Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Salesably/salesably-marketplace --skill content-atomizergit clone --depth 1 https://github.com/Salesably/salesably-marketplaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/salesably/salesably-marketplace/content-atomizer)<a href="https://agentmods.dev/skills/salesably/salesably-marketplace/content-atomizer"><img src="https://agentmods.dev/badge/skills/salesably/salesably-marketplace/content-atomizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/salesably/salesably-marketplace/content-atomizer"><img src="https://agentmods.dev/badge/skills/salesably/salesably-marketplace/content-atomizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.02673 |
| Opus 5 | $0.00028 | $0.01337 |
| Sonnet 5 | $0.00011 | $0.00535 |
| Haiku 4.5 | $0.00006 | $0.00267 |
Grade A, and why
content-atomizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Atomizer
This skill extracts maximum value from every piece of content by strategically repurposing it across formats and platforms - without losing quality or sounding repetitive.
Objective
Transform one substantial piece of content into many derivative pieces, each optimized for its destination platform while maintaining consistent brand voice and messaging.
Intake Questions
Before atomizing content, gather context:
- Source content: What is the original piece? (Blog post, video, podcast, webinar, report)
- Target platforms: Where should the atomized content go? (LinkedIn, Twitter/X, Instagram, YouTube, email, etc.)
- Audience overlap: Are the same people on multiple platforms or different segments?
- Brand voice: What's the established voice? (Reference
brand-voiceskill) - Goals per platform: What action do you want on each? (Awareness, clicks, engagement, follows)
- Content calendar: What's the publishing cadence per platform?
- Evergreen vs. timely: Does this content have a shelf life?
The Atomization Matrix
Transform source content into these derivative formats:
From a Blog Post / Article
| Derivative Format | What to Extract | Platform |
|---|---|---|
| Twitter/X thread | Main points as numbered list | Twitter/X |
| LinkedIn post | Key insight + personal take | |
| Instagram carousel | Tips/steps as slides | |
| Email newsletter section | Summary + link | |
| Quote graphics | Quotable lines | All social |
| YouTube Short / Reel | One tip explained | YouTube, IG, TikTok |
| Podcast talking points | Discussion outline | Podcast |
| Infographic | Data/process visualization | Pinterest, LinkedIn |
| Slide deck | Key points as slides | SlideShare, LinkedIn |
| FAQ content | Questions answered in post | Website, help docs |
From a Video / Webinar
| Derivative Format | What to Extract | Platform |
|---|---|---|
| Blog post | Full transcript, edited | Website |
| Short clips (60-90s) | Key moments | YouTube Shorts, Reels, TikTok |
| Audiogram | Audio + waveform visual | Social media |
| Quote graphics | Best soundbites | All social |
| Twitter/X thread | Main points summarized | Twitter/X |
| Email recap | Highlights + replay link | |
| LinkedIn article | Expanded written version | |
| Slide deck | Presentation slides reused | SlideShare |
| GIF moments | Reactions, demonstrations | Social, website |
| Transcript PDF | Lead magnet or resource | Website |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 279 lines · 57 tokens per session scan A f298d4d164aa
content-atomizer is a skill published in the GitHub repository Salesably/salesably-marketplace (49 stars, last pushed 8mo ago), licensed MIT. It adds 57 tokens to every session and 2,673 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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