Dare To Follow

Community

kangarooking

dare-to-follow

当用户在考虑是否进入一个已有竞争者的市场、或者纠结"先发还是后发"时使用。典型触发：用户说"这个市场已经有很多人在做了，还值得进入吗""我们应该抢占先机还是等市场成熟""怎么在红海中竞争""小企业怎么和大企业竞争"。核心逻辑：敢为天下后，前提是有差异化能力——看清市场后集中兵力精准切入。不适用于：完全创新的蓝海市场（没有人做过，不存在"后发"）、纯垄断市场（无法进入）。

Overview

Publisher	kangarooking
Repository	`duan-yongping-skill`
Skill name	`dare-to-follow`
Stars	57
Forks	13
Bundled files	1
License	MIT
Links	Skill source Repository

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 kangarooking on GitHub. Read the source before you install it.

Installation

Install the Dare To Follow AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/kangarooking/duan-yongping-skill/tree/main/business-logic/dare-to-follow
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/kangarooking/duan-yongping-skill/tree/main/business-logic/dare-to-follow

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/kangarooking/duan-yongping-skill.git /tmp/duan-yongping-skill
mkdir -p .claude/skills
cp -r /tmp/duan-yongping-skill/business-logic/dare-to-follow .claude/skills/dare-to-follow

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dare To Follow 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 Dare To Follow 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 Dare To Follow 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.

"敢为天下后"后发制人策略

R — 原文 (Reading)

"没有差异化的'敢为天下后'是无法生存的。所谓差异化指的就是客户需要但别人未能满足的东西。"

— 段永平, 第4节

"敢为天下后，前提是你能够提供用户需要而别人没有的东西。微软没有什么东西是先做的。"

— 段永平, 第4节

I — 方法论骨架 (Interpretation)

这是一个市场进入策略框架，核心思想是：不追求先发优势，等市场验证后再进入，但必须带差异化。

前提条件（缺一不可）：

市场已被验证（有人做成了，证明需求存在）
自己能提供差异化（用户需要但现有产品没有的）
有足够的资源集中投入

为什么后发更好：

培育市场的代价由先行者承担——小企业付不起这个学费
看清市场后做SWOT分析更可靠，减少了不确定性
进入时机选在产品成长期与成熟期之间——需求确认但格局未定

关键区分：

"敢为天下后" ≠ 消极等待 → 而是看清后精准出击
"敢为天下后" ≠ 简单模仿 → 必须有差异化的创新
后发的核心是"后中争先"——进入后做到最好

适用边界：

适合：已有成熟模式但差异化空间大的市场
不适合：真正全新的蓝海（没有可参考的模式）

A1 — 书中的应用 (Past Application)

案例 1: 步步高进入VCD市场

问题: VCD市场已有万燕等先行者，步步高要不要做？
方法论的使用: 市场已被验证（VCD需求确实存在），步步高能提供差异化（更好的品质和品牌），后发进入
结论: 等市场成型后切入
结果: 步步高成为VCD市场领导者，而先行者万燕因培育市场代价过大而失败

案例 2: OPPO/vivo进入手机市场

问题: 手机市场已有诺基亚、摩托罗拉等巨头
方法论的使用: 市场巨大且已有验证，步步高能提供差异化（更好的渠道、更了解中国消费者、聚焦线下）
结论: 后发进入，但集中兵力做差异化
结果: OPPO/vivo成为全球前五手机品牌

案例 3: 微软——后发制人的标杆

问题: 微软为什么总是在别人后面却总是赢？
方法论的使用: "微软没有什么东西是先做的"——操作系统（IBM先做）、浏览器（网景先做）、Office（各种先做者），但微软每次后发都带了差异化和资源优势
结论: 后发不是问题，有没有差异化才是

A2 — 触发场景 (Future Trigger) ★

用户会在什么情境下需要这个 skill?

用户想进入一个已有竞争者的市场，担心"太晚了"
用户在纠结是先发（培育市场）还是后发（等市场成熟）
用户是小企业，资源有限，不知道该进攻哪个市场
用户在分析"为什么有些后发者赢了先行者"
用户在评估某个市场的进入时机

语言信号 (用户的话里出现这些就应激活)

"这个市场已经有人做了，还值得进入吗"
"我们应该先发还是等一等"
"小企业怎么和大企业竞争"
"太晚了吗" / "还有机会吗"
"红海里怎么活" / "怎么后来居上"

与相邻 skill 的区分

与 business-model-evaluator 的区别: business-model-evaluator 评估已有生意的模式好坏，本 skill 决定要不要进入一个新市场——是决策工具不是评估工具
与 stop-doing-list 的区别: stop-doing-list 说"不做没差异化的产品"，本 skill 说"只有有差异化才能后发进入"——是正反两面

E — 可执行步骤 (Execution)

市场验证检查
- 完成标准: 确认市场已有成功案例（至少1-2家公司在赚钱），需求被验证
- 判停条件: 如果市场完全没人做过、需求未验证 → 不适用本策略，需要另外评估
差异化能力评估
- 完成标准: 列出你能提供的、现有玩家无法满足的用户需求（至少1个核心差异化点）
- 判停条件: 如果找不到差异化 → "没有差异化的敢为天下后就变成悲剧" → 不进入
资源和时机评估
- 完成标准: 确认自己有足够资源集中投入这一个市场；判断市场处于成长期还是成熟期
- 最佳进入时机：成长期中后期（需求确认+格局未定）
集中兵力，后中争先
- 完成标准: 制定"进入后做到最好"的执行计划——聚焦一个差异化点做到极致
- 关键: 不是"试试看"，而是"全力以赴"

B — 边界 (Boundary) ★

不要在以下情况使用此 skill

真正创新的蓝海市场（没人做过，不存在"后发"选项）
强监管/牌照制市场（后发者可能根本无法进入）
网络效应极强的平台（后发者很难打破先行者的网络效应壁垒）

作者在书中警告的失败模式

没有差异化的后发: "没有差异化的'敢为天下后'是无法生存的"——这是最致命的错误
照搬先行者: 如果只是模仿没有创新，后发等于送死
进入太晚: 市场已完全成熟、格局已定后进入，差异化空间消失

作者的盲点 / 时代局限

段永平的经验来自消费电子行业（产品差异化空间大），在差异化空间极小的行业（如基础原材料），"敢为天下后"可能不适用
网络效应型平台（如微信、Facebook）的先行者优势极强，后发者即使有差异化也很难颠覆

容易混淆的邻近方法论

蓝海战略: 蓝海战略是创造全新市场空间，本策略是在已有市场中找差异化切入点——市场不同但都强调差异化
快速跟随者(Fast Follower): 概念相似，但段永平更强调"必须有差异化"这个硬前提——不只是快速跟随

审计信息

验证通过: V1 ✓ / V2 ✓ / V3 ✓
测试通过率: {{%}} (详见 test-prompts.json)
蒸馏时间: 2026-04-16

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

test-prompts.json5 KB

Frequently asked questions

What does the Dare To Follow AI skill do?

Why use Dare To Follow on TypingMind?

Because you install it once and use it with any model. Dare To Follow 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 Dare To Follow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kangarooking/duan-yongping-skill/tree/main/business-logic/dare-to-follow. 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 Dare To Follow?

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 Dare To Follow?

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

Is the Dare To Follow AI skill free?

Yes. It is published on GitHub by kangarooking 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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Dare To Follow

Overview