AI search and citation behavior change quickly. The frameworks below use the best available public data as of early 2026 (Google surfaces, ChatGPT, Perplexity, Gemini, and large-sample studies). Re-check assumptions as platforms update.
AI Search Visibility (AEO, GEO, LLMO) covers how your brand appears inside generative answers on ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude—not just traditional blue links. This skill builds a structured program to measure share of voice in AI responses, identify citation gaps versus competitors, and prioritize content and entity signals that LLMs reliably reference.
It connects answer engine optimization (AEO) and generative engine optimization (GEO) to concrete workflows: prompt libraries for monitoring, source page audits, schema and clarity improvements for machine-readable facts, and digital PR angles that earn mentions AI systems pick up. LLMO (large language model optimization) here means shaping what models can confidently say about your product category, pricing, integrations, and differentiation.
SEO leads expanding into AI visibility, content strategists who need a framework beyond Google Search Console rankings, and B2B SaaS teams whose buyers research vendors inside ChatGPT before visiting a website. It suits brands seeing traffic shifts as zero-click AI answers grow, and agencies packaging GEO retainers with defensible measurement.
If your category triggers comparison questions ("best CRM for startups," "X vs Y pricing"), this skill is directly relevant. It also helps comms and product marketing align on canonical facts models should repeat accurately.
Begin with category prompt discovery: which questions buyers ask AI tools about your space. Map current AI answers for brand mentions, citations, sentiment, and competitor presence. Audit pages that should be cited—product, pricing, docs, comparison—and fix ambiguity, outdated stats, and missing structured data.
Build a monitoring cadence with tracked prompts and scoring rubrics. Prioritize content formats AI systems favor: clear definitions, tables, authoritative guides, and third-party validation. Coordinate with link building and PR for earned references that appear in training and retrieval contexts. Report share-of-model trends alongside classic SEO metrics.
Teams ask how AEO differs from SEO. This skill explains overlap (quality content, authority) and divergence (prompt-level visibility, citation parsing, recency in RAG). Another question is whether to optimize for ChatGPT versus Perplexity; workflows cover platform-agnostic signals with optional platform-specific prompt sets.
Measurement is a common blocker—how do you track AI visibility without official APIs everywhere? The skill outlines repeatable manual and semi-automated monitoring, proxy metrics, and integration with the Prompt Picker and SEO Reporting skills for unified dashboards.
Enable this skill on a task where you define your brand, competitors, and priority topics. Paste sample AI answers or monitoring notes if you have them. Metaflow produces a visibility baseline, gap analysis, and prioritized action list spanning content, technical, and PR levers. Iterate weekly by re-running prompt checks and updating the content roadmap as citations improve.
For broader context, see our roundup of claude skills for marketing, and read Claude skills for SEO for related setup guidance.