seo▌

Full-site and single-page SEO analysis with technical audits, content quality assessment, schema validation, and AI search optimization. \n \n Orchestrates 12 specialized sub-skills across technical SEO, E-E-A-T content evaluation, Core Web Vitals (INP), schema markup detection and generation, and image optimization \n Detects business type (SaaS, local service, e-commerce, publisher, agency) and applies industry-specific quality gates and scoring methodology \n Analyzes AI search readiness incl

Detect from page signals before analysis. This determines which checks apply.

✅ Integration pages (with real setup docs) ✅ Template/tool pages (with downloadable content) ✅ Glossary pages (200+ word definitions) ✅ Product pages (unique specs, reviews) ✅ User profile pages (user-generated content)

Structured technical SEO diagnostic framework organized by crawlability, indexability, renderability, and signals. \n \n Diagnoses four interdependent layers in order: crawlability (robots.txt, sitemaps, site architecture, server response), indexability (meta robots, canonical tags, duplicate content), renderability (JavaScript rendering, content visibility), and signals (titles, descriptions, headings, structured data, hreflang) \n Provides specific checklists for each diagnostic area, includin

Comprehensive technical SEO audits covering crawlability, indexability, Core Web Vitals, and site health. \n \n Analyzes eight core areas: robots.txt and sitemaps, index status and blockers, Core Web Vitals (LCP/CLS/INP/TTFB), mobile-friendliness, HTTPS/security headers, URL structure and redirects, structured data validation, and hreflang implementation \n Produces a scored technical health report (0–100) with critical/high/medium issue triage, affected page counts, and specific data points for

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Create data-driven content briefs via inference.sh CLI.

Guides entity-based SEO—making your brand, product, and authors recognizable as distinct entities in search engines' knowledge systems. Google moved from keyword-matching to meaning-based understanding (Hummingbird, RankBrain, BERT, MUM); entity understanding is central to how search processes queries. Content structured around entities can receive ~3.2× more visibility in AI-powered search. References: Semrush, Search Engine Land.

You are a keyword cannibalization specialist analyzing content overlap between provided pages.

Search engine optimization based on Lighthouse SEO audits and Google Search guidelines. Focus on technical SEO, on-page optimization, and structured data.