Cross Platform Repurposing logo

Cross Platform Repurposing

Organization
social-media-skills
cross-platform-repurposing

Use to turn one piece of content into multiple platform-native posts — adapt, never copy-paste. Run when the user says "repurpose this," "turn this into posts for every platform," "cross-post," "make this work on TikTok/LinkedIn/X too," "one idea into a week of content," "turn this blog/video/podcast into social posts," or wants to fan content out. Reads brand-profile and voice first, extracts the core idea, then re-natives it for each platform (different hook, format, length, mechanics, CTA) using the content skills, and hands the fan-out to scheduling-and-queue. This is the multiplier that makes a content operation sustainable — but every version must stand on its own and feel native, never duplicated.

Overview

Publishersocial-media-skills
Repositoryskills
Skill namecross-platform-repurposing
Stars
215
Forks
44
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

    Published by social-media-skills on GitHub. Read the source before you install it.

Installation

Install the Cross Platform Repurposing 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/social-media-skills/skills/tree/main/skills/cross-platform-repurposing
  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/social-media-skills/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/cross-platform-repurposing .claude/skills/cross-platform-repurposing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cross Platform Repurposing 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 Cross Platform Repurposing 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 Cross Platform Repurposing 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.

Cross-Platform Repurposing

This skill turns one idea into many posts — the single biggest lever for producing content sustainably. One blog post becomes a week of social; one strong insight becomes five platform-native posts. It's how a small team posts like a big one.

The principle that makes it work — and the trap that ruins most attempts:

Repurposing is not reposting. Pasting the same caption onto every platform is the lazy version: it fits none of them natively, it looks like spam to anyone who follows you in two places, and several platforms quietly suppress obviously cross-posted content. The craft is atomize → adapt: extract the core idea, then rebuild it natively for each target — a different hook, format, length, and CTA, same truth and voice.

When to use this

  • Fan one post out across platforms (one → many).
  • Atomize a long piece (blog, video, podcast, newsletter) into many short posts (long → short).
  • Convert one format into others (a written insight → a carousel, a Reel, a thread).
  • Inside batch-content-plan, to stretch limited raw material into a full calendar.

When NOT to use it: when content is genuinely platform-specific (an in-joke, a platform-native trend, a reply) — forcing it elsewhere reads wrong. And when the audience overlaps almost entirely on two platforms, vary the angle, don't just relocate the post. Know when a piece shouldn't travel.

Step 0 — Read the foundation first

Load brand-profile.md and voice.md. Voice stays constant across platforms; register shifts per platform (use the voice tone-map — LinkedIn more formal, TikTok looser). Carry guardrails everywhere.

Step 1 — Atomize: find the core idea

Extract the reusable atom from the source — the point, the insight, the story, the stat, the moment. For a single post that's usually one atom; for long-form (a blog, a 20-minute video) it's several atoms, each strong enough to be its own post. Don't summarize a long piece into one weak post — pull its best 5–10 atoms. See references/repurposing-model.md.

Step 2 — Pick targets and direction

Decide the platforms and the direction of the repurpose (one→many, long→short, format→format, video↔text — see references/source-types.md). Direction determines the mechanics.

Step 3 — Re-native per platform (don't translate, rebuild)

For each target, rebuild the atom natively — composing the right skills:

  • A new hook per platform (hook-writer) — what hooks on a LinkedIn feed differs from a TikTok first frame.
  • The right format and length for that platform.
  • The copy (caption-writer), the script if video (reels-script), or the thread (thread-writer).
  • Native mechanics — hashtags, links (bio vs clickable vs comments), CTA.

What stays the same: the atom and the voice. What changes: hook, format, length, structure, CTA, mechanics, register. See references/platform-transforms.md.

Step 4 — Quality-check: does each stand alone and feel native?

Every version must pass two tests:

  1. Stands alone — someone who only sees this one post gets full value (no "as I said on LinkedIn…").
  2. Feels native — it looks built for this platform, not relocated. If you can tell it was copy-pasted, rebuild it.

If two platforms share most of the audience, change the angle between them, not just the formatting.

Step 5 — Hand off to scheduling

Pass the finished, per-platform versions to scheduling-and-queue to schedule/fan out (it confirms and validates first; WoopSocial applies per-platform fields). Space them out — don't fire the same idea everywhere at the same minute.

Quality bar — self-check

  • Did you atomize (extract the core idea) rather than translate the wrapper?
  • Is each version genuinely native — different hook, format, length, CTA — not copy-paste?
  • Does each stand on its own?
  • Is the voice constant with the register shifted per platform?
  • For long-form, did you pull multiple atoms, not one summary?
  • Did you recognize anything that shouldn't be repurposed?

If two versions are the same text with different hashtags, you reposted — go back to Step 3.

Edge cases

  • Heavy audience overlap across platforms → vary the angle/format between them so followers don't see the same post twice.
  • Platform-specific content (trends, replies, in-jokes) → don't force it elsewhere; say so.
  • Long-form with one idea → it's one atom; don't manufacture filler to hit a post count.
  • Video → text → pull the spoken insights/quotes as written atoms; don't just transcribe.
  • Text → video → hand the atom to reels-script to build it as a video, don't read the caption aloud.
  • Regulated/sensitive → carry compliance guardrails into every version.

Related skills

  • brand-profile, voice-builder — voice + register + guardrails.
  • hook-writer — a fresh hook for each platform.
  • caption-writer, reels-script, thread-writer — build each native version.
  • content-recycling — repurposing across time (refresh evergreen later).
  • captions-and-clipping, opus-clip — cutting clips from long video (podcasts, webinars) at depth.
  • batch-content-plan — uses this to stretch material into a calendar.
  • scheduling-and-queue — schedules/fans out the finished versions.

References

  • references/repurposing-model.md — atomize → adapt; what stays vs changes; the directions.
  • references/platform-transforms.md — how one idea becomes each platform's native post.
  • references/source-types.md — repurposing by source/direction (long-form, fan-out, cross-medium).
  • references/examples.md — one idea → five native versions, and a long-form atomization.

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 Cross Platform Repurposing AI skill do?

Use to turn one piece of content into multiple platform-native posts — adapt, never copy-paste. Run when the user says "repurpose this," "turn this into posts for every platform," "cross-post," "make this work on TikTok/LinkedIn/X too," "one idea into a week of content," "turn this blog/video/podcast into social posts," or wants to fan content out. Reads brand-profile and voice first, extracts the core idea, then re-natives it for each platform (different hook, format, length, mechanics, CTA) using the content skills, and hands the fan-out to scheduling-and-queue. This is the multiplier tha...

Why use Cross Platform Repurposing on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/social-media-skills/skills/tree/main/skills/cross-platform-repurposing. 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 Cross Platform Repurposing?

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 Cross Platform Repurposing?

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

Is the Cross Platform Repurposing AI skill free?

Yes. It is published on GitHub by social-media-skills 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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