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 agentmods add skills/lonormaly/builders-stack/ai-seonpx skills add lonormaly/builders-stack --skill ai-seogit clone --depth 1 https://github.com/lonormaly/builders-stackWrote 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/lonormaly/builders-stack/ai-seo)<a href="https://agentmods.dev/skills/lonormaly/builders-stack/ai-seo"><img src="https://agentmods.dev/badge/skills/lonormaly/builders-stack/ai-seo.svg" alt="Measured on agentmods" 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 | $0.00191 | $0.06034 |
| Opus 5 | $0.00096 | $0.03017 |
| Sonnet 5 | $0.00038 | $0.01207 |
| Haiku 4.5 | $0.00019 | $0.00603 |
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 4d 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
84% identical to ai-seo — 14 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 — 493 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.
Before Starting
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, 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
7 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.
- 4d ago First seen · 493 lines · 191 tokens per session scan A 009333fb0cd4
ai-seo is a skill published in the GitHub repository lonormaly/builders-stack (41 stars, last pushed 18d ago), licensed MIT. It adds 191 tokens to every session and 6,034 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to ai-seo, differing in 14 lines, and is treated as a copy.
Other skills, from other repositories
openspec-explore
Enter explore mode - a thinking partner for exploring ideas, investigating problems, and clarifying requirements. Use when the user wants to think through something before or during a change.
openspec-propose
Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.
openspec-archive-change
Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.
openspec-sync-specs
Sync delta specs from a change to main specs. Use when the user wants to update main specs with changes from a delta spec, without archiving the change.
scaffold-project
Scaffold a new app, API, backend, fullstack project, monorepo, or starter with Better-T-Stack — including new projects built on a specific framework like Hono, Express, Fastify, Elysia, Next.js, TanStack Router/Start, Nuxt, Svelte, Solid, Astro, or React Native (native-bare, native-uniwind, native-unistyles). Use…
add-to-project
Add addons or features (PWA, Tauri, Starlight/Fumadocs docs, Biome/Oxlint, Husky/Lefthook, Turborepo/Nx, the MCP addon, etc.) to an existing Better-T-Stack project. Use when the user wants to extend, enhance, or add tooling to a project that was created with Better-T-Stack.