Wb Xhs Topic Bank

Community

wenziai

wb-xhs-topic-bank

Use when the user has positioning and wants a reusable Xiaohongshu topic/title bank instead of asking AI to freely brainstorm, especially to map topics to user needs such as emotional resonance, growth proof, new insight, learning value, or identity expression. Trigger phrases: "选题库", "标题公式", "每天发什么", "批量选题", "爆款需求", "XHS topic bank", "title formulas". Do not use before account positioning and target audience are known.

Overview

Publisher	wenziai
Repository	`wenzi-xiaohongshu-workflow`
Skill name	`wb-xhs-topic-bank`
Stars	178
Forks	34
Bundled files	1
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 wenziai on GitHub. Read the source before you install it.

Installation

Install the Wb Xhs Topic Bank 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/wenziai/wenzi-xiaohongshu-workflow/tree/main/wb-xhs-topic-bank
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.

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-topic-bank .claude/skills/wb-xhs-topic-bank

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

Use it in TypingMind

Enable Wb Xhs Topic Bank 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 Topic Bank 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 Topic Bank 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.

小红书七类选题库

R — 原文

"建选题库，别每天临时想。"

— 文子, X Article, 2026-07-06

"发帖前先想清楚：这条内容，满足了用户的哪一种需求？"

— yanliudreamer, X Article, 2026-07-05

I — 方法论骨架

选题焦虑来自每天从空白开始。文章的方法是把选题变成类型化生产：痛点、数字、对比、稀缺、共鸣、资源、反常识。

每种类型都有不同作用。痛点型负责命中问题，数字型负责制造清晰感，对比型负责凸显变化，稀缺型负责制造注意力，共鸣型负责建立信任，资源型负责提高收藏，反常识型负责制造转发理由。

WorkBuddy 不应该自由发挥，而应该在账号档案和对标公式约束下，按类型生成可筛选的选题。

yanliudreamer 系列补充了“选题需求层”：一条内容要先回答三问：解决了谁的问题、用户为什么点进来、看完能带走什么。标题公式只是入口，真正决定放大的，是这条内容满足了情感共鸣、见证成长、获得新知、学习提升、身份表达中的哪一种底层需求。

dbskill 的标题模块补充了“公式可追溯”原则：每个标题都要能说清来自哪类触发器、为什么适合这个话题、有没有保留悬念。标题不是越炸越好，而是要在 20 个中文字符以内，同时命中真实痛点、扩大受众词、至少包含 2 个张力元素。

xhs-visual-director-skill 补充了“标题必须能变成封面”的要求：选题库不只输出文字标题，还要判断每个选题适合哪种图文形态、封面钩子、主视觉和收藏理由。一个好选题应该能落成 3:4 竖版封面和 6-8 页滑动结构，而不是只停留在一句标题。

A1 — 文章中的应用

案例 1: 七类方向各出多个选题

问题: 新手每天不知道发什么。
方法论的使用: 作者让 WorkBuddy 按七种标题方向，每类生成多条选题。
结论: 一次生成一组选题，而不是每天临时想一条。
结果: 一周内容素材可以被快速筛出。

A2 — 触发场景

用户会在什么情境下需要这个 skill?

用户已经有定位，但每天不知道发什么。
用户要为一个赛道批量生成 20-50 个选题。
用户觉得 AI 标题太像营销号，需要用公式约束。
用户想建立可持续内容栏目。
用户想知道某个题目为什么值得点、值得看、值得收藏或转发。

语言信号

"帮我建选题库"
"小红书标题公式"
"一周发什么"
"这条内容满足什么需求"
"用户为什么要点进来"
"give me XHS topics"
"topic bank"

与相邻 skill 的区分

与 wb-xhs-low-follower-pattern 的区别: 低粉爆款 skill 负责从样本拆公式；本 skill 负责用公式生产新题。
与 wb-xhs-humanize-compliance 的区别: 本 skill 只产出题目和标题，后者处理正文初稿。

E — 可执行步骤

读取账号约束
- 确认目标用户、赛道关键词、变现路径、人设语气、禁区。
- 完成标准: 没有这些信息时先调用账号档案或定位 skill。
建立七类公式
- 为痛点、数字、对比、稀缺、共鸣、资源、反常识各生成 3-5 个模板。
- 完成标准: 每个模板包含可替换变量。
标注标题触发器
- 给每个候选标题标注主触发器: 认知冲突、好奇缺口、损失提醒、身份代入、数字锚定、结果承诺、案例证明、争议互动、场景条件、行动号召、权威借力、互动测试。
- 完成标准: 每个标题都有“类型来源 + 推荐理由”，不能只自由发挥。
做标题五项硬检
- 检查是否 ≤20 个中文字符、保留悬念、不把答案说完、用更大的用户词、击中真实痛点、至少有 2 个张力元素。
- 完成标准: 标题过长、过窄、太平、太像结论时必须重写。
叠加五类用户需求
- 为每个选题标注主需求: 情感共鸣、见证成长、获得新知、学习提升、身份表达。
- 完成标准: 每个选题都能说明“用户为什么会点/看/藏/评/转”。
做三问校验
- 对候选题逐条回答: 解决谁的问题、为什么点进来、看完带走什么。
- 完成标准: 回答不清的题目降级或重写。
批量生成选题
- 每类生成 5 个选题，附标题、目标读者、内容形态、收藏理由。
- 完成标准: 至少 35 个候选题。
筛选发布优先级
- 按变现相关度、用户痛感、制作难度、收藏价值、身份表达强度打分。
- 完成标准: 选出本周 3-5 个优先题。
补充视觉交付字段
- 为优先选题补充内容类型、传播目标、读者情绪、信息密度、封面钩子、主视觉方向、推荐风格和页数建议。
- 完成标准: 每个优先选题都能继续交给图文视觉导演生成 3:4 封面或 6-8 页图文。

B — 边界

不要在以下情况使用

用户没有定位或目标人群。
用户需要完整正文，而不是选题和标题。

失败模式

让 AI 自由发挥标题，导致表达过度夸张。
只生成标题，不说明内容形态和读者收益。
选题全是热点，和账号变现路径无关。
每条都追求信息密度，却没有情绪、身份或成长证据。
标题很好点，但正文没有明确带走感。
标题无法追溯到任何触发器，只是“看起来像爆款”。
标题把答案说完、话题太窄或超过 20 个中文字符。
选题只有文字标题，没有封面钩子、主视觉或滑动页结构。

作者盲点

七类公式能提高产出稳定性，但可能让账号表达变得模式化。
不同赛道可能需要补充其他类型，例如测评型、案例型、教程型。

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.

test-prompts.json4 KB

Frequently asked questions

What does the Wb Xhs Topic Bank AI skill do?

Why use Wb Xhs Topic Bank on TypingMind?

Because you install it once and use it with any model. Wb Xhs Topic Bank 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 Topic Bank in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wenziai/wenzi-xiaohongshu-workflow/tree/main/wb-xhs-topic-bank. 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 Topic Bank?

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 Topic Bank?

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

Is the Wb Xhs Topic Bank 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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Overview