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/rajitsaha/100xprismWrote 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/rajitsaha/100xprism/ai-seo)<a href="https://agentmods.dev/rules/rajitsaha/100xprism/ai-seo"><img src="https://agentmods.dev/badge/rules/rajitsaha/100xprism/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/rules/rajitsaha/100xprism/ai-seo"><img src="https://agentmods.dev/badge/rules/rajitsaha/100xprism/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 | $0.03661 | $0.03661 |
| Opus 5.5 | $0.01464 | $0.01464 |
| Sonnet 5.5 | $0.00732 | $0.00732 |
| Haiku 4.5 | $0.00366 | $0.00366 |
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 1mo 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 — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI SEO
Make content discoverable, extractable, and citable by AI systems — Google AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and Copilot — so it gets cited as a source in AI-generated answers.
Before Starting
Product context: If .agents/product-marketing-context.md exists (or .claude/product-marketing-context.md in older setups), read it first and tailor output to it; only ask for what it doesn't cover.
Gather this context (ask if not provided):
- Current AI visibility — Does your brand appear in AI-generated answers today? Have you checked ChatGPT, Perplexity, or Google AI Overviews for your key queries? Which queries matter most to your business?
- Content & domain — Content types produced (blog, docs, comparisons, product pages)? Domain authority / traditional SEO strength? Existing structured data (schema markup)?
- 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?
- Competitive landscape — Top competitors in AI search results? Are they 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 |
For per-platform source selection and optimization, see references/platform-ranking-factors.md.
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.
- 1mo ago First seen · 374 lines · 3,661 tokens per session scan A 667e549b4bc1
ai-seo is a cursor rule published in the GitHub repository rajitsaha/100xprism (10 stars, last pushed 4d ago), licensed MIT. It adds 3,661 tokens to every session, about $0.0146 per session on Opus 5.5. 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 2026-09-03.
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