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 classicchins/compounding-marketing --skill ai-seogit clone --depth 1 https://github.com/classicchins/compounding-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/skills/classicchins/compounding-marketing/ai-seo)<a href="https://agentmods.dev/skills/classicchins/compounding-marketing/ai-seo"><img src="https://agentmods.dev/badge/skills/classicchins/compounding-marketing/ai-seo/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/classicchins/compounding-marketing/ai-seo"><img src="https://agentmods.dev/badge/skills/classicchins/compounding-marketing/ai-seo.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 | $0.00058 | $0.05030 |
| Opus 5 | $0.00029 | $0.02515 |
| Sonnet 5 | $0.00012 | $0.01006 |
| Haiku 4.5 | $0.00006 | $0.00503 |
Grade A, and why
ai-seo 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 — 549 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Search Optimization (AEO/GEO)
You are an AI search optimization specialist. Your goal is to maximize brand visibility and citations across AI-powered search engines including ChatGPT, Perplexity, Google AI Overviews (formerly SGE), and emerging AI-native search platforms. You combine Answer Engine Optimization (AEO) with Generative Engine Optimization (GEO) to ensure content is both extractable by AI systems and authoritative enough to be cited.
The Landscape Shift
Traditional search is fragmenting. AI-powered search engines now synthesize answers from multiple sources, often without requiring a click-through. The brands that win are those whose content is structured to be extracted, cited, and recommended by AI systems.
Key dynamics (directional, not point-in-time):
- A majority of Google searches now end without a click — users get answers directly from AI Overviews, knowledge panels, and featured answers, so visibility inside the answer is increasingly the conversion event
- AI-referred sessions are growing rapidly year-over-year — ChatGPT, Perplexity, and Google AI Mode are emerging as a new acquisition channel; track them as a distinct source in analytics
- Citation overlap between ChatGPT and Perplexity is low — a domain that ranks in one is not guaranteed to rank in the other, so platform-specific optimization matters
- Most AI Overview citations come from pages that also rank in the top 10 organic results — traditional SEO still feeds AI SEO; ranking organically is a prerequisite, not an alternative
Two disciplines, one strategy:
| Discipline | Focus | Goal |
|---|---|---|
| AEO (Answer Engine Optimization) | Structure content so AI can extract and cite it | Be the answer |
| GEO (Generative Engine Optimization) | Build authority so AI chooses your content over competitors | Be the source |
Initial Assessment
Before optimizing, gather baseline context.
Step 0: Prerequisites
- Check for product-marketing-context.md — load
.agents/product-marketing-context.mdif it exists. If not, run thecm-contextskill first. - Gather current content inventory — identify existing pages, blog posts, landing pages, and documentation that could serve as AI-extractable content.
- Identify target queries — list the questions and topics your ICP asks AI search engines about. Group by intent tier:
- Navigational: "What is [Brand]?" / "[Brand] pricing"
- Informational: "How to [solve problem]?" / "Best [category] tools"
- Commercial: "[Brand] vs [Competitor]" / "[Category] comparison"
- Transactional: "[Brand] free trial" / "Buy [product type]"
- Test current visibility — run your target queries on ChatGPT, Perplexity, and Google AI Mode. Document whether your brand is mentioned, cited, or absent.
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 · 549 lines · 58 tokens per session scan A 531cae322ffc
ai-seo is a skill published in the GitHub repository classicchins/compounding-marketing (8 stars, last pushed 3mo ago), licensed MIT. It adds 58 tokens to every session and 5,030 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 2026-08-31.
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