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Post Analyze

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bozhouDev
post-analyze

拆解分析火爆的社交媒体帖子(推文、公众号文章、小红书帖子等),提取钩子类型、情绪驱动、为什么火,并根据判断写入经典选题库或选题池。当用户说"拆解这条帖子"、"分析一下为什么火"、"这条推文怎么写的"、"帮我拆一下"、或使用 /热帖拆解 时触发。用户分享一条帖子并想了解其传播机制时也应触发。

Overview

PublisherbozhouDev
Repositorybozhou-skills
Skill namepost-analyze
Stars
78
Forks
7
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by bozhouDev on GitHub. Read the source before you install it.

Installation

Install the Post Analyze 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/bozhouDev/bozhou-skills/tree/main/post-analyze
  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/bozhouDev/bozhou-skills.git /tmp/bozhou-skills
mkdir -p .claude/skills
cp -r /tmp/bozhou-skills/post-analyze .claude/skills/post-analyze
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Post Analyze 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 Post Analyze 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 Post Analyze 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.

热帖拆解

分析一条火爆帖子的核心机制,提取可学习的要素。

输入

用户提供一条帖子(粘贴文本或链接)。如果是链接,先用 WebFetch 抓取内容。

分析框架

精简拆解,只抓最有用的信息:

1. 一句话总结

这条帖子在说什么,写给谁看的。

2. 钩子分析

开头用了什么手法把人留住。常见类型:

  • 痛点共鸣("你是不是也...")
  • 反常识/颠覆认知("其实...是错的")
  • 数据冲击(具体数字开头)
  • 悬念/好奇心
  • 身份认同("作为一个...")
  • 争议性观点
  • 其他

不需要每个都勾选,只说这条帖子实际用了哪个,怎么用的。

3. 情绪驱动

这条帖子触发了什么情绪,让人愿意点赞/转发。说具体:

  • 焦虑 → 安全感("跟着做就行")
  • 好奇 → 满足("原来是这样")
  • 惊讶 → 分享冲动("我要告诉别人")
  • 共鸣 → 认同感("说得太对了")
  • 优越感 → 转发炫耀("我知道你们不知道的")

4. 为什么火

用 2-3 句话说清楚核心原因。要具体——"因为它用了 xxx 手法,触发了 xxx 情绪,让 xxx 人群产生了 xxx 反应",而不是"因为内容好"。

拆解后的去向

分析完成后,判断这条帖子的价值类型,并问用户确认去向:

建议进入经典选题库的情况: 这条帖子代表了一种可反复使用的选题模式。比如"零基础入门指南"是一种模式,"真实数据复盘"是一种模式,"反常识观点"是一种模式。关键是:这个模式换个话题还能再用。

建议进入选题池的情况: 这个话题本身有价值,用户可以用自己的角度写一篇类似的,但它不代表一种通用模式。

给出你的建议和理由,让用户最终决定。

写入经典选题库

追加到 选题中心/经典选题库.md:

markdown
## [模式名称]

**为什么反复火:** xxx
**情绪驱动:** xxx → xxx
**钩子类型:** xxx
**参考帖子:** 链接或原文摘要
**下次可以写:** 基于这个模式我能写什么

写入选题池

追加到 选题中心/选题池.md 的 ## 待写 下:

markdown
- [ ] 选题标题
  - 大概内容: 受这条帖子启发,我可以怎么写、切什么角度
  - 相关: 原帖链接

如果目录或文件不存在,先创建。

Frequently asked questions

What does the Post Analyze AI skill do?

拆解分析火爆的社交媒体帖子(推文、公众号文章、小红书帖子等),提取钩子类型、情绪驱动、为什么火,并根据判断写入经典选题库或选题池。当用户说"拆解这条帖子"、"分析一下为什么火"、"这条推文怎么写的"、"帮我拆一下"、或使用 /热帖拆解 时触发。用户分享一条帖子并想了解其传播机制时也应触发。

Why use Post Analyze on TypingMind?

Because you install it once and use it with any model. Post Analyze 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 Post Analyze in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bozhouDev/bozhou-skills/tree/main/post-analyze. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Post Analyze?

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 Post Analyze?

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

Is the Post Analyze AI skill free?

It is published on GitHub by bozhouDev. 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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