医疗/金融产品的发布需要完整审批流程
极其稳定的业务这类业务的大批量发布反而成本更低

作者盲点提醒:小批量需要组织文化支持——如果团队习惯于"功能完成后才算成绩"的绩效考核,小批量会让团队感觉"什么都没完成"。需要在绩效评估上同步调整,让"快速验证"本身就是成绩。

关联Skills

  • MVP构建法 — MVP是产品开发中小批量的极端实践
  • Build-Measure-Learn循环 — 小批量让BML循环转得更快
  • 对比测试 — 小批量发布后,用对比测试快速验证效果

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 Small Batch AI skill do?

用小批量方式减少浪费、加速反馈。当产品开发周期过长、团队陷入"憋大招"困境、错误发现太晚导致返工量大时,使用此Skill。

Why use Small Batch on TypingMind?

Because you install it once and use it with any model. Small Batch 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 Small Batch in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ace3000chao/book2startup/tree/master/精益创业skills/skills/007-small-batch. 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 Small Batch?

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 Small Batch?

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

Is the Small Batch AI skill free?

Yes. It is published on GitHub by ace3000chao 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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