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 nocodework/growth-os --skill geo-contentgit clone --depth 1 https://github.com/nocodework/growth-osWrote 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/nocodework/growth-os/geo-content)<a href="https://agentmods.dev/skills/nocodework/growth-os/geo-content"><img src="https://agentmods.dev/badge/skills/nocodework/growth-os/geo-content/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/nocodework/growth-os/geo-content"><img src="https://agentmods.dev/badge/skills/nocodework/growth-os/geo-content.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 | $0.00111 | $0.01289 |
| Opus 5 | $0.00056 | $0.00645 |
| Sonnet 5 | $0.00022 | $0.00258 |
| Haiku 4.5 | $0.00011 | $0.00129 |
Grade A, and why
geo-content 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 10d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
geo-content
geo-audit tells you whether AI assistants cite you. geo-content is how you earn more of those citations — by restructuring content so an assistant can lift a clean, self-contained, factual answer straight from your page. It's not a growth hack; it's disciplined information design that happens to also help human readers and traditional SEO.
What it does
Takes a page (or a content plan) and applies a concrete checklist that makes it more extractable and quotable, then adds the two machine-readable affordances assistants and their crawlers look for:
- A 9-pattern citability pass on the content itself.
- An llms.txt file describing the site for AI crawlers.
- FAQPage / structured data where the content is genuinely Q&A shaped.
When to use
- After
geo-auditsurfaces queries where the brand is invisible or losing citations to competitors. - When writing or rewriting a cornerstone page that should become the quotable answer for a topic.
- As the content-side follow-through on a growth audit finding.
The 9 citability patterns
Work through each. They compound — a page that does all nine is dramatically easier for an assistant to quote confidently.
- Answer-first. Put the direct answer in the first sentence or two under the heading, before context or story. Assistants extract the top of a section; bury the answer and it gets skipped.
- Descriptive H2/H3. Headings that state the question or the claim ("How much does X cost?" / "X reduces onboarding time by 40%"), not clever labels. The heading is the retrieval hook.
- Standalone sections. Each section should make sense lifted out of the page with no surrounding context. No "as mentioned above," no dangling pronouns referring to earlier sections.
- Tables for structured comparisons. Pricing, feature comparisons, specs — put them in real HTML tables. Assistants parse and reproduce tables cleanly.
- Lists for steps and enumerations. Ordered lists for processes, unordered for sets. Extractable, scannable, quotable as-is.
- Fact density. Concrete numbers, dates, named specifics over adjectives. "Ships in 3 business days" beats "fast shipping." Facts are what gets quoted; vibes get paraphrased away or dropped.
- Entity naming. Name the product, company, people, and category explicitly and consistently — don't rely on "we," "our platform," "it." Assistants attribute to named entities; unnamed subjects lose the citation.
- Clean semantic HTML. Proper heading hierarchy, real
<table>/<ul>/<ol>, no critical content trapped in images or rendered only by client-side JS an crawler won't run. If it isn't in the served HTML, it can't be cited. - Freshness signals. Visible published/updated dates and current figures. Assistants prefer sources that look maintained.
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.
- 10d ago First seen · 66 lines · 111 tokens per session scan A 55518e675281
geo-content is a skill published in the GitHub repository nocodework/growth-os (6 stars, last pushed 2mo ago), licensed MIT. It adds 111 tokens to every session and 1,289 once invoked, about $0.0006 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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