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 agentmods add skills/moizibnyousaf/marketing-cli/content-atomizernpx skills add MoizIbnYousaf/marketing-cli --skill content-atomizergit clone --depth 1 https://github.com/MoizIbnYousaf/marketing-cliWrote 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/moizibnyousaf/marketing-cli/content-atomizer)<a href="https://agentmods.dev/skills/moizibnyousaf/marketing-cli/content-atomizer"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/marketing-cli/content-atomizer.svg" alt="Measured on agentmods" 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 | $0.00180 | $0.03300 |
| Opus 5 | $0.00090 | $0.01650 |
| Sonnet 5 | $0.00036 | $0.00660 |
| Haiku 4.5 | $0.00018 | $0.00330 |
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 5d 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 — 322 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Atomizer
You take one piece of long-form content and extract 10-20 standalone social posts, each native to its target platform. Not lazy copy-paste with different character counts — genuine reformatting that makes each post feel like it was written platform-first.
On Activation
- Read
brand/voice-profile.md— maintain consistent voice across platforms - Read
brand/audience.md— know which platforms matter and how the audience behaves on each - Load
references/platform-specs.mdfor the quick reference table, then load individual platform files fromreferences/platforms/as needed (linkedin.md, twitter.md, instagram.md, tiktok.md, youtube.md, threads.md, bluesky.md, reddit.md) - Accept the source content (blog post, newsletter, video transcript, podcast transcript)
- If no source provided, check
marketing/content/for recent articles
Zero Context (No Brand Files)
If brand files don't exist, the skill still works:
- No voice-profile.md: Write in a clear, professional default voice. Ask the user for 2-3 adjectives describing their brand tone.
- No audience.md: Default to all platforms. Ask which platforms matter most to them.
- The skill always works — brand files enhance, never gate.
Brand Integration
Brand files shape how content gets adapted per platform:
- voice-profile.md → Each platform gets the same voice in different registers. Twitter gets punchy voice (short, sharp). LinkedIn gets authoritative voice (complete thoughts, data). Reddit gets authentic voice (conversational, no marketing speak). The voice DNA stays constant; the register shifts.
- audience.md → Watering holes from the audience profile determine which platforms to prioritize. If your audience lives on Twitter and Reddit, don't waste time on Instagram carousels.
The Atomization Process
Step 1: Extract Raw Material
Read the source content and extract:
- Key insights: 3-5 original ideas or arguments
- Quotable lines: Sentences that stand alone as wisdom
- Stats/data points: Any numbers, percentages, results
- Stories/anecdotes: Narrative moments that create emotional connection
- Contrarian takes: Opinions that challenge conventional wisdom
- Step-by-step processes: Any how-to sequences
- Lists: Any grouped items (tools, tips, mistakes, etc.)
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/platform-specs.md 5.8 KB
- references/platforms/bluesky.md 2.2 KB
- references/platforms/instagram.md 2.5 KB
- references/platforms/linkedin.md 2.7 KB
- references/platforms/reddit.md 3.2 KB
- references/platforms/threads.md 2.2 KB
- references/platforms/tiktok.md 2.2 KB
- references/platforms/twitter.md 2.4 KB
- references/platforms/youtube.md 2.5 KB
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.
- 5d ago First seen · 322 lines · 180 tokens per session scan A d8b2057c65c6
content-atomizer is a skill published in the GitHub repository MoizIbnYousaf/marketing-cli (31 stars, last pushed 19d ago), licensed MIT. It adds 180 tokens to every session and 3,300 once invoked, about $0.0009 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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