What It Does
The output is a detailed `GEO-CONTENT-ANALYSIS.md` report with dimension-by-dimension breakdowns, flagged weaknesses, and prioritized improvement recommendations.
It scores pages across Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) — each weighted at 25 points — then applies a topical authority modifier to produce a final citability score out of 100.
GEO Content Quality & E-E-A-T Assessment is a skill that evaluates web content against the frameworks AI search platforms use to decide what to cite.
Key Features
- 100-Point E-E-A-T Scoring Framework — Each of the four E-E-A-T dimensions — Experience, Expertise, Authoritativeness, and Trustworthiness — is broken into granular signals with defined point values. Every signal includes clear scoring criteria (e.g., "5 if original data, 3 if references original work, 0 if none") so scores are reproducible and actionable.
- Topical Authority Modifier — Beyond per-page scores, the skill assesses whether the entire site comprehensively covers its core topic. Sites with 20+ well-clustered pages earn up to +10 bonus points; thin sites with fewer than 5 pages receive a -5 penalty, reflecting how AI platforms favor recognized topical authorities.
- AI Content Quality Detection — The skill explicitly flags low-quality AI-generated content patterns — generic phrasing, hedging overload, filler paragraphs, and missing human voice — while also identifying high-quality signals like original data, contrarian views, and specific named examples. Assessment aligns with Google's March 2024 guidance that quality, not production method, is what matters.
- Content Freshness Assessment — Each analyzed page is checked for visible publication and modification dates, evaluated against a five-tier freshness scale from "Excellent" (updated within 3 months) to "Critical" (24+ months or no date). Evergreen content is flagged separately to avoid penalizing timeless reference material.
- Paragraph & Heading Structure Analysis — The skill evaluates whether paragraphs are self-contained units of meaning (2–4 sentences, one idea each, lead claim first) and whether heading hierarchy is correct and descriptive. Both factors directly affect whether AI platforms can extract and accurately attribute specific claims.
- Structured GEO-CONTENT-ANALYSIS.md Report — All findings are written to a single markdown file with a standardized format: score summary table, dimension-by-dimension findings, citability ratings per page, the top 5 most-citable passages, and tiered recommendations split into quick wins, content gaps, and E-E-A-T improvements.
Use Cases
- Pre-publish content audit for AI citability — Before publishing a new pillar page or guide, run it through this skill to confirm it meets E-E-A-T thresholds. The score breakdown reveals exactly which signals are missing — author credentials, original data, trust disclosures — so you can fix them before the page goes live.
- Site-wide E-E-A-T baseline assessment — Point the skill at a homepage and key content pages to generate a scored baseline across all four E-E-A-T dimensions. Use the topical authority modifier and content gap findings to build a content calendar that plugs missing subtopics and strengthens clustering.
- AI-generated content quality review — If your team uses AI writing tools, run published AI-assisted pages through the skill to detect low-quality patterns (hedging language, no specific examples, filler paragraphs) and get rewrite suggestions before the content is deprioritized by AI search platforms.
- Competitive content gap analysis — Assess your own site's topical authority score, then use the content gap recommendations to identify subtopics competitors cover that you do not. The skill flags these gaps explicitly as part of the authority assessment, giving you a prioritized list of pages to create.