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Content Atomizer

Community
sanky369
content-atomizer

Turn one piece of long-form content (blog post, video/podcast transcript, guide, talk) into a ready-to-post multi-platform content pack: LinkedIn posts, X/Twitter thread, newsletter item, short-video scripts, carousel outlines, quote pulls, and more — each natively adapted, not copy-pasted. Use when the user says 'repurpose this post', 'turn this into social content', 'atomize this', 'get more mileage out of my content', 'make LinkedIn posts from my article', or shares long-form content wanting distribution. Produces an idea inventory plus a per-platform atomization pack with a suggested posting order.

Overview

Publishersanky369
Repositoryvibe-building-skills
Skill namecontent-atomizer
Stars
31
Forks
7
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

    Published by sanky369 on GitHub. Read the source before you install it.

Installation

Install the Content Atomizer 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/sanky369/vibe-building-skills/tree/main/skills/marketing/content-atomizer
  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/sanky369/vibe-building-skills.git /tmp/vibe-building-skills
mkdir -p .claude/skills
cp -r /tmp/vibe-building-skills/skills/marketing/content-atomizer .claude/skills/content-atomizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Content Atomizer 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 Content Atomizer 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 Content Atomizer 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.

Content Atomizer

Break one substantial piece of content into many platform-native pieces. Prime directive: adapt, never excerpt — each atomic piece must stand alone as if written for that platform on purpose, carrying the user's voice, while format and length obey the platform. The second rule: atomize ideas, not paragraphs — you extract the content's core ideas first, then re-express each idea per platform.

When to use / when not to

  • Use when there is existing long-form source content and the goal is distribution across channels.
  • If the user needs the long-form piece written first, use skills/marketing/seo-content — then come back.
  • If the user wants one great tweet/thread crafted and researched from scratch (not derived from source content), use skills/marketing/tweet-writer — it does niche research this skill skips.
  • If the user wants a full newsletter issue (not just an item), hand the source to skills/marketing/newsletter.

Intake

Ask in one batch, only what's missing:

  1. The seed content — the actual text/transcript (or link). Verify it's substantial enough: multiple distinct ideas and a point of view. If it's thin (one idea, <~500 words), say so and offer a single-platform adaptation instead of a pack.
  2. Active platforms — where does the user actually publish, and which 1–2 matter most?
  3. Goal — reach/followers, traffic back to the source, or list growth? (This sets the CTAs.)
  4. Voice — brand-voice guide or sample posts (use skills/marketing/brand-voice output if it exists).

Don't ask which formats they want — that's your job to propose in Step 3. If platforms are unknown, default to LinkedIn + X + newsletter item and say so. State assumptions and proceed — don't stall.

Workflow

1. Extract the idea inventory

Read the seed content and pull 5–10 discrete ideas, each tagged by type — the type determines which formats it can power:

  • Core concept — the piece's main argument
  • Framework/process — anything with steps or named parts (best carousel/thread material)
  • Story/example — narratives and cases (best LinkedIn/newsletter material)
  • Surprising insight or contrarian take — the counterintuitive bits (best hook material)
  • Actionable tip — standalone do-this-today advice (best short-video/single-post material)
  • Quotable line — sentences that sting (quote graphics, thread closers)

If you can't find at least 4 distinct ideas, the content is too thin for a full pack — tell the user and scale scope down.

2. Select formats

Pick 5–7 formats, never all 15 — quality over coverage (heuristic: a strong pack on the user's 2 priority platforms beats a thin spray across 8). Decision rules:

  • B2B / professional audience → weight LinkedIn posts + carousel; consumer/creator audience → weight short-video scripts + X.
  • Goal = traffic/list growth → include the newsletter item and give posts link-out CTAs; goal = reach → engagement CTAs, links in replies/comments.
  • Framework-type ideas present → include a carousel or thread; strong story present → include a story-post; no visual asset capacity → skip infographic/quote graphics, deliver text specs only.

Format spec table (length and structure per format):

FormatYield from one seedSpec
LinkedIn posts3–5100–200 words; hook line → story/insight → lesson → question or CTA; line breaks for scannability
X/Twitter thread1–25–10 tweets; hook tweet ≤250 chars; one idea per tweet; closer with CTA (full craft: skills/marketing/tweet-writer)
Single tweets3–5≤110 chars preferred; sharpest standalone insights
Newsletter item1300–500 words; hook → insight → lesson → link to full piece
Short-video scripts (TikTok/Reels/Shorts)2–330–60s; hook (≤3s) → problem → insight → CTA; write as spoken lines with [B-ROLL] notes
Carousel outline1–25–10 slides; slide 1 = hook, one point per slide, last slide = CTA; text per slide ≤25 words
Quote graphics (specs)3–5One pulled quote + attribution; supply exact text, no design
Instagram captions2–3100–150 words; hook first line (feed truncates), story → lesson → CTA
Slide deck outline110–15 slides: title → problem → 3–5 content → conclusion → CTA
FAQ/explainer15–10 Q&As, 40–80 word answers (feeds SEO FAQ blocks)
Case study11,000–1,500 words; situation → action → result → lesson (only if seed contains a real result)
Webinar/talk outline1Timed outline: intro → problem → solution → examples → Q&A → CTA (only on request)

3. Propose the pack, then generate

Show the user a one-screen plan first: idea inventory + chosen formats + which idea powers which piece. Get a quick confirm (or proceed if they said "just do it"). Then write every piece in full — no "you could write a post about X" stubs. While generating:

  • Rewrite hooks natively per platform; never reuse the seed's opening line verbatim across pieces.
  • Keep facts and claims within what the seed content supports — atomizing must not amplify a hedge into a certainty or invent numbers.
  • Apply the one-CTA rule per piece, CTAs set by the goal from intake.

4. Sequence the distribution

Order the pieces for posting: lead with the strongest hook on the priority platform, don't post two derivatives of the same idea on the same platform back-to-back, thread/carousel mid-pack when early pieces have tested the angle. Present as a numbered posting order (the user maps it to their own dates/scheduler — no calendar prescriptions).

Required output format

# Atomization Pack — [seed content title]

## Idea inventory
| # | Idea (one line) | Type | Powers |
| 1 | ... | framework | Carousel, LinkedIn #2 |
(5–10 rows)

## Pack plan
Formats chosen: [list] — [one-line rationale tied to platforms/goal]
Skipped: [notable exclusions + why]

## Pieces

### LinkedIn Post 1 — [idea #]
[Full post text, ready to paste]
CTA: [the one ask]

### X Thread — [idea #s]
1/ [tweet]
2/ [tweet]
...
(char count noted per tweet)

### [Every other piece, fully written, grouped by platform]

## Posting order
1. [Piece] — [platform] — [why first]
2. ...
(strongest hook first; same-idea derivatives spaced apart)

## Leftovers
[Ideas from the inventory not yet used — future seed material]

Quality bar

Check every piece before delivering:

  • Every piece is fully written and paste-ready — zero outlines-as-deliverables (except formats that are outlines: carousel, deck, webinar)
  • No two pieces share an opening line; no piece is a copy-paste excerpt of the seed
  • Each piece within its format's length spec (count characters for tweets: ≤110 single, ≤250 per thread tweet)
  • Exactly one CTA per piece, aligned with the intake goal
  • Every claim/number traceable to the seed content or the user — nothing invented in translation
  • Voice consistent across all pieces — read three at random; they must sound like the same person
  • 5–7 formats, not more; each chosen format justified by a platform the user actually uses
  • Posting order spaces out same-idea derivatives

Integration

  • skills/marketing/seo-content — primary source of seed articles; atomize each published article as a standard follow-up.
  • skills/marketing/brand-voice — supplies the voice guide every piece must pass.
  • skills/marketing/tweet-writer — takes over when the X pieces deserve dedicated niche research and hook testing; hand it the idea inventory.
  • skills/marketing/newsletter — consumes the newsletter item; strong issues flow back here as seeds.
  • skills/marketing/email-sequences — value emails can be atomized into social pieces and vice versa.

Frequently asked questions

What does the Content Atomizer AI skill do?

Turn one piece of long-form content (blog post, video/podcast transcript, guide, talk) into a ready-to-post multi-platform content pack: LinkedIn posts, X/Twitter thread, newsletter item, short-video scripts, carousel outlines, quote pulls, and more — each natively adapted, not copy-pasted. Use when the user says 'repurpose this post', 'turn this into social content', 'atomize this', 'get more mileage out of my content', 'make LinkedIn posts from my article', or shares long-form content wanting distribution. Produces an idea inventory plus a per-platform atomization pack with a suggested po...

Why use Content Atomizer on TypingMind?

Because you install it once and use it with any model. Content Atomizer 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 Content Atomizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sanky369/vibe-building-skills/tree/main/skills/marketing/content-atomizer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Content Atomizer?

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 Content Atomizer?

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

Is the Content Atomizer AI skill free?

Yes. It is published on GitHub by sanky369 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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