majestic-seo

Measurement framework for Answer Engine Optimization (AEO). Provides AI visibility metrics, share of voice tracking, citation monitoring, and referral demand measurement. Use when discussing AEO/GEO metrics or AI visibility performance.

Configure HTTP-layer signals for LLM discovery and citation. Use when auditing robots.txt for AI bots, serving Markdown alternates, adding alternate Link headers, handling Accept: text/markdown, or measuring AI-referrer traffic.

Audit and establish a website's SEO, entity, AI navigation, crawler, structured-data, and AEO measurement foundations. Use when the user invokes ai-search-visibility-foundation by name or wants a site-level AI search visibility program with validated implementation evidence.

Position team members as industry analysts and experts for AI credibility and citation authority. Use when building thought leadership profiles, creating expert entity signals for E-E-A-T, or establishing personal brands that AI systems recognize and cite as authorities.

Optimize for behavioral SEO signals including CTR, dwell time, and engagement metrics. Use when diagnosing low click-through rates, improving dwell time, optimizing for Bing behavioral factors, or improving user engagement signals that affect search rankings.

Find bottom-of-funnel keywords with high purchase intent for any product. Use when targeting buyers ready to convert, planning conversion-focused content, or building high-intent keyword lists. Uses Perplexity for real-time search data. Not for broad topic research (use keyword-research).

Create persona x journey grids for AEO content strategy. Use when mapping content to buyer stages, planning question-based content for AI citations, or building a content matrix that covers all personas across awareness, consideration, and decision stages.

Analyze and optimize E-E-A-T signals (Experience, Expertise, Authority, Trust) for content credibility. Use when auditing content for Google quality signals, improving author credibility markers, or diagnosing why content lacks trust signals. Provides signal-by-signal implementation framework.

Build entity triplets and brand associations that LLMs recognize and cite consistently. Use when establishing brand identity in AI knowledge bases, creating structured entity relationships, or improving how AI systems associate your brand with your category.

Optimize content structure and framing for AI citation in ChatGPT, Perplexity, and Gemini. Use when preparing content for GEO (Generative Engine Optimization), improving AI extractability, or increasing chances of being cited in LLM responses. Covers trust formats, fact-density, and semantic depth.

Find content topics and prioritize them without expensive SEO tools. Use when search demand drives topic selection, deciding what to write about against real queries, or doing keyword research on a budget. Uses the 6 Circles Method for systematic topic discovery and prioritization. Not for audience-led editorial programs, calendars, or production capacity planning.

Analyze keyword density, suggest LSI keywords and semantic variations for content optimization.

Create llms.txt files that help AI systems navigate and understand your site structure. Use when setting up AI visibility for a website, improving LLM citation rates, or want AI crawlers to discover your key content. Generates the llms.txt file format for your domain.

Create optimized meta titles, descriptions, and URL suggestions based on character limits and best practices. Generate compelling, keyword-rich metadata.

Frame press releases for AI/LLM citation and authority building. Use when writing press releases optimized for AI training data, building authority signals through newswire distribution, or positioning company news as research findings for higher AI trust.

Maximize AI visibility through query fan-out coverage. Use when planning content clusters, targeting LLM sub-questions, or expanding semantic keyword coverage to get cited by AI systems. Covers semantic variation analysis and sub-question targeting strategies.

Optimize review scores and leverage user-generated content for AI visibility and brand trust. Use when building review presence across platforms, improving review response rates, or leveraging reviews for AI recommendation signals. Covers target metrics and platform prioritization.

Use when adding or auditing Schema.org structured data for Google Search rich results, AI Overviews, and classic search indexing. Generate JSON-LD for organization, FAQ, HowTo, product, and article schemas. Not for direct LLM citation in ChatGPT, Claude, or Perplexity; use ai-crawler-readiness for HTTP-layer LLM visibility. Not for content structure; use structure-architect.

Run SEO and GEO audits on URLs covering technical SEO, content quality, E-E-A-T signals, and AI citation readiness. Use when evaluating search performance or diagnosing ranking issues.

Create one search-led SEO/AEO content asset from keyword or opportunity selection through draft, audit, and ledger update. Use when search intent, keyword strategy, answer-engine optimization, or SEO tracking is central; use content-writer for general articles without a search workflow.

Format content to be eligible for featured snippets and SERP features. Create snippet-optimized content blocks based on best practices.

Analyze and optimize content structure, header hierarchy, schema markup, and internal linking for SEO.

Build and measure topical authority through comprehensive content coverage. Use when planning content clusters, measuring topic coverage gaps, analyzing competitor content depth, or building a content moat around your core subject areas.