AI SEO

What This Skill Does

• This skill helps optimize content for AI search platforms like Chat GPT and Google AI Overviews.
• It focuses on making content easily extractable and citable by AI systems.
• It's designed for content marketers and SEO professionals who want to improve their content's visibility in the age of AI search.

When to Use

• Auditing existing content for AI visibility.
• Optimizing content to be easily extracted by AI.
• Identifying target queries for AI search.
• Monitoring AI citations over time.
• Restructuring content to match AI extraction patterns.
• Improving content's chances of being cited by AI assistants.

Key Features

• Performs AI visibility audits to identify areas for improvement.
• Optimizes content structure and enhances content for AI extraction.
• Monitors AI citations to track performance.
• Helps users understand how AI search differs from traditional SEO.
• Identifies target queries to focus optimization efforts.
• Leverages marketing context for better results.

Manual Installation

Manual installation## Before Starting

Check for context first: If marketing-context.md exists, read it. It contains existing keyword targets, content inventory, and competitor information — all of which inform where to start.

Gather what you need:

What you need

URL or content to audit — specific page, or a topic area to assess Target queries — what questions do you want AI systems to answer using your content? Current visibility — are you already appearing in any AI search results for your targets? Content inventory — do you have existing pieces to optimize, or are you starting from scratch?

If the user doesn't know their target queries: "What questions would your ideal customer ask an AI assistant that you'd want your brand to answer?"

How This Skill Works

Three modes. Each builds on the previous, but you can start anywhere:

Mode 1: AI Visibility Audit

Map your current presence (or absence) across AI search platforms. Understand what's getting cited, what's getting ignored, and why.

Mode 2: Content Optimization

Restructure and enhance content to match what AI systems extract. This is the execution mode — specific patterns, specific changes.

Mode 3: Monitoring

Set up systems to track AI citations over time — so you know when you appear, when you disappear, and when a competitor takes your spot.

How AI Search Works (and Why It's Different)

• Traditional SEO: Google ranks your page.
• User clicks through.
• You get traffic.

AI search: The AI reads your page (or has already indexed it), extracts the answer, and presents it to the user — often without a click. You get cited, not ranked.

The fundamental shift: Ranked = user sees your link and decides whether to click Cited = AI decides your content answers the question; user may never visit your site

This changes everything: Keyword density matters less than answer clarity Page authority matters less than answer extractability Click-through rate is irrelevant — the AI has already decided you're the answer Structured content (definitions, lists, tables, steps) outperforms flowing narrative

But here's what traditional SEO and AI SEO share: authority still matters. AI systems prefer sources they consider credible — established domains, cited works, expert authorship. You still need backlinks and domain trust. You just also need structure.

See references/ai-search-landscape.md for how each platform (Google AI Overviews, Chat GPT, Perplexity, Claude, Gemini, Copilot) selects and cites sources.

The 3 Pillars of AI Citability

Every AI SEO decision flows from these three:

Pillar 1: Structure (Extractable)

AI systems pull content in chunks. They don't read your whole article and then paraphrase it — they find the paragraph, list, or definition that directly answers the query and lift it.

Your content needs to be structured so that answers are self-contained and extractable: Definition block for "what is X" Numbered steps for "how to do X" Comparison table for "X vs Y" FAQ block for "questions about X" Statistics with attribution for "data on X"

Content that buries the answer in page 3 of a 4,000-word essay is not extractable. The AI won't find it.

Pillar 2: Authority (Citable)

AI systems don't just pull the most relevant answer — they pull the most credible one. Authority signals in the AI era:

Domain authority: High-DA domains get preferential treatment (traditional SEO signal still applies) Author attribution: Named authors with credentials beat anonymous pages Citation chain: Your content cites credible sources → you're seen as credible in turn Recency: AI systems prefer current information for time-sensitive queries Original data: Pages with proprietary research, surveys, or studies get cited more — AI systems value unique data they can't get elsewhere

Pillar 3: Presence (Discoverable)

AI systems need to be able to find and index your content. This is the technical layer:

Bot access: AI crawlers must be allowed in robots.txt (GPTBot, Perplexity Bot, Claude Bot, etc.) Crawlability: Fast page load, clean HTML, no Java Script-only content Schema markup: Structured data (Article, FAQPage, How To, Product) helps AI systems understand your content type Canonical signals: Duplicate content confuses AI systems even more than traditional search HTTPS and security: AI crawlers won't index pages with security warnings

Step 1 — Bot Access Check

First: confirm AI crawlers can access your site.

• Check robots.txt at yourdomain.com/robots.txt.
• Verify these bots are NOT blocked:.

Should NOT be blocked (allow AI indexing):

GPTBot # Open AI / Chat GPT Perplexity Bot # Perplexity Claude Bot # Anthropic / Claude Google-Extended # Google AI Overviews anthropic-ai # Anthropic (alternate identifier) Applebot-Extended # Apple Intelligence cohere-ai # Cohere

If any AI bot is blocked, flag it. That's an immediate visibility killer for that platform.

robots.txt to allow all AI bots: User-agent: GPTBot Allow: /

User-agent: Perplexity Bot Allow: /

User-agent: Claude Bot Allow: /

User-agent: Google-Extended Allow: /

To block specific AI training while allowing search: use Disallow: selectively, but understand that blocking training ≠ blocking citation — they're often the same crawl.

Step 2 — Current Citation Audit

Manually test your target queries on each platform:

For each query, document: Are you cited? (yes/no) Which competitors are cited? What content type gets cited? (definition? list? stats?) How is the answer structured?

• This tells you the pattern that's currently winning.
• Build toward it.

Step 3 — Content Structure Audit

Review your key pages against the Extractability Checklist:

[ ] Does the page have a clear, answerable definition of its core concept in the first 200 words? [ ] Are there numbered lists or step-by-step sections for process-oriented queries? [ ] Does the page have a FAQ section with direct Q&A pairs? [ ] Are statistics and data points cited with source name and year? [ ] Are comparisons done in table format (not narrative)? [ ] Is the page's H1 phrased as the answer to a question,