ai-seo

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# Agent Readiness — Can an Agent Reach, Navigate, and Parse Your Site?

AI visibility work splits into two layers: what your content says (the rest of this skill) and whether an agent can *get to it at all*. This reference covers the second layer — the access/discovery/parseability audit — plus the emerging shift from agent-*readable* to agent-*actionable* sites.

Two free scoring tools shipped in August 2026 and turned this into a measurable discipline:

| Tool | Run it | Method |
|---|---|---|
| **Is Agentic** (Vercel + Ora) | `npx is-agentic yourdomain.com` or [is-agentic.com](https://is-agentic.com) | 100+ checks; Essential checks carry most of the score; Recommended checks activate only when evidence shows you have that surface (API, MCP server, commerce); not-applicable checks are excluded, not failed; includes an observed agent journey showing where a real agent hit friction |
| **Frase Agent Readiness Checker** | [frase.io/tools/agent-readiness](https://www.frase.io/tools/agent-readiness) | Access / Discovery / Parseability triad; 80+ = agents can reliably use the site, 60–79 = solid with gaps, <60 = real access problems |

Run one before and after any agent-readiness work 

# Citations vs. Recommendations: The AI Visibility Ladder

Being cited by an AI engine and being recommended by it are **two different outcomes governed by two different systems**. A citation means your page was useful enough to pull information from. A recommendation means the model put your brand on the buyer's shortlist. Optimizing for the first does not automatically earn the second — and for smaller brands, conflating them leads to content strategies that can actively help competitors.

Source note: the analysis and data in this reference draw on Lily Ray's (Amsive) 2026 study of B2B "best [category] software" queries, behavioral studies by Scrunch and SimilarWeb, and commentary by John-Henry Scherck (Growth Plays).

---

## The Visibility Ladder

AI visibility is a ladder, not a binary. Each rung has different selection criteria and different measurement:

| Rung | What it means | What governs it | How to see it |
|---|---|---|---|
| **1. Retrieved** | The model read your content while building its answer, without citing it | Crawlability, parseable structure, query relevance | Mostly invisible; bot logs hint at it |
| **2. Cited** | Your page appears as a source in the answe

# AEO and GEO Content Patterns

Reusable content block patterns optimized for answer engines and AI citation.

---

## Contents
- Answer Engine Optimization (AEO) Patterns (Definition Block, Step-by-Step Block, Comparison Table Block, Pros and Cons Block, FAQ Block, Listicle Block)
- Generative Engine Optimization (GEO) Patterns (Statistic Citation Block, Expert Quote Block, Authoritative Claim Block, Self-Contained Answer Block, Evidence Sandwich Block)
- Domain-Specific GEO Tactics (Technology Content, Health/Medical Content, Financial Content, Legal Content, Business/Marketing Content)
- Voice Search Optimization (Question Formats for Voice, Voice-Optimized Answer Structure)

## Answer Engine Optimization (AEO) Patterns

These patterns help content appear in featured snippets, AI Overviews, voice search results, and answer boxes.

### Definition Block

Use for "What is [X]?" queries.

```markdown
## What is [Term]?

[Term] is [concise 1-sentence definition]. [Expanded 1-2 sentence explanation with key characteristics]. [Brief context on why it matters or how it's used].
```

**Example:**
```markdown
## What is Answer Engine Optimization?

Answer Engine Optimization (AEO) is the 

# AI SEO by Content Type

Tactical guidance for optimizing specific content types for AI search citation. These tactics work for non-Google AI engines (ChatGPT, Claude, Perplexity, Copilot) and don't hurt Google AI Overviews / AI Mode.

For the cross-cutting strategy, see [SKILL.md](../SKILL.md).

---

## SaaS Product Pages

**Goal:** Get cited in "What is [category]?" and "Best [category]" queries. (Citation is the realistic goal here; being *recommended* in the answer depends on offsite consensus — see [citations-vs-recommendations.md](citations-vs-recommendations.md).)

**Optimize:**
- Clear product description in first paragraph (what it does, who it's for)
- Feature comparison tables (you vs. category, not just competitors)
- Specific metrics ("processes 10,000 transactions/sec" not "blazing fast")
- Customer count or social proof with numbers
- Pricing transparency (AI cites pages with visible pricing) — add a `/pricing.md` file so AI agents can parse your plans without rendering your page (see "Machine-Readable Files" in the main skill)
- FAQ section addressing common buyer questions

---

## Blog Content

**Goal:** Get cited as an authoritative source on topics in your spac

# Open Knowledge Format (OKF)

Google's v0.1 markdown spec for representing site content as an agent-readable bundle. Introduced on the [Google Cloud blog](https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing) on 2026-06-12 and shipped inside Knowledge Catalog.

## What it is

OKF is a directory of cross-linked markdown files. Each file has:

- A YAML frontmatter block (`type` required; `title`, `description`, `resource`, `tags`, `timestamp` recommended)
- A standard markdown body
- Standard markdown links to other files in the bundle (which the spec treats as concept relationships)

An optional `index.md` lists the files for progressive disclosure. The bundle can be distributed as a git repo (recommended), a tarball/zip, or a subdirectory of a larger repo.

The [full spec](https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/HEAD/okf/SPEC.md) fits on one page. The repo lives under `GoogleCloudPlatform` (the "not an official Google product" disclaimer is Google's standard open-source boilerplate, not a denial — it appears on most of Google's open-source repos including their main AI samples repo).

### A minimal conce

# How Each AI Platform Picks Sources

Each AI search platform has its own search index, ranking logic, and content preferences. This guide covers what matters for getting cited on each one.

Sources cited throughout: Princeton GEO study (KDD 2024), SE Ranking domain authority study, ZipTie content-answer fit analysis.

---

## The Fundamentals

Every AI platform shares three baseline requirements:

1. **Your content must be in their index** — Each platform uses a different search backend (Google, Bing, Brave, or their own). If you're not indexed, you can't be cited.
2. **Your content must be crawlable** — AI bots need access via robots.txt. Block the bot, lose the citation.
3. **Your content must be extractable** — AI systems pull passages, not pages. Clear structure and self-contained paragraphs win.

Beyond these basics, each platform weights different signals. Here's what matters and where.

---

## Google AI Overviews

Google AI Overviews pull from Google's own index and lean heavily on E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness). They appear in roughly 45% of Google searches.

**What makes Google AI Overviews different:** They already have your 

# YouTube Videos That Get Cited by AI

YouTube is one of the most-cited third-party surfaces in AI answers — Google AI Overviews and Gemini cite it heavily, and ChatGPT/Perplexity lift from it for how-to queries. The core insight that changes how you produce for it:

**Models don't watch your video. They read everything around it.** The citation is earned by the text layer — title, transcript, captions, chapters, description, and comments — not the footage. A mediocre-looking video with a clean, structured text layer beats a beautiful one that's opaque to a crawler.

## The anatomy

Work through these in order of leverage:

### 1. The transcript (the real content)

This is what the model actually reads. Optimize the *spoken words*:

- **Answer questions in complete, liftable sentences.** "The five steps to create an SOP are…" extracts cleanly; a rambling answer spread across three tangents doesn't.
- Script or outline the key answers before recording so each core question gets a clear, structured spoken answer in one place.
- Say the important terms out loud — the product name, the category, the entities you want associated. If it's only on a slide, the model may never see it.

###

---
name: ai-seo
description: "When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'llms-full.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' 'agent-readable site,' 'agent readiness,' 'is my site agent-ready,' or 'WebMCP.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema."
metadata:
  version: 2.4.0
---

# 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 i