geo-contentlisted

# GEO content quality and E-E-A-T assessment ## Purpose AI search platforms do not just find content — they evaluate whether content deserves to be cited. The primary framework for this evaluation is **E-E-A-T** (Experience, Expertise, Authoritativeness, Trustworthiness), which per Google's December 2025 Quality Rater Guidelines update now applies to **ALL competitive queries**, not just YMYL topics. Content that scores high on E-E-A-T is dramatically more likely to be cited by AI platforms. Two lenses: 1. **E-E-A-T signals** — does the content demonstrate real expertise and trust? 2. **AI citability** — is the content structured so AI platforms can extract and cite specific claims? ## Operational protocol 1. Fetch the target page(s) — homepage, key blog posts, service/product pages 2. Evaluate E-E-A-T across the 4 dimensions (25 points each) using rubrics in `signals.md` 3. Assess content quality metrics (word count, readability, paragraph/heading structure, internal linking) using `scoring.md` 4. Check for low-quality AI content signals (see `signals.md`) 5. Evaluate content freshness and topical authority modifier (`scoring.md`) 6. Score and generate `GEO-CONTENT-ANALYSIS.md` using the template in `templates.md` ## References - `signals.md` — per-signal scoring rubrics for all 4 E-E-A-T dimensions, plus AI content quality signals (low/high) - `scoring.md` — word count benchmarks, readability, paragraph/heading/linking rules, freshness scoring, topical authority modi