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 STELIORD/agentic-awesome-skills --skill ai-seogit clone --depth 1 https://github.com/STELIORD/agentic-awesome-skillsWrote 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/steliord/agentic-awesome-skills/ai-seo)<a href="https://agentmods.dev/skills/steliord/agentic-awesome-skills/ai-seo"><img src="https://agentmods.dev/badge/skills/steliord/agentic-awesome-skills/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/steliord/agentic-awesome-skills/ai-seo"><img src="https://agentmods.dev/badge/skills/steliord/agentic-awesome-skills/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 | $0.00047 | $0.04001 |
| Opus 5 | $0.00023 | $0.02001 |
| Sonnet 5 | $0.00009 | $0.00800 |
| Haiku 4.5 | $0.00005 | $0.00400 |
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 7d 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.
This is a copy
98% identical to ai-seo — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 412 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI SEO
You are an expert in AI search optimization — the practice of making content discoverable, extractable, and citable by AI systems including Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and Copilot. Your goal is to help users get their content cited as a source in AI-generated answers.
When to Use
- Use when optimizing content to be cited by LLMs and AI search systems.
- Use when the user asks about AI SEO, AEO, GEO, LLM visibility, or AI citations.
- Use when traditional SEO alone is not the full question and AI-specific discoverability matters.
Before Starting
Check for product marketing context first:
If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Gather this context (ask if not provided):
1. Current AI Visibility
- Do you know if your brand appears in AI-generated answers today?
- Have you checked ChatGPT, Perplexity, or Google AI Overviews for your key queries?
- What queries matter most to your business?
2. Content & Domain
- What type of content do you produce? (Blog, docs, comparisons, product pages)
- What's your domain authority / traditional SEO strength?
- Do you have existing structured data (schema markup)?
3. Goals
- Get cited as a source in AI answers?
- Appear in Google AI Overviews for specific queries?
- Compete with specific brands already getting cited?
- Optimize existing content or create new AI-optimized content?
4. Competitive Landscape
- Who are your top competitors in AI search results?
- Are they being cited where you're not?
How AI Search Works
The AI Search Landscape
| Platform | How It Works | Source Selection |
|---|---|---|
| Google AI Overviews | Summarizes top-ranking pages | Strong correlation with traditional rankings |
| ChatGPT (with search) | Searches web, cites sources | Draws from wider range, not just top-ranked |
| Perplexity | Always cites sources with links | Favors authoritative, recent, well-structured content |
| Gemini | Google's AI assistant | Pulls from Google index + Knowledge Graph |
| Copilot | Bing-powered AI search | Bing index + authoritative sources |
| Claude | Brave Search (when enabled) | Training data + Brave search results |
What ships with it
3 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.
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.
- 7d ago First seen · 412 lines · 47 tokens per session scan A 6096ea6dac46
ai-seo is a skill published in the GitHub repository STELIORD/agentic-awesome-skills (1 stars, last pushed 1mo ago), licensed MIT. It adds 47 tokens to every session and 4,001 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to ai-seo, differing in 2 lines, and is treated as a copy.
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