google-search-ai-optimization

google-search-ai-optimization

Build and audit web experiences for Google Search generative AI features using Google's official guidance as the baseline.

Match the scope to the request. A title fix, schema question, or content edit does not require a full audit. Use only the references and tools needed to resolve the task; ordinary layout, section order, and content length remain design choices, not Google eligibility rules.

Decision Tree

What are you doing?

• Implementing SEO/GEO in a codebase

  • Read references/technical-implementation.md when the change needs crawl, render, metadata, or schema details.
  • Use scripts/audit_page.py when static page evidence helps. Add --expect-indexable only when the target is intended to be indexed, never for an intentional noindex page.

• Planning or editing content for AI Overviews, AI Mode, or query fan-out

  • Read references/content-and-entity-patterns.md for content choices, or references/google-guidance.md when the policy rationale is needed.

• Working on ecommerce, local business, or agentic user journeys

  • Read references/ecommerce-local-agentic.md

• Debugging myths, hacks, preview controls, or questionable recommendations

  • Read references/gotchas.md

• Doing a broad codebase SEO audit with remediation handoff

  • Prefer {{ skill:seo-analysis }} first, then use this skill for Google AI Search-specific implementation choices.

Quick Reference

Task	Action	Why
Make a page eligible for Google AI Search surfaces	Ensure it is indexable, snippet-eligible, crawlable, and useful in Google Search	Google's generative AI features are rooted in Search ranking, retrieval, and quality systems
Optimize for GEO/AEO on Google	Treat it as SEO, not a separate hack track	Google's guidance frames AEO/GEO work as optimizing the Search experience
Implement technical foundations	Use crawlable links, canonical URLs, correct status codes, rendered primary content, sitemaps, and stable metadata	Google must discover, render, understand, and select the page before content work matters
Improve content for AI answers	Add unique experience, original facts, clear structure, media, and user-satisfying coverage	Non-commodity, people-first content is the durable signal
Use structured data	Add only page-representative JSON-LD that matches visible content and Google feature docs	Structured data helps eligibility for rich results but is not required for generative AI search
Control snippets carefully	Use nosnippet, max-snippet, max-image-preview, and data-nosnippet only when the tradeoff is intentional	Restrictive preview controls can also limit use in AI Overviews and AI Mode
Ignore unsupported hacks	Do not create llms.txt, AI-only markup, artificial chunking, fake mentions, or mass query-variation pages for Google	Google explicitly lists these as unnecessary or risky for Google Search
Support browser agents	Use semantic controls, labels, stable layouts, visible actions, and clean accessibility tree signals	Agentic experiences inspect screenshots, HTML, and accessibility trees

Core Workflow

Apply the steps relevant to the requested change; use the whole workflow only for a full audit or implementation plan.

1. Establish the page type and business goal: informational, commercial, product, local, documentation, support, account, or utility.
2. When diagnosing eligibility for content intended for Search, check status code, indexability, robots directives, canonical, crawlable links, rendered main content, and snippet eligibility. Preserve intentional exclusions.
3. Inspect the relevant source abstraction and rendered output when needed to verify the change. Shared layout, metadata, routing, and CMS template bugs can affect many URLs.
4. Improve the page for humans first: unique experience, non-commodity facts, clear headings, useful media, descriptive links, and satisfying next steps.
5. Add structured data only when it accurately represents visible page content and a supported Google Search feature or entity clarification.
6. For ecommerce and local sites, keep product, merchant, and business details consistent across on-page content, structured data, Merchant Center, and Google Business Profile.
7. For agentic readiness, test the journey as machine-readable UI: semantic HTML, labels, stable layout, visible actions, and no invisible overlays blocking controls.
8. Document tradeoffs. If a page uses noindex, nosnippet, JavaScript-only rendering, or duplicate templates, record whether that is intentional.

Do not fabricate reviews, use deceptive schema, or cloak content for crawlers. Obtain authorization before changing external properties such as Search Console, Merchant Center, or Business Profile; an on-site code task is not consent to modify those accounts.

Expected Deliverables

Return the requested answer, patch, or plan with relevant evidence and acceptance checks. For a full audit, the optional templates/implementation-brief-template.md can organize:

• eligibility evidence and limits, when eligibility is in scope;
• findings and ordered fixes, using a table when useful;
• content/entity recommendations, when content work is requested.

Discuss unsupported tactics only when the request or evidence raises them. Do not append a myth report or unrelated plans to a scoped answer.

Reading Guide

Need	Read
Official Google AI Search guidance distilled for developers	references/google-guidance.md
Crawl, render, index, metadata, canonical, robots, JS, and schema implementation	references/technical-implementation.md
Content, entity, media, internal-link, and query fan-out patterns	references/content-and-entity-patterns.md
Ecommerce, local business, Merchant Center, Business Profile, and browser-agent UX	references/ecommerce-local-agentic.md
Unsupported tactics, edge cases, and policy traps	references/gotchas.md
Static HTML/URL signal probe	scripts/audit_page.py
Reusable implementation handoff	templates/implementation-brief-template.md

Gotchas

1. GEO is not a separate Google hack layer: for Google Search, optimize the search experience and foundations instead of inventing AI-only markup.
2. Indexing, ranking, and serving are never guaranteed: passing technical checks does not guarantee selection or visibility.
3. Preview controls affect AI features: nosnippet and restrictive max-snippet settings can limit direct use in AI Overviews and AI Mode.
4. Structured data is not a generative AI requirement: use it because it accurately describes visible content and supports rich-result eligibility.
5. Do not scale pages for every fan-out query: mass pages for query variants can become scaled content abuse.
6. Rendered reality wins: framework metadata APIs, client rendering, web components, and hydration can change what Google and agents actually see.

Source Baseline

The recorded baseline is official Google guidance reviewed on May 16, 2026, especially the AI optimization guide, recorded as updated 2026-05-15 UTC. This is a dated baseline, not a claim that every policy remains current.

When a gap, uncertain policy, or suspected change affects the task, consult the relevant current official Google/tool documentation; use trusted sources for corroboration, not as substitutes for Google policy. Otherwise reuse sufficient evidence without routine browsing. If verification is unavailable, state the gap. Propose a sourced correction plus an example or check in the canonical package; never automatically edit installed copies.

Helper Files

• references/README.md - source map and disclosure guide.
• references/google-guidance.md - official Google AI Search guidance, reframed for web development.
• references/technical-implementation.md - implementation checks and framework patterns.
• references/content-and-entity-patterns.md - durable content and entity strategies.
• references/ecommerce-local-agentic.md - commerce, local, and browser-agent readiness.
• references/gotchas.md - traps, myths, and false positives.
• scripts/audit_page.py - deterministic static signal audit for one URL or HTML file.
• scripts/validate.py - structural validator for this skill.
• scripts/test_skill.py - lightweight regression tests for packaging and audit behavior.
• templates/implementation-brief-template.md - fix-ready planning template.