Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/robroyhobbs/marketing-skillsnpx agentmods add skills/robroyhobbs/marketing-skills/content-atomizerWrote 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/robroyhobbs/marketing-skills/content-atomizer)<a href="https://agentmods.dev/skills/robroyhobbs/marketing-skills/content-atomizer"><img src="https://agentmods.dev/badge/skills/robroyhobbs/marketing-skills/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/robroyhobbs/marketing-skills/content-atomizer"><img src="https://agentmods.dev/badge/skills/robroyhobbs/marketing-skills/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 | $0.00273 | $0.13172 |
| Opus 5 | $0.00137 | $0.06586 |
| Sonnet 5 | $0.00055 | $0.02634 |
| Haiku 4.5 | $0.00027 | $0.01317 |
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 — 1,716 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Atomizer Skill
One piece of content should become ten. The best creators don't create more—they distribute better.
This skill transforms any source content into platform-optimized assets. Not generic repurposing. Platform-native content that performs.
The math: A single blog post can become 1 LinkedIn carousel + 2 LinkedIn text posts + 1 Twitter thread + 3 single tweets + 2 Instagram carousels + 1 Reel script + 2 TikTok scripts + 1 YouTube Short script + 1 Threads mini-thread + 1 Bluesky post + 1 Reddit value post = 16 pieces of content from one source.
Read ./brand/ per _system/brand-memory.md
Follow all output formatting rules from _system/output-format.md
Brand Memory Integration
This skill reads brand context to ensure every atomized piece sounds like the user's brand, adapts tone per platform, and builds on what has worked before. It also checks the learnings journal for platform performance data and the stack file for scheduling tool availability.
Reads: voice-profile.md, creative-kit.md, learnings.md, stack.md (all optional)
On invocation, check for ./brand/ and load available context:
-
Load
voice-profile.md(if exists):- Extract tone DNA, vocabulary, sentence patterns, and formality level
- Apply the platform adaptation table (see below) to adjust voice per platform
- A "direct, proof-heavy" voice sounds different on LinkedIn vs TikTok vs Reddit
- Use vocabulary lists to stay on-brand: preferred words, banned words, signature phrases
- Show: "Your voice is [tone summary]. Adapting for each platform."
-
Load
creative-kit.md(if exists):- Use brand colors, fonts, and visual identity for carousel slide direction
- Reference logo placement and design system for visual content notes
- Show: "Creative kit loaded. Visual notes will reference your brand system."
-
Load
learnings.md(if exists):- Check for platform-specific performance data (e.g., "long-form LinkedIn posts outperform short ones")
- Check for hook patterns that have worked or failed on specific platforms
- Check for optimal posting times per platform
- Check for format preferences (carousels vs text, threads vs single tweets)
- Show: "Found [N] platform learnings. Applying: [key insight]."
What ships with it
1 file 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.
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 · 1,716 lines · 273 tokens per session scan A 486af45d89a3
content-atomizer is a skill published in the GitHub repository robroyhobbs/marketing-skills (5 stars, last pushed 5mo ago), licensed MIT. It adds 273 tokens to every session and 13,172 once invoked, about $0.0014 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 2026-08-31.
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