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Gr Social Distill

Community
Gingiris-1031
gr-social-distill

把一篇已发布的博客拆成 4 个社媒变体:X thread(10-15 推)、小红书笔记(3-5 图)、LinkedIn carousel、 dev.to / Zenn 二发。目标:在不新写博客的前提下激活 Organic Social 分发渠道。 当用户说"把这篇博客改成推特"、"做小红书版本"、"社媒分发"、"复用博客到多平台"时调用。

Overview

PublisherGingiris-1031
Repositorygingiris-skills
Skill namegr-social-distill
Stars
88
Forks
6
Bundled files
8
LicenseMIT
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.

  • 8 bundled files

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

  • Open source

    Published by Gingiris-1031 on GitHub. Read the source before you install it.

Installation

Install the Gr Social Distill 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/Gingiris-1031/gingiris-skills/tree/main/skills/gr-social-distill
  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/Gingiris-1031/gingiris-skills.git /tmp/gingiris-skills
mkdir -p .claude/skills
cp -r /tmp/gingiris-skills/skills/gr-social-distill .claude/skills/gr-social-distill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gr Social Distill 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 Gr Social Distill 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 Gr Social Distill 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.

⚠️ 2C 产品的渠道调整

本 skill 默认 dev / B2B 渠道。2C 消费品 / 教育 / 应用:获客主战场是 垂类社区 + 短视频 + 垂直 KOL,按地区公开数据选第一平台(如印尼/泰国短视频已反超 Facebook)。KOL 优先 nano / micro 垂类——粉丝越多互动率越低,micro > mega 性价比更高。完整 2C 渠道数据库 + 公开来源见 → gingiris-seo-geo/references/2c-adaptation.md。


gr-social-distill — 博客到社媒复用

核心理念

复用 > 新创。已发 60 篇博客,但社媒只用了一次的话,下一步 ROI 最高的不是写新文,是拆变体(来源:原子库 2026Q2_016)。

一篇 2000-3500 字的博客 → 4 种社媒格式 → 4x 流量放大,而且不需要新内容灵感。


什么时候用

场景动作
"把这篇博客拆成 X thread"X 模板(见下)
"做小红书版本"小红书模板(75 公式标题)
"LinkedIn 怎么发"LinkedIn carousel 模板(仅 B2B 内容)
"dev.to / Zenn 二发"技术博客跨平台模板
"每周分发节奏"完整 workflow(1 篇博客 → 4 变体)

输入

  • 1 个已发布博客 URL 或 _posts/ 文件名
  • 可选:目标渠道(X / xhs / LinkedIn / dev.to / Zenn / all)

输出

按渠道产出对应 markdown 或 draft 文本,直接可发。


4 渠道模板

1️⃣ X thread(10-15 推)

结构:

[推 1 — HOOK] 一个反常识 / 数据 / 问题引入(≤ 280 字,是否有吸引力决定全盘)
[推 2 — STAKES] 我 X 个月踩过 Y 个坑,结论是...
[推 3-12 — BODY] 每推一个要点。每推 ≤ 280 字。用短句。
[推 13 — TL;DR] 回到 HOOK 的答案
[推 14 — LINK] 链接到原博客 + 1 句 why it's worth clicking
[推 15 — CTA] @WeiYipei if you want the template

原则:

  • 每推独立成立,单独截图也能被转发
  • 用具体数字代替形容词("30 天内" vs "很快")
  • 每 3-4 推插 1 张截图 / 表格
  • Hook 句型参考:
    • "Here's what 60 blog posts taught me about X:"
    • "I analyzed 27 X tools. 22 of them aren't worth $0."
    • "Stop doing X. I tried it for 90 days. Data:"

详细例句库见 templates/x-thread.md。


2️⃣ 小红书笔记(3-5 张图)

标题公式(75 爆款公式,抄 dbs-xhs-title):

  • 数字派:"我测了 27 个 XX 工具,只有 4 个真的免费好用"
  • 踩坑派:"别再用 XX 了,我花 3 万块换的教训"
  • 教程派:"手把手教你 5 步搞定 XX(附工具清单)"
  • 反差派:"用了 3 年 XX,结果发现 Y 才是真香"
  • 揭秘派:"做 XX 的人不会告诉你的 3 件事"

内容结构(每篇 < 1000 字):

  • 开头 100 字:钩子 + 痛点共鸣
  • 正文 600 字:3-5 个要点,每个 100-200 字
  • 结尾 100 字:TL;DR + 互动问题
  • 加 5-8 个相关 tag:#出海创业 #SaaS增长 #独立开发者 ...

配图(3-5 张):

  • 图 1:标题图(大字标题 + 关键数字)
  • 图 2-4:每张 1 个要点(用手机截图 + 红笔框关键词)
  • 图 5(可选):数据表 / 对比图

详细模板见 templates/xhs-note.md。


3️⃣ LinkedIn carousel(仅 B2B / 技术文)

触发条件:原博客是 B2B / 企业 / 开发者向内容才发 LinkedIn,ToC 内容跳过。

10 页 carousel 结构:

  • 页 1:标题("How we [result] in [time]",配 key visual)
  • 页 2:问题定义(读者 why care)
  • 页 3-8:6 个步骤 / 要点,每页 1 个
  • 页 9:数据结果(before/after 对比)
  • 页 10:CTA(关注 + 链接到原博客)

每页内容:

  • 大字标题(≤ 40 字)
  • 3-5 条 bullet(≤ 10 字 / 条)
  • 配图 / icon / chart

正文(caption 500-1000 字):

  • 开头 3 行抓住注意(LinkedIn 折叠显示)
  • 正文讲"我学到什么"(不是"工具有什么功能")
  • 结尾问问题引评论

详细模板见 templates/linkedin-carousel.md。


4️⃣ dev.to / Zenn 二发

适用:原博客是技术向(SEO / GEO / API / 代码 / 工具)

关键规则(从 2026-04-20 dev.to 掉榜事件学到):

  • ⚠️ canonical_url 必须指向 dev.to 自身,不要指向 gingiris.tools
    • 错误做法:canonical_url: https://gingiris.tools/...
    • 正确做法:canonical_url: https://dev.to/iris1031/... 或留空
    • 原因:canonical 指向主站 → Google 把 dev.to 版视为副本 → 不参与独立 SERP
  • 发布时间差 ≥ 7 天(不要同天发,避免 Google 认为 copy)
  • 内容做本地化变化:改开场 hook、加 1-2 段 dev.to 用户视角的评论
  • 末尾加"Originally I wrote this for my blog at gingiris.tools"(建立双向 reference,但不用 canonical)

Zenn 特殊:

  • frontmatter type: tech 技术文 / type: idea 观点文
  • 日文版本优先,标题用"〜してみた" / "〜の作り方"等日式句型
  • emoji 系统选可爱一点的(📝 / 🚀 / 💡)

详细模板见 templates/devto-zenn.md。


每周工作流(Phase 2 主线 2 的节奏)

每周一 10:00
    ↓
从 gr-seo-patrol 输出里挑:
- Organic Search 流量 top 3
- 或:最近 30 天新发且排名 top 30 的
    ↓
选 1 篇"高潜力博客"作为本周分发源
    ↓
产出 4 变体(X / xhs / LinkedIn / dev.to)
    ↓
分散发布:
- 周一:X thread
- 周三:小红书笔记
- 周五:LinkedIn carousel(如 B2B)
- 次周周一:dev.to / Zenn 二发(延迟 7 天)
    ↓
下周一:复盘
- 哪个渠道带回了多少 GA4 referral?
- 哪个变体互动率最高?
- 更新 MEMORY 里的分发 playbook

追踪成效

发布后每个变体记录 UTM:

?utm_source=x|xhs|linkedin|devto&utm_medium=social&utm_campaign=distill_{post-slug}

GA4 Referral 报告里按 utm_source 分组,判断每个渠道 ROI。


级联推荐

  • 变体发完后 24h → gr-seo-patrol 看 Organic Social 占比是否上涨
  • 发现某个渠道 ROI 特别高 → 下周加码(同渠道多发 2 篇)
  • 小红书爆款 → 加大 UGC 创作者合作力度(联动 gr-aso UGC 创作者矩阵策略)
  • dev.to 排名上来 → 确认 canonical 配置,避免重蹈覆辙

反模式

  • ❌ 同一天 4 渠道齐发 —— 你自己的读者重复看到同内容会反感
  • ❌ 原文复制粘贴 —— X / xhs / LinkedIn 每个渠道有自己的语境,照搬互动率差 10x
  • ❌ dev.to canonical 指向主站 —— 2026-04-20 血泪教训,永远 self-canonical
  • ❌ LinkedIn 发 ToC 内容 —— 严重不匹配平台受众
  • ❌ 小红书加外链(平台严重降权 —— 把 URL 改成"主页简介找链接")
  • ❌ 一次拆完 10 篇,堆积在 draft 里不发 —— 宁可 1 篇完整分发 > 10 篇半成品

加入出海社群与购买资料包 · Community & Resources

  • 加入出海社群: 微信添加 staywildgrow,留言「加入社群」即可。

  • 购买资料包: 微信添加 staywildgrow,咨询购买 Gingiris 出海资料包,价格 599 元人民币。

  • Join the global growth community: Add staywildgrow on WeChat and send “加入社群” (join the community).

  • Get the resource pack: Add staywildgrow on WeChat to purchase the Gingiris global growth resource pack for CNY 599.

社群与资料包为可选入口,不影响本开源 Skill 的安装和使用。

The community and paid resource pack are optional; neither is required to install or use this open-source skill.

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 Gr Social Distill AI skill do?

把一篇已发布的博客拆成 4 个社媒变体:X thread(10-15 推)、小红书笔记(3-5 图)、LinkedIn carousel、 dev.to / Zenn 二发。目标:在不新写博客的前提下激活 Organic Social 分发渠道。 当用户说"把这篇博客改成推特"、"做小红书版本"、"社媒分发"、"复用博客到多平台"时调用。

Why use Gr Social Distill on TypingMind?

Because you install it once and use it with any model. Gr Social Distill 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 Gr Social Distill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Gingiris-1031/gingiris-skills/tree/main/skills/gr-social-distill. 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 Gr Social Distill?

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 Gr Social Distill?

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

Is the Gr Social Distill AI skill free?

Yes. It is published on GitHub by Gingiris-1031 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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