ai-search-optimization

仓库: dirnbauer/webconsulting-skills
安装量: 116
排名: #7393

安装

npx skills add https://github.com/dirnbauer/webconsulting-skills --skill ai-search-optimization

AI Search Optimization (AEO & GEO)

Scope: Optimizing content for AI-powered search engines and answer engines This skill covers strategies for visibility in ChatGPT, Perplexity, Google AI Overviews, Microsoft Copilot, and other generative AI platforms.

  1. Understanding AEO & GEO What is AEO (Answer Engine Optimization)?

Answer Engine Optimization focuses on structuring content to provide direct, concise answers to user queries through AI-powered platforms. Unlike traditional SEO which aims for link clicks, AEO optimizes for being cited as the answer source.

Target platforms:

Google AI Overviews (formerly SGE) Perplexity AI ChatGPT Search Microsoft Copilot Search Voice assistants (Siri, Alexa, Google Assistant) What is GEO (Generative Engine Optimization)?

Generative Engine Optimization is the broader discipline of enhancing content visibility within AI-generated search results. It targets generative engines that synthesize answers from multiple sources rather than presenting traditional link lists.

Key differences from traditional SEO:

Aspect Traditional SEO AEO/GEO Goal Rank in SERPs Be cited in AI answers User behavior Click through to site Get answer directly Content format Keyword-optimized pages Structured, citable content Success metric Click-through rate Citation frequency Query type Short keywords Conversational, long-tail The AI Search Landscape (2025-2026) Google AI Overviews: 2B+ monthly users across 200 countries (TechCrunch) Google AI Mode: 100M+ monthly users in US and India ChatGPT Search: Real-time web search with citations Perplexity AI: Real-time citation engine, emphasis on freshness Microsoft Copilot Search: Bing integration with generative AI Zero-click searches: About 60% of global searches end without a click (neotype.ai) 2. Content Structure for AI Readability Semantic HTML Structure

AI systems extract information more effectively from well-structured content:

<html lang="en"> <head> <meta charset="UTF-8"> <title>Descriptive, Question-Answering Title</title> </head> <body>

Primary Topic as Question or Clear Statement

Direct 2-3 sentence answer to the main question.

Subtopic Heading

Detailed explanation with facts and data.

  • Key point 1 with specific information
  • Key point 2 with verifiable data
  • Key point 3 with actionable insight
</body> </html>

Heading Hierarchy Best Practices

H1: Main Topic (contains primary question/keyword)

└── ## H2: Major subtopic └── ### H3: Specific aspect └── #### H4: Details (use sparingly)

Rules:

Single H1 per page H1 should answer "What is this page about?" Use question-format headings when appropriate Include target keywords naturally The Inverted Pyramid Pattern

Structure content for AI extraction:

┌─────────────────────────────────────┐ │ DIRECT ANSWER (First 1-2 │ ← AI extracts this │ sentences answer the query) │ ├─────────────────────────────────────┤ │ KEY FACTS & CONTEXT │ ← Supporting evidence │ (Bullet points, data, quotes) │ ├─────────────────────────────────────┤ │ DETAILED EXPLANATION │ ← Comprehensive coverage │ (Background, methodology, │ │ examples, case studies) │ ├─────────────────────────────────────┤ │ RELATED TOPICS │ ← Topic authority signals │ (Links to related content) │ └─────────────────────────────────────┘

Lists and Tables for Extraction

AI engines prefer structured data formats:

Feature Comparison: Product A vs Product B
Feature Product A Product B
Price $99/month $149/month
AEO
Answer Engine Optimization - optimizing content for direct answers
GEO
Generative Engine Optimization - visibility in AI-generated results
  1. Step one with clear action
  2. Step two with measurable outcome
  3. Step three with verification method
  1. Schema Markup for AI Understanding Essential Schema Types

Research shows structured data significantly improves AI search visibility:

Pages with schema are up to 40% more likely to appear in Google AI Overviews (zarkx.com) Organization schema: 2.8x increase in citation frequency FAQPage schema: 2.5x rise in answer inclusion Article schema: 2.2x boost in content citations Sites with 15+ schema types see 2.4x higher citation rates (surgeboom.com) FAQPage Schema { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "What is Answer Engine Optimization?", "acceptedAnswer": { "@type": "Answer", "text": "Answer Engine Optimization (AEO) is a strategic approach to structuring content so AI platforms like ChatGPT, Perplexity, and Google AI Overviews can easily extract and cite it as direct answers to user queries." } }, { "@type": "Question", "name": "How is AEO different from SEO?", "acceptedAnswer": { "@type": "Answer", "text": "While SEO focuses on ranking in traditional search results for clicks, AEO optimizes content to be cited directly in AI-generated answers, often resulting in zero-click interactions where users get information without visiting the source." } } ] }

HowTo Schema { "@context": "https://schema.org", "@type": "HowTo", "name": "How to Optimize Content for AI Search", "description": "Step-by-step guide to improving visibility in AI-powered search engines", "totalTime": "PT30M", "step": [ { "@type": "HowToStep", "name": "Structure Content Semantically", "text": "Use proper HTML5 semantic elements like article, section, and aside", "position": 1 }, { "@type": "HowToStep", "name": "Implement Schema Markup", "text": "Add FAQPage, HowTo, and Article schema to your pages", "position": 2 }, { "@type": "HowToStep", "name": "Optimize for Conversational Queries", "text": "Write content that answers natural language questions", "position": 3 } ] }

Article Schema with Author { "@context": "https://schema.org", "@type": "Article", "headline": "Complete Guide to AI Search Optimization", "description": "Learn how to optimize content for ChatGPT, Perplexity, and Google AI Overviews", "datePublished": "2025-01-15", "dateModified": "2025-01-15", "author": { "@type": "Person", "name": "Expert Name", "url": "https://example.com/about/expert-name", "jobTitle": "SEO Specialist", "sameAs": [ "https://linkedin.com/in/expertname", "https://twitter.com/expertname" ] }, "publisher": { "@type": "Organization", "name": "Company Name", "logo": { "@type": "ImageObject", "url": "https://example.com/logo.png" } } }

Organization Schema { "@context": "https://schema.org", "@type": "Organization", "name": "Company Name", "url": "https://example.com", "logo": "https://example.com/logo.png", "description": "Brief description of what the organization does", "foundingDate": "2010", "sameAs": [ "https://www.linkedin.com/company/companyname", "https://twitter.com/companyname", "https://github.com/companyname" ], "contactPoint": { "@type": "ContactPoint", "telephone": "+1-555-123-4567", "contactType": "customer service", "availableLanguage": ["English", "German"] } }

  1. E-E-A-T Signals for AI Trust Experience, Expertise, Authoritativeness, Trustworthiness

AI systems prioritize content from credible sources. Implement these signals:

Author Bios

Jane Doe

About the Author

Jane Doe, PhD

15 years of experience in digital marketing. Former Head of SEO at Fortune 500 company. Published in Search Engine Journal, Moz, and Ahrefs Blog.

Trust Signals Checklist Author expertise: Detailed bios with credentials and experience Citations: Link to reputable sources (studies, official docs, experts) Contact information: Clear "About Us" and "Contact" pages HTTPS: Secure connection required Privacy policy: Transparent data handling Update dates: Visible "Last updated" timestamps Original research: Proprietary data, case studies, expert quotes Reviews/testimonials: Third-party validation where applicable Building Domain Authority Earn quality backlinks from reputable industry sites Get mentioned in authoritative publications Contribute guest posts to established platforms Participate in industry forums and communities Create original research that others cite 5. Content Freshness Strategy Update Frequency by Platform Platform Freshness Preference Recommended Update Cycle Perplexity AI Very high Every 2-3 days for trending topics ChatGPT Search High Weekly updates Google AI Overviews Moderate Monthly refresh Bing Copilot Moderate Monthly refresh Content Refresh Protocol

Content Freshness Checklist

Weekly Tasks

  • [ ] Update statistics with latest data
  • [ ] Refresh screenshots and examples
  • [ ] Add new developments or news
  • [ ] Update "Last modified" timestamp

Monthly Tasks

  • [ ] Review and update all factual claims
  • [ ] Add new sections for emerging topics
  • [ ] Update broken links
  • [ ] Refresh expert quotes

Quarterly Tasks

  • [ ] Comprehensive content audit
  • [ ] Competitive analysis
  • [ ] Restructure based on query trends
  • [ ] Update all schema markup

Visible Timestamps

Article Title

  1. Robots.txt for AI Crawlers Allowing AI Bots

To be indexed by AI search engines, explicitly allow their crawlers:

robots.txt - AI Search Optimization

Standard search engines

User-agent: Googlebot Allow: /

User-agent: Bingbot Allow: /

OpenAI (ChatGPT)

User-agent: GPTBot Allow: /

User-agent: ChatGPT-User Allow: /

Perplexity AI

User-agent: PerplexityBot Allow: /

Anthropic (Claude)

User-agent: ClaudeBot Allow: / User-agent: anthropic-ai Allow: /

Google AI (Gemini)

User-agent: Google-Extended Allow: /

Meta AI

User-agent: FacebookBot Allow: /

Common Crawl (used by many AI systems)

User-agent: CCBot Allow: /

Microsoft/Bing AI

User-agent: Applebot Allow: /

Default rule

User-agent: * Allow: / Disallow: /admin/ Disallow: /private/

Sitemap

Sitemap: https://example.com/sitemap.xml

Blocking AI Training While Allowing AI Search (Optional)

Some organizations want to be cited in AI search results but don't want their content used to train AI models. Here's how:

Understanding the difference:

Bot What it does Block = GPTBot Crawls for training OpenAI models Your content won't train future GPT versions ChatGPT-User Live browsing when users search ChatGPT can't cite you in real-time answers Google-Extended Crawls for training Gemini AI Your content won't train Gemini PerplexityBot Live search for Perplexity answers Perplexity can't cite you CCBot Common Crawl - open training datasets Your content won't be in public AI training data

Example: Block training, allow live search citations:

BLOCK: AI model training (your content won't train future AI)

User-agent: GPTBot Disallow: /

User-agent: Google-Extended Disallow: /

User-agent: CCBot Disallow: /

ALLOW: Real-time AI search (AI can cite you in answers)

User-agent: ChatGPT-User Allow: /

User-agent: PerplexityBot Allow: /

Note: Most businesses focused on AI search visibility should allow all bots (Section 6 above). Only use this approach if you have specific concerns about AI training on your content.

AI Bot Reference Bot Name Company Purpose GPTBot OpenAI Training data & ChatGPT browsing ChatGPT-User OpenAI ChatGPT web browsing PerplexityBot Perplexity Real-time search & citations ClaudeBot Anthropic Training & retrieval anthropic-ai Anthropic Claude AI training Google-Extended Google Gemini AI training FacebookBot Meta Meta AI training CCBot Common Crawl Open dataset for AI training 7. Conversational Query Optimization Target Long-Tail, Question-Based Queries

AI search favors natural language:

Traditional keyword: "best project management software"

Conversational queries:

"What is the best project management software for small teams?" "How do I choose project management software for remote work?" "Which project management tool has the best free plan?" Question-Answer Content Pattern

What is [Topic]?

[Topic] is [direct definition in 1-2 sentences].

Key characteristics:

  • Characteristic 1
  • Characteristic 2
  • Characteristic 3

How does [Topic] work?

[Clear explanation of process]

Step-by-step breakdown:

  1. First step
  2. Second step
  3. Third step

Why is [Topic] important?

[2-3 sentences on significance]

Benefits include:

  • Benefit 1 with specific outcome
  • Benefit 2 with measurable result
  • Benefit 3 with real-world application

FAQ Section Template

Frequently Asked Questions

What is Answer Engine Optimization?

Answer Engine Optimization (AEO) is the practice of...

How is GEO different from traditional SEO?

While traditional SEO focuses on...

Which AI search platforms should I optimize for?

The main platforms to consider are...

  1. Multimedia Optimization Image Requirements

Perplexity and other AI engines prefer visual content:

Diagram showing how AI search engines process and cite content
How AI search engines extract and cite content sources

Best practices:

Minimum 2 unique, relevant images per article Descriptive alt text (not keyword stuffing) WebP format for performance Include diagrams, infographics, process flows Add captions with context Video Integration

Video: Complete guide to optimizing for AI search engines

Video Schema { "@context": "https://schema.org", "@type": "VideoObject", "name": "AI Search Optimization Tutorial", "description": "Learn how to optimize content for ChatGPT, Perplexity, and Google AI", "thumbnailUrl": "https://example.com/video-thumbnail.jpg", "uploadDate": "2025-01-15", "duration": "PT10M30S", "contentUrl": "https://example.com/videos/ai-search-tutorial.mp4" }

  1. Monitoring AI Search Visibility AI Brand Monitoring Tools Tool Platforms Monitored Key Features Semrush AI Visibility ChatGPT, Gemini, Perplexity Free tier, mention tracking Brand24 ChatGPT, Perplexity, Claude, Gemini Multi-platform analysis SE Ranking Google AI Overviews, ChatGPT, Gemini Share of voice tracking Keyword.com Google AI Overviews, ChatGPT, Perplexity Optimization suggestions BrandBeacon.ai ChatGPT, Perplexity Competitor benchmarking Sight AI ChatGPT, Claude, Perplexity Sentiment analysis Key Metrics to Track Citation frequency: How often your content is cited Brand mentions: Unprompted mentions in AI responses Referral traffic: Visits from AI search click-throughs Share of voice: Your visibility vs competitors Sentiment: Positive/negative context of mentions Manual Testing Protocol

Monthly AI Visibility Audit

Test Queries (adapt to your niche)

  1. "What is [your product/service]?"
  2. "Best [your category] in [year]"
  3. "[Your brand] vs [competitor]"
  4. "How to [task your product solves]"
  5. "[Your expertise area] best practices"

Platforms to Test

  • [ ] ChatGPT (chat.openai.com)
  • [ ] Perplexity (perplexity.ai)
  • [ ] Google (check for AI Overviews)
  • [ ] Microsoft Copilot (copilot.microsoft.com)
  • [ ] Claude (claude.ai)

Record for Each Query

  • Were you cited? (Yes/No)
  • Citation context (positive/neutral/negative)
  • Competitors mentioned
  • Information accuracy

  • Suggested improvements

  • AI Search Optimization Checklist Content Structure Clear H1 with primary topic/question Logical heading hierarchy (H1 > H2 > H3) Direct answer in first 1-2 sentences Bullet points and numbered lists Comparison tables where applicable Definition lists for terminology Technical Implementation Semantic HTML5 elements (article, section, aside) FAQPage schema on Q&A content HowTo schema on instructional content Article schema with author info Organization schema on about pages robots.txt allows AI crawlers XML sitemap updated and submitted Authority Signals Detailed author bios with credentials Links to author social profiles Citations to authoritative sources Visible publication and update dates HTTPS enabled Contact information accessible Privacy policy present Content Quality Original, expert-level content Factual claims supported by sources Regular updates (at least monthly) Addresses conversational queries Includes relevant images with alt text Mobile-responsive design Monitoring AI visibility monitoring tool configured Monthly manual query testing Competitor citation tracking Referral traffic analysis Content refresh schedule maintained

  • Platform-Specific Optimization Google AI Overviews Pages with schema are up to 40% more likely to appear in AI Overviews Focus on featured snippet optimization (still relevant) Emphasize E-E-A-T signals Target informational and comparison queries Perplexity AI Freshness is critical - update content every 2-3 days for trending topics Real-time citations from current sources Prefer authoritative domains Include unique images and data ChatGPT Search Web browsing uses GPTBot and ChatGPT-User Emphasizes recent, authoritative content Good at following citations and references Benefits from clear, structured content Microsoft Copilot Built on Bing index Strong integration with Microsoft ecosystem Emphasizes factual, well-sourced content Benefits from Bing Webmaster Tools optimization
  • Future-Proofing Your AI Search Strategy Emerging Trends Multimodal search: AI understanding images, video, audio Conversational commerce: AI-driven purchase decisions Personalized AI responses: Context-aware answer customization Agent-based search: AI agents completing tasks autonomously Real-time fact-checking: AI validating claims before citation Adaptation Strategy

Quarterly Review Checklist

AI Platform Updates

  • [ ] Review new AI search features from major platforms
  • [ ] Update robots.txt for new AI bot user agents
  • [ ] Test visibility on new/emerging AI platforms

Content Strategy

  • [ ] Analyze which content types get most citations
  • [ ] Identify gaps in AI coverage vs competitors
  • [ ] Plan new content for underserved queries

Technical Updates

  • [ ] Review schema.org for new relevant types
  • [ ] Update structured data implementation

  • [ ] Test page speed and Core Web Vitals

  • TYPO3 Implementation Guide

Compatibility: TYPO3 v13.x and v14.x (v14 preferred) All configurations in this section work on both v13 and v14.

This section covers TYPO3-specific implementation of AEO/GEO strategies using TYPO3 extensions, configuration, and best practices.

Installation Mode: Composer vs Classic

⚠️ Composer Mode Highly Recommended

For AI search optimization, Composer-based TYPO3 installations are strongly recommended. All extensions in this guide are available via both Composer (Packagist) and TER (Classic Mode).

Why Composer Mode is Essential for Modern TYPO3 Aspect Composer Mode Classic Mode Dependency Resolution Automatic with version constraints Manual, no transitive dependencies Autoloading PSR-4 optimized, production-ready TYPO3 internal, less optimized Security Separate web root (/public) All files in web root Updates Single command: composer update Manual download/upload per extension Reproducibility composer.lock ensures identical installs No version locking mechanism TYPO3 v14 Future Fully supported Requires composer.json in all extensions Technical Explanation

Composer Mode uses PHP's standard dependency manager to:

Resolve Dependencies Automatically: Extensions like brotkrueml/schema depend on psr/http-message and other packages. Composer resolves the entire dependency tree, ensuring compatible versions are installed.

Generate Optimized Autoloaders: Composer creates a PSR-4 compliant autoloader that loads classes on-demand, improving performance compared to TYPO3's legacy class loading.

Enforce Version Constraints: The composer.json constraint "typo3/cms-core": "^13.4 || ^14.0" guarantees only compatible versions are installed.

Enable Security Isolation: The recommended structure places vendor/, config/, and other sensitive directories outside the web-accessible /public folder.

Support Modern Workflows: CI/CD pipelines, automated testing, and deployment tools expect Composer-based projects.

TYPO3 v14 Breaking Change: In TYPO3 v14, even Classic Mode requires every extension to have a valid composer.json with proper type and extension-key definitions. Extensions without this file will not be detected.

// Required composer.json structure for all extensions (v14+) { "name": "vendor/extension-key", "type": "typo3-cms-extension", "extra": { "typo3/cms": { "extension-key": "extension_key" } } }

Extension Compatibility Matrix Extension TYPO3 v13 TYPO3 v14 PHP Composer TER Purpose typo3/cms-seo ✓ ✓ 8.2+ ✓ ✓ Core SEO (meta tags, sitemaps, canonicals) brotkrueml/schema ✓ (v4.x) ✓ (v4.x) 8.2+ ✓ ✓ Schema.org structured data (JSON-LD) yoast-seo-for-typo3/yoast_seo ✓ ✗ 8.1+ ✓ ✓ Content analysis, readability (v13 only) clickstorm/cs_seo ✓ (v9.3+) ✓ (v9.3+) 8.2+ ✓ ✓ Extended SEO features, evaluations 13.1 Required Extensions Installation Composer Mode (Recommended)

Core SEO extension (meta tags, sitemaps, canonicals)

ddev composer require typo3/cms-seo

Schema.org structured data (essential for AI search)

Version constraint ensures v13/v14 compatibility

ddev composer require brotkrueml/schema:"^4.2"

Optional: Extended SEO features (v13/v14 compatible)

ddev composer require clickstorm/cs_seo:"^9.3"

In Composer mode, extensions are auto-activated

Verify installation:

ddev typo3 extension:list | grep -E "seo|schema"

Version Constraints Explained:

{ "require": { "typo3/cms-seo": "^13.4 || ^14.0", "brotkrueml/schema": "^4.2" } }

^4.2 = Any version ≥4.2.0 and <5.0.0 (allows minor/patch updates) ^13.4 || ^14.0 = Supports both TYPO3 v13.4+ and v14.0+ Classic Mode (TER)

Note: Classic Mode is supported but not recommended. TYPO3 v14 requires all extensions to have a valid composer.json even in Classic Mode.

Download from TER:

https://extensions.typo3.org/extension/seo https://extensions.typo3.org/extension/schema

Install via Extension Manager:

Backend → Admin Tools → Extensions Click "Upload Extension" or use "Get Extensions" to search TER Activate each extension after upload

Verify Installation:

Check Admin Tools → Extensions for active status Clear all caches after activation 13.2 Robots.txt Configuration for AI Bots

Configure robots.txt via TYPO3's static routes to allow AI crawlers:

config/sites/main/config.yaml

routes: - route: robots.txt type: staticText content: | # Standard search engines User-agent: Googlebot Allow: /

  User-agent: Bingbot
  Allow: /

  # OpenAI (ChatGPT)
  User-agent: GPTBot
  Allow: /

  User-agent: ChatGPT-User
  Allow: /

  # Perplexity AI
  User-agent: PerplexityBot
  Allow: /

  # Anthropic (Claude)
  User-agent: ClaudeBot
  Allow: /

  User-agent: anthropic-ai
  Allow: /

  # Google AI (Gemini)
  User-agent: Google-Extended
  Allow: /

  # Meta AI
  User-agent: FacebookBot
  Allow: /

  # Common Crawl (used by many AI systems)
  User-agent: CCBot
  Allow: /

  # Default
  User-agent: *
  Allow: /
  Disallow: /typo3/
  Disallow: /typo3conf/
  Disallow: /typo3temp/

  Sitemap: https://example.com/sitemap.xml

13.3 Schema.org Implementation with EXT:schema Installation and Setup ddev composer require brotkrueml/schema:"^4.2" ddev typo3 extension:activate schema

Include the static TypoScript template in your site package.

FAQPage Schema via Fluid ViewHelper

Article Schema via Fluid ViewHelper

<schema:type.person -as="author"
    name="{article.author.name}"
    url="{article.author.profileUrl}"

    <schema:property -as="sameAs" value="{article.author.linkedIn}" />
    <schema:property -as="sameAs" value="{article.author.twitter}" />
</schema:type.person>

<schema:type.organization -as="publisher"
    name="{settings.siteName}"
    url="{settings.siteUrl}"
>
    <schema:type.imageObject -as="logo" url="{settings.logoUrl}" />
</schema:type.organization>

HowTo Schema via Fluid ViewHelper

<schema:type.howTo name="How to Optimize Content for AI Search" description="Step-by-step guide to improving visibility in AI-powered search engines"

<f:for each="{steps}" as="step" iteration="iter">
    <schema:type.howToStep -as="step"
        name="{step.title}"
        text="{step.description}"
        position="{iter.cycle}"
    />
</f:for>

Organization Schema via PHP API (PSR-14 Event)

typeFactory->create('Organization') ->setProperty('name', 'Your Company Name') ->setProperty('url', 'https://example.com') ->setProperty('logo', 'https://example.com/logo.png') ->setProperty('description', 'Brief company description for AI understanding') ->setProperty('sameAs', [ 'https://www.linkedin.com/company/yourcompany', 'https://twitter.com/yourcompany', 'https://github.com/yourcompany', ]); $contactPoint = $this->typeFactory->create('ContactPoint') ->setProperty('telephone', '+43-1-234567') ->setProperty('contactType', 'customer service') ->setProperty('availableLanguage', ['German', 'English']); $organization->setProperty('contactPoint', $contactPoint); $event->addType($organization); } } Dynamic Article Schema via PSR-14 Event getRequest(); $pageInformation = $request->getAttribute('frontend.page.information'); $page = $pageInformation->getPageRecord(); // Only add Article schema for specific doktypes (e.g., 1 = standard page) if ((int)$page['doktype'] !== 1) { return; } $article = $this->typeFactory->create('Article') ->setProperty('headline', $page['title']) ->setProperty('description', $page['description'] ?: $page['abstract']) ->setProperty('datePublished', date('c', $page['crdate'])) ->setProperty('dateModified', date('c', $page['tstamp'])); // Add author if available if (!empty($page['author'])) { $author = $this->typeFactory->create('Person') ->setProperty('name', $page['author']); $article->setProperty('author', $author); } $event->addType($article); } } 13.4 Content Freshness with Last Modified Headers TypoScript Configuration # Expose last modified date in HTTP headers config { sendCacheHeaders = 1 additionalHeaders { 10 { header = X-Content-Last-Modified value = TEXT value.data = page:SYS_LASTCHANGED value.strftime = %Y-%m-%dT%H:%M:%S%z } } } # Display last updated date in content lib.lastModified = TEXT lib.lastModified { data = page:SYS_LASTCHANGED strftime = %B %d, %Y wrap = } Fluid Template for Visible Timestamps

{page.title}

13.5 Author Bio Schema for E-E-A-T TCA Extension for Author Fields [ 'label' => 'Author Name', 'config' => [ 'type' => 'input', 'size' => 50, 'max' => 255, ], ], 'tx_sitepackage_author_title' => [ 'label' => 'Author Title/Credentials', 'config' => [ 'type' => 'input', 'size' => 50, 'max' => 255, ], ], 'tx_sitepackage_author_bio' => [ 'label' => 'Author Bio', 'config' => [ 'type' => 'text', 'rows' => 5, ], ], 'tx_sitepackage_author_linkedin' => [ 'label' => 'Author LinkedIn URL', 'config' => [ 'type' => 'link', 'allowedTypes' => ['url'], ], ], ]; ExtensionManagementUtility::addTCAcolumns('pages', $additionalColumns); ExtensionManagementUtility::addToAllTCAtypes( 'pages', '--div--;Author,tx_sitepackage_author_name,tx_sitepackage_author_title,tx_sitepackage_author_bio,tx_sitepackage_author_linkedin' ); Author Schema PSR-14 Event Listener getRequest(); $pageInformation = $request->getAttribute('frontend.page.information'); $page = $pageInformation->getPageRecord(); if (empty($page['tx_sitepackage_author_name'])) { return; } $author = $this->typeFactory->create('Person') ->setProperty('name', $page['tx_sitepackage_author_name']) ->setProperty('jobTitle', $page['tx_sitepackage_author_title'] ?? '') ->setProperty('description', $page['tx_sitepackage_author_bio'] ?? ''); if (!empty($page['tx_sitepackage_author_linkedin'])) { $author->setProperty('sameAs', [$page['tx_sitepackage_author_linkedin']]); } $event->addType($author); } } 13.6 FAQ Content Element with Schema Content Block Definition (EXT:content_blocks) # ContentBlocks/ContentElements/faq-accordion/config.yaml name: vendor/faq-accordion typeName: faq_accordion title: FAQ Accordion description: FAQ with structured data for AI search group: common fields: - identifier: faq_items type: Collection labelField: question fields: - identifier: question type: Text required: true - identifier: answer type: Textarea enableRichtext: true required: true Fluid Template with Schema {namespace schema=Brotkrueml\Schema\ViewHelpers}
{item.question}
{item.answer}
13.7 Breadcrumb Schema Fluid ViewHelper Implementation {namespace schema=Brotkrueml\Schema\ViewHelpers} 13.8 Semantic HTML via Fluid Layouts <html lang="{siteLanguage.locale.languageCode}"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1"> </head> <body>

{page.title}

{page.subtitle}
</body> </html> 13.9 TYPO3 AI Search Optimization Checklist Extensions & Configuration EXT:seo installed and configured EXT:schema (brotkrueml/schema ^4.2) installed Static TypoScript templates included robots.txt configured via site config with AI bot rules Schema Implementation Organization schema on all pages Article schema on content pages FAQPage schema on FAQ content HowTo schema on tutorial content BreadcrumbList on all pages Author/Person schema with credentials Content Structure Semantic HTML5 elements in Fluid templates Proper heading hierarchy (single H1) Visible publication and update dates Author bios with credentials Alt text on all images via FAL Technical SYS_LASTCHANGED used for content freshness Cache headers configured XML sitemap via EXT:seo Canonical URLs configured hreflang for multi-language sites 13.10 Debugging Schema Output Admin Panel Integration EXT:schema integrates with TYPO3's Admin Panel. Enable it to see generated JSON-LD: # config/system/settings.php $GLOBALS['TYPO3_CONF_VARS']['BE']['adminPanel'] = true; Validation Tools After implementing structured data, validate using: Schema Markup Validator: https://validator.schema.org/ Google Rich Results Test: https://search.google.com/test/rich-results Google Search Console: Submit and monitor structured data View Generated JSON-LD # Fetch page and extract JSON-LD curl -s https://example.com/page | grep -o '<script type="application/ld+json">.*</script>' 14. Markdown & MDX Implementation This section covers AI search optimization for static sites and documentation platforms using Markdown (MD) and MDX. 14.1 Frontmatter for AI Search Use frontmatter to define structured metadata that frameworks can transform into meta tags and structured data: --- title: "How to Optimize Content for AI Search Engines" description: "Complete guide to AEO and GEO strategies for ChatGPT, Perplexity, and Google AI Overviews visibility." date: 2025-01-15 lastmod: 2025-01-15 author: name: "Jane Doe" title: "SEO Specialist" linkedin: "https://linkedin.com/in/janedoe" twitter: "https://twitter.com/janedoe" tags: ["aeo", "geo", "ai-search", "seo"] category: "SEO" image: "/images/ai-search-guide.jpg" schema: type: "Article" wordCount: 2500 draft: false --- 14.2 Content Structure Best Practices # Main Topic as H1 (Single, Contains Primary Question/Keyword) Brief 2-3 sentence summary answering the main question directly. This paragraph is what AI engines extract first. ## What is [Topic]? Direct definition in 1-2 sentences. [Topic] is... ### Key Characteristics - **Point 1:** Specific, factual information - **Point 2:** Verifiable data with source - **Point 3:** Actionable insight ## How Does [Topic] Work? Clear process explanation. 1. First step with expected outcome 2. Second step with verification 3. Third step with result ## Why is [Topic] Important? | Benefit | Impact | Evidence | |---------|--------|----------| | Benefit 1 | Measurable result | Source/study | | Benefit 2 | Specific outcome | Data point | ## Frequently Asked Questions
Question 1? Direct answer to question 1.
Question 2? Direct answer to question 2.
14.3 JSON-LD in MDX (Next.js / Astro) Next.js App Router // components/JsonLd.tsx type JsonLdProps = { data: Record; }; export function JsonLd({ data }: JsonLdProps) { return ( <script type="application/ld+json" dangerouslySetInnerHTML={{ __html: JSON.stringify(data).replace(/ # AI Search Optimization Guide Content here... Astro with astro-seo-schema npm install schema-dts astro-seo-schema --- // src/layouts/Article.astro import { Schema } from 'astro-seo-schema'; const { frontmatter } = Astro.props; --- <html> <head> </head> <body> </body> </html> 14.4 FAQ Schema Component for MDX // components/FAQ.tsx import { JsonLd } from './JsonLd'; type FAQItem = { question: string; answer: string; }; type FAQProps = { items: FAQItem[]; }; export function FAQ({ items }: FAQProps) { const schemaData = { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": items.map(item => ({ "@type": "Question", "name": item.question, "acceptedAnswer": { "@type": "Answer", "text": item.answer } })) }; return ( <>
{items.map((item, index) => (
{item.question}

{item.answer}

))}
); } import { FAQ } from '@/components/FAQ'; ## Frequently Asked Questions 14.5 Raw MDX View with URL Parameter Enable viewing raw MDX source for transparency and AI training accessibility: Next.js Implementation // next.config.js - Rewrite .md URLs to API module.exports = { async rewrites() { return [ { source: '/docs/:path*.md', destination: '/api/raw-mdx?path=:path*', }, ]; }, }; // app/api/raw-mdx/route.ts import { NextRequest, NextResponse } from 'next/server'; import fs from 'fs'; import path from 'path'; export async function GET(request: NextRequest) { const searchParams = request.nextUrl.searchParams; const filePath = searchParams.get('path'); if (!filePath) { return NextResponse.json({ error: 'Path required' }, { status: 400 }); } // Prevent directory traversal const safePath = filePath.replace(/\.\./g, ''); const mdxPath = path.join(process.cwd(), 'content', `${safePath}.mdx`); try { const content = fs.readFileSync(mdxPath, 'utf8'); return new NextResponse(content, { headers: { 'Content-Type': 'text/plain; charset=utf-8' }, }); } catch { return NextResponse.json({ error: 'Not found' }, { status: 404 }); } } Usage: /docs/getting-started → Rendered page /docs/getting-started.md → Raw MDX source Query Parameter Alternative // app/docs/[...slug]/page.tsx import { notFound } from 'next/navigation'; import fs from 'fs'; import path from 'path'; type Props = { params: { slug: string[] }; searchParams: { raw?: string }; }; export default async function Page({ params, searchParams }: Props) { const slug = params.slug.join('/'); const mdxPath = path.join(process.cwd(), 'content', `${slug}.mdx`); // Show raw MDX if ?raw=true if (searchParams.raw === 'true') { try { const rawContent = fs.readFileSync(mdxPath, 'utf8'); return ( ); } catch { notFound(); } } // Normal MDX rendering // ... your MDX processing } Usage: /docs/getting-started → Rendered page /docs/getting-started?raw=true → Raw MDX source 14.6 Automatic Schema Generation from Frontmatter // lib/generateSchema.ts type Frontmatter = { title: string; description: string; date: string; lastmod?: string; author?: { name: string; title?: string; linkedin?: string; twitter?: string; }; image?: string; schema?: { type?: 'Article' | 'HowTo' | 'FAQPage'; wordCount?: number; }; }; export function generateSchema(frontmatter: Frontmatter, url: string) { const schemaType = frontmatter.schema?.type || 'Article'; const baseSchema = { "@context": "https://schema.org", "@type": schemaType, "headline": frontmatter.title, "description": frontmatter.description, "url": url, "datePublished": frontmatter.date, "dateModified": frontmatter.lastmod || frontmatter.date, }; if (frontmatter.author) { const sameAs = [ frontmatter.author.linkedin, frontmatter.author.twitter, ].filter(Boolean); baseSchema["author"] = { "@type": "Person", "name": frontmatter.author.name, "jobTitle": frontmatter.author.title, ...(sameAs.length > 0 && { "sameAs": sameAs }), }; } if (frontmatter.image) { baseSchema["image"] = frontmatter.image; } if (frontmatter.schema?.wordCount) { baseSchema["wordCount"] = frontmatter.schema.wordCount; } return baseSchema; } 14.7 MD/MDX AI Search Checklist Frontmatter: Title, description, date, lastmod, author with credentials Structure: Single H1, logical heading hierarchy, direct answers first Schema: JSON-LD in layout or per-page (Article, FAQPage, HowTo) FAQ sections: Use
/ with FAQPage schema Tables: For comparisons (AI extracts structured data) Lists: Bullet points and numbered steps Last modified: Visible and in frontmatter Author bio: Name, credentials, social links Raw view: Optional .md or ?raw=true endpoint Images: Alt text, proper dimensions, WebP format llms.txt: LLM-friendly site index (see Section 15) 15. llms.txt - LLM Site Index The llms.txt standard provides a structured, machine-readable file to help LLMs understand and navigate your website content efficiently. Note: As of late 2025, adoption by major AI companies is still limited, but implementing llms.txt is low-effort and future-proofs your site for AI discovery. 15.1 What is llms.txt? Similar to robots.txt for crawlers, llms.txt is a Markdown file at your site root that: Provides a concise index of key documentation/pages Includes descriptions to help LLMs understand content purpose Enables AI tools to find relevant content without parsing your entire site Supports an optional llms-full.txt with complete documentation 15.2 Basic llms.txt Format # Your Company Name > Brief one-sentence description of your site/product. Additional context about the site, target audience, and how to use this index. ## Documentation - [Getting Started](/docs/getting-started): Quick introduction for new users - [API Reference](/docs/api): Complete API documentation with examples - [Configuration Guide](/docs/configuration): Setup and configuration options ## Tutorials - [Building Your First App](/tutorials/first-app): Step-by-step beginner guide - [Advanced Patterns](/tutorials/advanced): In-depth exploration of features ## Optional - [About Us](/about): Company background and team - [Blog](/blog): Latest news and articles - [Changelog](/changelog): Version history and updates Key rules: Single H1 (#) with site/project name Blockquote (>) with brief description H2 sections (##) for content groups Links formatted as [Title](URL): Description ## Optional section for content LLMs can skip 15.3 llms-full.txt - Complete Documentation For sites with extensive documentation, provide a llms-full.txt containing your entire documentation in a single Markdown file: # Your Company Documentation > Complete documentation for Your Company's platform. --- ## Getting Started [Full content of getting started page...] --- ## API Reference ### Authentication [Full API auth documentation...] ### Endpoints [Full API endpoints documentation...] --- ## Configuration [Full configuration documentation...] Use cases: AI coding assistants need full API context Complex integrations require complete documentation Technical support AI needs comprehensive knowledge base 15.4 TYPO3 Implementation TYPO3 has a dedicated extension for llms.txt generation: # Install the extension (TYPO3 v13+) ddev composer require web-vision/ai-llms-txt Site Configuration # config/sites/main/config.yaml imports: - resource: 'EXT:ai_llms_txt/Configuration/Routes/RouterEnhancer.yaml' TypoScript Configuration # Include extension TypoScript @import 'EXT:ai_llms_txt/Configuration/TypoScript/setup.typoscript' # Custom configuration plugin.tx_aillmstxt { settings { # Pages to include (comma-separated UIDs or "auto") includePages = auto # Exclude specific pages excludePages = 1,2,3 # Include page types includeDoktypes = 1,4 # Maximum depth maxDepth = 3 } } Manual llms.txt via Static Route For full control, create a static route: # config/sites/main/config.yaml routes: - route: llms.txt type: staticText content: | # Your TYPO3 Site > Enterprise content management and digital experience platform. ## Main Sections - [Home](https://example.com/): Main landing page - [Products](https://example.com/products): Our product catalog - [Documentation](https://example.com/docs): Technical documentation - [Blog](https://example.com/blog): Latest articles and news ## Optional - [About Us](https://example.com/about): Company information - [Contact](https://example.com/contact): Get in touch 15.5 Next.js Implementation Static File (Simple) # Your Next.js App > Modern web application built with Next.js. ## Pages - [Home](/): Main landing page - [Documentation](/docs): Technical docs - [Blog](/blog): Latest articles Dynamic Generation (App Router) // app/llms.txt/route.ts import { getDocPages, getBlogPosts } from '@/lib/content'; export async function GET() { const docs = await getDocPages(); const posts = await getBlogPosts(); const content = `# Your Site Name > Brief description of your site. ## Documentation ${docs.map(doc => `- [${doc.title}](/docs/${doc.slug}): ${doc.description}`).join('\n')} ## Blog ${posts.slice(0, 10).map(post => `- [${post.title}](/blog/${post.slug}): ${post.excerpt}`).join('\n')} ## Optional - [About](/about): About us - [Contact](/contact): Get in touch `; return new Response(content, { headers: { 'Content-Type': 'text/plain; charset=utf-8', 'Cache-Control': 'public, max-age=3600, must-revalidate', }, }); } llms-full.txt Generation // app/llms-full.txt/route.ts import { getDocPages } from '@/lib/content'; import fs from 'fs'; import path from 'path'; export async function GET() { const docs = await getDocPages(); let fullContent = `# Complete Documentation > Full documentation for Your Site. `; for (const doc of docs) { const mdxPath = path.join(process.cwd(), 'content/docs', `${doc.slug}.mdx`); try { const content = fs.readFileSync(mdxPath, 'utf8'); // Remove frontmatter const cleanContent = content.replace(/^---[\s\S]*?---\n/, ''); fullContent += `---\n\n## ${doc.title}\n\n${cleanContent}\n\n`; } catch { // Skip if file not found } } return new Response(fullContent, { headers: { 'Content-Type': 'text/plain; charset=utf-8', 'Cache-Control': 'public, max-age=3600, must-revalidate', }, }); } 15.6 Astro Implementation Using Integration npm install @waldheimdev/astro-ai-llms-txt // astro.config.mjs import llmsTxt from '@waldheimdev/astro-ai-llms-txt'; export default { integrations: [ llmsTxt({ projectName: 'Your Project', description: 'Your project description.', site: 'https://your-domain.com', }), ], }; Manual API Route // src/pages/llms.txt.ts import { getCollection } from 'astro:content'; import type { APIRoute } from 'astro'; export const GET: APIRoute = async () => { const docs = await getCollection('docs'); const blog = await getCollection('blog'); const content = `# Your Astro Site > Static site built with Astro. ## Documentation ${docs.map(doc => `- [${doc.data.title}](/docs/${doc.slug}): ${doc.data.description}`).join('\n')} ## Blog ${blog.slice(0, 10).map(post => `- [${post.data.title}](/blog/${post.slug}): ${post.data.excerpt}`).join('\n')} `; return new Response(content, { headers: { 'Content-Type': 'text/plain; charset=utf-8' }, }); }; 15.7 llms.txt Best Practices Aspect Recommendation File size Keep under 50KB for efficient parsing Descriptions Brief, informative (not marketing copy) Links Use absolute URLs for external consumption Updates Regenerate on content changes Sections Group logically (Docs, API, Tutorials, Optional) Optional section Mark non-essential content LLMs can skip 15.8 llms.txt Checklist llms.txt at site root with structured index H1 heading with site/project name Blockquote summary describing the site Organized sections (Docs, API, Blog, Optional) Link descriptions for each URL llms-full.txt for documentation-heavy sites (optional) Cache headers set appropriately (1 hour recommended) UTF-8 encoding with text/plain content type Resources & References Official Documentation Google AI Overviews Guidelines Schema.org Full Hierarchy OpenAI GPTBot Documentation llms.txt Specification TYPO3 EXT:ai_llms_txt Documentation Industry Resources Semrush AI Search Study Ahrefs AI Impact on SEO Monitoring Tools Semrush AI Visibility Checker Brand24 AI Brand Visibility SE Ranking AI Visibility Tracker Credits & Attribution This skill synthesizes best practices from industry research by Microsoft Advertising, Semrush, Ahrefs, and the broader SEO community's work on generative engine optimization. Key sources: Microsoft Advertising: "From Discovery to Influence: A Guide to AEO and GEO" Semrush research on AI search content optimization Ahrefs analysis of AI impact on SEO Created by webconsulting.at for the Claude Cursor Skills collection. ?>
← 返回排行榜