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.
git clone --depth 1 https://github.com/thatrebeccarae/claude-marketingWrote 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/rules/thatrebeccarae/claude-marketing/aeo-geo-optimizer)<a href="https://agentmods.dev/rules/thatrebeccarae/claude-marketing/aeo-geo-optimizer"><img src="https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/aeo-geo-optimizer/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/rules/thatrebeccarae/claude-marketing/aeo-geo-optimizer"><img src="https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/aeo-geo-optimizer.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 | $0.00086 | $0.03342 |
| Opus 5 | $0.00043 | $0.01671 |
| Sonnet 5 | $0.00017 | $0.00668 |
| Haiku 4.5 | $0.00009 | $0.00334 |
Grade A, and why
aeo-geo-optimizer 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 9d 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 — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AEO/GEO Optimizer
Optimize content and websites for AI-powered search experiences — ChatGPT, Perplexity, Claude, Google AI Overviews, and Bing Copilot.
Why This Matters
Traditional SEO optimizes for 10 blue links. AEO/GEO optimizes for AI-generated answers. When someone asks ChatGPT or Perplexity a question, the answer synthesizes from sources — and those sources get cited, linked, and trusted. If your content is not structured for AI consumption, you are invisible in the fastest-growing search channel.
Core Concepts
AEO vs GEO vs Traditional SEO
| Dimension | Traditional SEO | AEO (Answer Engine) | GEO (Generative Engine) |
|---|---|---|---|
| Target | Google/Bing SERPs | Featured snippets, AI Overviews, voice assistants | ChatGPT, Perplexity, Claude citations |
| Goal | Rank on page 1 | Be THE answer | Be cited in AI-generated responses |
| Content format | Long-form, keyword-rich | Concise, structured Q&A | Authoritative, quotable, fact-dense |
| Signals | Backlinks, keywords, UX | Schema markup, direct answers, authority | E-E-A-T, data density, citation-worthiness |
| Measurement | Rankings, traffic | Answer box appearance, voice search hits | AI citation tracking, brand mentions in AI |
The Citation Hierarchy
AI models prioritize sources based on:
- Authority signals — Domain authority, author expertise, institutional backing
- Content structure — Clear headings, direct answers, structured data
- Freshness — Recent publication dates, updated statistics
- Specificity — Exact numbers, named sources, verifiable claims
- Uniqueness — Original research, proprietary data, novel frameworks
AEO/GEO Audit Workflow
Step 1: Assess Current AI Visibility
- Test AI citation presence: Query ChatGPT, Perplexity, and Google AI Overviews with questions your content should answer. Document which queries cite your content vs competitors.
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.
- 9d ago First seen · 338 lines · 86 tokens per session scan A 9c28ce0c6828
aeo-geo-optimizer is a cursor rule published in the GitHub repository thatrebeccarae/claude-marketing (133 stars, last pushed 3mo ago), licensed MIT. It adds 86 tokens to every session and 3,342 once invoked, about $0.0004 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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