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atomize

Skill0 starsChanged 9 months agoMITView on GitHub

Turn a blog post or long-form content into social media variations. Reads config.json for platforms, voice, and audience. /atomize <file-path-or-URL> The source can be: - A markdown file path (e.g., content/blog/my-post.md) - A URL to a published post Track progress through the content-pipeline.md entry status: On failure at step 5-6: Posts are already generated and shown. Just re-run the file writes. Read the full content. Extract: - Title and slug (from frontmatter if available) - Key stats (numbers, percentages, dollar…

2 other projects use a version of this.

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Most recently taken up by

  • Other versions2 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 2

    1. BayramAnnakov/ai-personal-os-skills★ 19same name
    2. sergeeey/Claude-cod-top-2026★ 9same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository5 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

8 rules. About 0 are common, 3 are distinctive, and 5 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read config.json for business name, voice, audience, and platforms

    line 15
  2. Very specific:

    Read data/portfolio/content-pipeline.md to understand existing content and avoid duplicates

    line 17
  3. Distinctive:

    Read the full content. Extract:

    line 35
  4. Distinctive:

    No hashtags unless they genuinely add value

    line 59
  5. Very specific:

    NEVER use .md filenames raw — X auto-links them.

    line 61
  6. Distinctive:

    Always include the source link.

    line 87
  7. Very specific:

    Add entries to data/portfolio/content-pipeline.md for each platform:

    line 105
  8. Very specific:

    Create the data/knowledge/social/ directory if it doesn't exist.

    line 144

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
skill.md

Content Atomizer

Turn a blog post or long-form content into social media variations. Reads config.json for platforms, voice, and audience.

Input

/atomize <file-path-or-URL>

The source can be:

  • A markdown file path (e.g., content/blog/my-post.md)
  • A URL to a published post

Before Starting

  1. Read config.json for business name, voice, audience, and platforms
  2. Fetch/read the source content
  3. Read data/portfolio/content-pipeline.md to understand existing content and avoid duplicates

Checkpointing

Track progress through the content-pipeline.md entry status:

StepCheckpoint
1Source content read and extracted
2-3Posts generated (in memory)
4Presented to user
5content-pipeline.md updated
6File saved to data/knowledge/social/

On failure at step 5-6: Posts are already generated and shown. Just re-run the file writes.

Step 1: Read the Source

Read the full content. Extract:

  • Title and slug (from frontmatter if available)
  • Key stats (numbers, percentages, dollar amounts)
  • Opinions the author actually holds (not generic observations)
  • Insights the audience would find interesting
  • Anything surprising or counterintuitive
  • Honest caveats — what's overhyped, what's not ready

Step 2: Generate Posts by Platform

For each platform configured in config.json, generate posts using the voice setting.

X/Twitter (if configured)

Post count: 5-8 variations, each under 280 characters (HARD LIMIT)

Voice calibration by config.json voice:

  • casual → First person, lowercase energy, opinions stated directly
  • friendly → Warm, approachable, uses "you", asks questions
  • professional → Clear, authoritative, data-driven
  • technical → Precise, jargon-appropriate, practitioner-to-practitioner

Platform rules:

  • Max 280 characters (count carefully)
  • No hashtags unless they genuinely add value
  • Max 1-2 posts with the source link. The rest are standalone.
  • NEVER use .md filenames raw — X auto-links them. Write "the SKILL file format" instead of SKILL.md.

Post variety requirements:

  • At least 1 post grounded in first-person experience or honest assessment
  • At least 1 post that reveals something non-obvious or counterintuitive
  • At least 1 post that invites the reader to DO or CHECK something
  • At least 1 post with a clear verdict (works / doesn't / overhyped)

Kill any post that:

  • Sounds like it came from a social media scheduler
  • Uses "The X just Y'd" dramatic framing
  • Could be posted by any generic account (not specific to what this content actually says)
  • Makes claims the author can't verify

Facebook (if configured)

Post count: 3-5 variations, 100-300 words each

Voice: Adapt from config.json but always more conversational than X. Assume the audience may not be technical. Use real-world analogies.

Post types:

  1. Explainer — What the content is about in plain English
  2. Lesson — One specific business lesson, expanded
  3. Listicle — "3 things we learned:" with bullet points
  4. Question — Ask something that invites comments

Always include the source link.

LinkedIn (if configured)

Post count: 2-3 variations, professional tone, up to 3000 chars

Voice: Lead with insight, end with takeaway. More formal than X or Facebook.

Other platforms

For any other configured platform, generate 3-5 posts adapting the voice and format to what's standard for that platform.

Step 3: Output to User

Present content organized by platform. Include character counts for X posts. Flag any over 280.

Step 4: Update Content Pipeline

Add entries to data/portfolio/content-pipeline.md for each platform:

### [Title] — Atomized Posts
- **Stage:** Draft
- **Priority:** High
- **Source:** Atomized from [source]
- **Notes:** [N] posts generated [date]. Ready for manual posting.

Step 5: Save the Posts

Write to data/knowledge/social/<slug>-atomized.md:

# Social Posts: [Title]

*Atomized: [date]*
*Source: [source URL or path]*

---

## X Posts

[all X posts with character counts]

---

## Facebook Posts

[all Facebook posts]

---

## [Other Platform] Posts

[posts]

Create the data/knowledge/social/ directory if it doesn't exist.

Principles

  1. Authenticity over reach — One genuine post beats five polished ones
  2. Platform-native voice — X audience ≠ Facebook audience ≠ LinkedIn audience. Never cross-contaminate.
  3. Standalone value — Each post should work without reading the original
  4. No hype — If the content has caveats, the social posts should too
  5. Avoid .md auto-linking — X renders SKILL.md, CLAUDE.md etc. as clickable links to random domains

ARGUMENTS: source

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