Skill Cross Platform Diff logo

Skill Cross Platform Diff

OrganizationPopular
ZJU-REAL
skill-cross-platform-diff

跨平台内容差异深度分析。分析同一话题/内容在不同平台（小红书、抖音、B站、知乎、 微博、公众号、X 等）的呈现差异：内容形式、受众偏好、话语体系、流量逻辑、变现路径。 帮创作者理解"同一个内容在不同平台应该怎么做"。当用户说"小红书和抖音有什么区别"、 "这个话题在B站怎么做"、"跨平台差异"、"不同平台"、"各平台怎么做"、"平台特点"时触发。 和 skill-content-repurposing 的区别：repurposing 做格式适配和内容改写， cross-platform-diff 做深度差异分析和策略洞察。

Overview

PublisherZJU-REAL
RepositoryEasel
Skill nameskill-cross-platform-diff
Stars
1.2K
Forks
175
Bundled files
2
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • 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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ZJU-REAL on GitHub. Read the source before you install it.

Installation

Install the Skill Cross Platform Diff 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.
  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/ZJU-REAL/Easel.git /tmp/Easel
mkdir -p .claude/skills
cp -r /tmp/Easel/skills/openclaw/skill-cross-platform-diff .claude/skills/skill-cross-platform-diff
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Cross Platform Diff 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 Skill Cross Platform Diff 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 Skill Cross Platform Diff 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.

跨平台内容差异分析

分析同一话题在不同中文社媒平台的呈现差异，输出平台适配策略。

输入

字段必填说明
话题/内容主题是要分析的话题、关键词或一段内容
目标平台推荐要对比的平台列表（默认全平台扫描）
分析目的推荐"我想把这个内容发到各平台" / "想了解平台差异" / "选择主攻平台"
已有内容可选如果用户已有某平台的内容，可作为对比基准
赛道 / 行业可选不同赛道在各平台的差异可能不同

支持的平台：小红书、抖音、B站、知乎、微博、公众号、视频号、X/Twitter、快手。

输出

markdown
# 跨平台差异分析：[话题/主题]

## 分析概要
- 分析话题：[话题]
- 对比平台：[平台列表]
- 分析时间：[日期]

## 平台差异全景

### 对比矩阵
| 维度 | 小红书 | 抖音 | B站 | 知乎 | 微博 | 公众号 |
|------|--------|------|-----|------|------|--------|
| 主力内容形式 | | | | | | |
| 核心受众画像 | | | | | | |
| 话语体系/语气 | | | | | | |
| 流量分发逻辑 | | | | | | |
| 内容生命周期 | | | | | | |
| 变现路径 | | | | | | |
| 该话题适配度 | | | | | | |

### 平台逐一分析

#### [平台名]
- **该话题在此平台的典型呈现**：内容形式、风格、角度
- **受众期待**：这个平台的用户看到这个话题时期待什么
- **话语体系**：用词习惯、语气、表达方式
- **流量逻辑**：该话题如何获取流量（搜索 / 推荐 / 社交传播）
- **成功案例特征**：该话题在此平台表现好的内容有什么共性
- **避坑指南**：该话题在此平台容易踩的坑
- **变现可能**：该话题在此平台的商业化路径

（每个目标平台各一节）

## 核心差异洞察
1. [最重要的跨平台差异发现]
2. [第二重要的发现]
3. ...

## 平台选择建议
- **主攻推荐**：[最适合的 1-2 个平台 + 原因]
- **辅助布局**：[值得同步运营的平台 + 策略]
- **不推荐**：[不适合的平台 + 原因]

## 适配策略
针对每个推荐平台，给出从"原始内容"到"平台适配内容"的转化要点：
- 标题改写方向
- 内容结构调整
- 语气和用词转换
- 格式和长度要求
- 标签/话题策略
- 发布时间建议

## 注意事项
- 平台规则和算法变化的时效性说明
- 分析依据（平台公开信息 + 行业认知）

执行步骤

  1. 明确分析范围

    • 确认话题/主题和目标平台
    • 若用户未指定平台，默认分析主流 6 个平台：小红书、抖音、B站、知乎、微博、公众号
    • 确认分析目的（了解差异 / 选平台 / 做适配）

  2. 加载平台知识

    • 读取 references/platform-traits.md 获取各平台基础特征
    • 这是底层知识，不直接输出，用于支撑分析

  3. 话题-平台交叉分析

    • 对每个目标平台，分析该话题在此平台的适配情况：
      • 内容形式：该话题在此平台通常以什么形式呈现（图文 / 短视频 / 长视频 / 问答 / 文章）
      • 受众期待：此平台用户看到这个话题时想获得什么（知识 / 娱乐 / 种草 / 讨论 / 共鸣）
      • 话语体系：此平台讨论这个话题的用词、语气、风格
      • 流量来源：该话题在此平台主要通过什么渠道获取流量

  4. 差异提取

    • 做平台间横向对比，识别关键差异点
    • 重点关注：同一话题在不同平台的"信息需求差异"（同一个关键词，用户在知乎搜和在小红书搜的目的完全不同）
    • 总结差异规律（如"越深度的平台越需要论据，越碎片的平台越需要情绪"）

  5. 适配度评估

    • 结合话题特性和平台特征，给出各平台的适配度评级
    • 评估维度：内容天然匹配度、竞争饱和度、变现可行性、运营成本
    • 给出主攻/辅助/不推荐的分层建议

    适配度评分（每项 1-5 分）

    维度1 分3 分5 分
    内容匹配该话题在此平台几乎无人关注有一定受众但非主流该平台此类内容流量大、互动高
    竞争饱和头部账号垄断，新人几乎无机会有竞争但仍可差异化内容稀缺，供不应求
    变现可行该平台此类内容无商业模式可接广告但品牌少品牌方活跃，变现路径清晰
    运营成本需要持续高频更新+运营互动中等运营投入内容长尾效应强，运营压力小

  6. 适配策略生成

    • 对推荐的平台，给出具体的内容适配策略
    • 不做内容改写（那是 repurposing 的职责），而是给出"转化方向指引"
    • 包括标题方向、结构调整、语气转换、格式要求、标签策略

  7. 输出报告

    • 按模板生成完整分析报告
    • 保存到 outputs/

Profile 感知

有 Profile 时：

  • 读取 identity.md（赛道定位）、platforms.md（当前主攻平台）、audience.md（受众画像）
  • 平台选择建议对齐账号现有能力（"你擅长长图文，B站和知乎比抖音更适合你"）
  • 适配策略考虑账号已有的内容资产（"你公众号的深度文章可以拆成小红书的卡片组"）
  • 变现路径建议结合 Profile 中的商业化目标

无 Profile 时：

  • 退到通用模式，做客观的平台差异分析
  • 平台选择建议基于话题特性而非账号特性
  • 不做"你适合什么"的个性化判断，只做"这个话题适合什么平台"

自研溯源与参考的学术研究/开源项目见同目录 EASEL-META.md。

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 Skill Cross Platform Diff AI skill do?

跨平台内容差异深度分析。分析同一话题/内容在不同平台（小红书、抖音、B站、知乎、 微博、公众号、X 等）的呈现差异：内容形式、受众偏好、话语体系、流量逻辑、变现路径。 帮创作者理解"同一个内容在不同平台应该怎么做"。当用户说"小红书和抖音有什么区别"、 "这个话题在B站怎么做"、"跨平台差异"、"不同平台"、"各平台怎么做"、"平台特点"时触发。 和 skill-content-repurposing 的区别：repurposing 做格式适配和内容改写， cross-platform-diff 做深度差异分析和策略洞察。

Why use Skill Cross Platform Diff on TypingMind?

Because you install it once and use it with any model. Skill Cross Platform Diff 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 Skill Cross Platform Diff in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ZJU-REAL/Easel/tree/main/skills/openclaw/skill-cross-platform-diff. 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 Skill Cross Platform Diff?

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 Skill Cross Platform Diff?

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

Is the Skill Cross Platform Diff AI skill free?

Yes. It is published on GitHub by ZJU-REAL under the Apache-2.0 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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