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Wb Xhs Low Follower Pattern

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wenziai
wb-xhs-low-follower-pattern

Use when the user has a Xiaohongshu niche and needs to find, compare, and extract reusable content structures from low-follower high-performing posts, or diagnose why a post did or did not become a breakout using click rate, retention, and interaction signals. Trigger phrases: "找对标", "低粉爆款", "拆爆款", "内容骨架", "点击率", "停留时长", "互动率", "competitor pattern", "viral post structure". Do not use before the monetization direction is clear.

Overview

Publisherwenziai
Repositorywenzi-xiaohongshu-workflow
Skill namewb-xhs-low-follower-pattern
Stars
178
Forks
34
Bundled files
1
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.

  • 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 wenziai on GitHub. Read the source before you install it.

Installation

Install the Wb Xhs Low Follower Pattern 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/wenziai/wenzi-xiaohongshu-workflow/tree/main/wb-xhs-low-follower-pattern
  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/wenziai/wenzi-xiaohongshu-workflow.git /tmp/wenzi-xiaohongshu-workflow
mkdir -p .claude/skills
cp -r /tmp/wenzi-xiaohongshu-workflow/wb-xhs-low-follower-pattern .claude/skills/wb-xhs-low-follower-pattern
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wb Xhs Low Follower Pattern 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 Wb Xhs Low Follower Pattern 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 Wb Xhs Low Follower Pattern 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.

低粉爆款内容骨架拆解

R — 原文

"找低粉爆款，直接拆。"

— 文子, X Article, 2026-07-06

"一条内容能不能爆，本质看这个：点击率 × 停留时长 × 互动率。"

— yanliudreamer, X Article, 2026-07-04

I — 方法论骨架

对新手来说，大号爆款往往混入了粉丝基础、品牌认知和历史权重，不一定能复制。更有价值的样本，是粉丝不多但近期数据突出的内容。

这个 skill 的核心是把“我喜欢这篇”变成结构分析：关键词入口、发布时间、账号粉丝量、封面标题点击承诺、开头三句、正文组织、停留理由、收藏理由、评论区反馈、标签组合。

yanliudreamer 系列补充了爆款判断公式：爆款不是绝对数据高，而是在小流量测试中明显优于平均。拆解时必须把样本拆成三段：点击率靠封面标题，停留时长靠开头和结构，互动率靠收藏、评论、转发背后的价值或共鸣。

拆解的目标不是复制原文，而是提取可迁移骨架，再换成用户自己的经历、观点和素材。

dbskill 的对标模块补充了“五重过滤”：样本是否接近商业目标、机制能否被看懂、结构能否用自己的素材复用、是否剔除了“我不一样”的主观噪音、是否避免空谈赛道。共鸣诊断还要求拆出内容为什么让人想说、想用、想站队、想分享给第一批人。

xhs-visual-director-skill 补充了视觉拆解维度：低粉高数据样本不仅要拆标题和正文，还要拆封面构图、3:4 画幅、信息层级、主视觉、页间节奏、是否有统一视觉母版、是否避免 PPT 感和廉价模板感。

A1 — 文章中的应用

案例 1: 搜关键词并切换最新排序

  • 问题: 默认搜索结果容易被旧爆款和大号占据。
  • 方法论的使用: 作者建议输入赛道核心词后看较新的高数据内容。
  • 结论: 新近跑出的低粉高数据内容更适合新号学习。
  • 结果: WorkBuddy 可以基于多个样本总结内容公式。

A2 — 触发场景

用户会在什么情境下需要这个 skill?

  1. 用户已经确定赛道，但不知道什么内容能被平台接受。
  2. 用户想找对标账号，却只会收藏大号。
  3. 用户有 5-10 篇样本，希望让 Agent 总结规律。
  4. 用户要把一个爆款结构改写成自己的内容。
  5. 用户有一条笔记数据异常好或异常差，想知道卡在点击、停留还是互动。

语言信号

  • "帮我找对标"
  • "怎么拆小红书爆款"
  • "这个赛道低粉爆款怎么看"
  • "为什么这条没爆"
  • "点击率停留互动怎么看"
  • "extract viral structure"
  • "competitor content pattern"

与相邻 skill 的区分

  • 与 wb-xhs-monetization-backsolve 的区别: 本 skill 不判断赛道值不值得做，只拆已选赛道的内容样本。
  • 与 wb-xhs-topic-bank 的区别: 本 skill 从外部样本提取公式；选题库 skill 用公式生成新选题。

E — 可执行步骤

  1. 定义搜索词

    • 从用户赛道中提取 5-10 个核心词、问题词和人群词。
    • 完成标准: 每个词都能在小红书搜索框直接使用。

  2. 筛选低粉高数据样本

    • 记录账号粉丝量、发布时间、点赞收藏评论、标题、封面承诺。
    • 完成标准: 至少得到 5 个“粉丝不多但数据明显高于同类”的样本。

  3. 做五重对标过滤

    • 判断样本是否与变现相关、内容机制是否可理解、是否能用自己的素材复用、是否排除了主观偏见、是否没有陷入抽象赛道讨论。
    • 完成标准: 只保留值得学习的样本，而不是只保留看起来热闹的样本。

  4. 拆解内容骨架

    • 对每篇样本拆封面承诺、标题句式、开头三句、正文段落功能、结尾动作、标签。
    • 完成标准: 输出一张样本对比表。

  5. 拆三指标杠杆

    • 判断样本主要强在点击率、停留时长还是互动率；记录对应证据。
    • 完成标准: 每篇样本都有“入口强点 / 内容强点 / 互动强点”的判断。

  6. 识别五类爆款需求

    • 判断内容满足的是情感共鸣、见证成长、获得新知、学习提升、身份表达中的哪一类或哪几类。
    • 完成标准: 不是只拆形式，也拆用户为什么愿意点、看、藏、评、转。

  7. 做共鸣原因解码

    • 判断样本是否释放了用户不敢说的话、满足了具体使用动机、提供了立场框架、让第一批人愿意转述、强化了某种信念。
    • 完成标准: 输出“它为什么会被互动”的心理原因，而不只输出排版结构。

  8. 总结可迁移公式

    • 提炼 3-5 个可复用标题模板和 1 个正文结构模板。
    • 完成标准: 模板不含原作者专属事实，可以替换成用户素材。

  9. 拆解视觉骨架

    • 记录封面标题占比、主视觉位置、背景风格、字体层级、强调色、页码/角标、内页结构和页面节奏。
    • 完成标准: 输出“可复用视觉公式”，例如封面冲击、内页卡片、对比页、案例页、总结页如何组织。

  10. 判断视觉是否值得学

  • 检查样本是否手机端可读、是否有收藏价值、是否像小红书图文而不是 PPT、是否有统一风格。
  • 完成标准: 只学习视觉结构，不复制原作者图片、素材或专属设计元素。

B — 边界

不要在以下情况使用

  • 用户还没确定服务人群或变现路径，应先调用定位 skill。
  • 用户要求逐字模仿某篇内容，应提醒只提取结构和逻辑。

失败模式

  • 只看点赞，不看账号粉丝量和发布时间。
  • 只看最终数据，不拆点击、停留和互动分别发生在哪里。
  • 把原文表达搬进自己的笔记，导致账号缺少真实差异。
  • 样本太少就总结公式。
  • 看到一篇爆了就误以为找到公式，没有连续验证。
  • 对标对象与自己的商业目标无关，只是数据好看。
  • 只拆标题句式，不拆它让谁获得了表达、使用或站队理由。
  • 只学封面颜色和装饰，不拆信息层级、页间节奏和手机端可读性。
  • 把别人的视觉样式整体照搬，导致账号缺少自己的视觉识别。

作者盲点

  • 文章默认用户能自行判断数据异常，实际可能需要更细的数据阈值。
  • 不同赛道的数据基准差异很大，不能用统一数字判断。

相关 skills

  • depends-on: [wb-xhs-monetization-backsolve]
  • contrasts-with: []
  • composes-with: [wb-xhs-topic-bank, wb-xhs-account-profile]

审计信息

  • 验证通过: V1 ✓ / V2 ✓ / V3 ✓
  • 测试通过率: prompts prepared
  • 蒸馏时间: 2026-07-07

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 Wb Xhs Low Follower Pattern AI skill do?

Use when the user has a Xiaohongshu niche and needs to find, compare, and extract reusable content structures from low-follower high-performing posts, or diagnose why a post did or did not become a breakout using click rate, retention, and interaction signals. Trigger phrases: "找对标", "低粉爆款", "拆爆款", "内容骨架", "点击率", "停留时长", "互动率", "competitor pattern", "viral post structure". Do not use before the monetization direction is clear.

Why use Wb Xhs Low Follower Pattern on TypingMind?

Because you install it once and use it with any model. Wb Xhs Low Follower Pattern 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 Wb Xhs Low Follower Pattern in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wenziai/wenzi-xiaohongshu-workflow/tree/main/wb-xhs-low-follower-pattern. 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 Wb Xhs Low Follower Pattern?

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 Wb Xhs Low Follower Pattern?

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

Is the Wb Xhs Low Follower Pattern AI skill free?

It is published on GitHub by wenziai. 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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