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Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure
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Basé sur la classification professionnelle SOC
| name | geo-content |
| description | Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure |
| version | 1.0.0 |
| author | geo-seo-claude |
| tags | ["geo","content-quality","eeat","citability","ai-content","topical-authority"] |
| allowed-tools | Read, Grep, Glob, Bash, WebFetch, Write |
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), from Google's Search Quality Rater Guidelines. E-E-A-T began as a YMYL (Your Money Your Life) lens but is now widely applied by raters well beyond YMYL topics; treat strong E-E-A-T as table stakes for any competitive query. Content that scores high on E-E-A-T is materially more likely to be cited by AI platforms. (See docs/SOURCES.md for the guidelines reference and dates.)
This skill evaluates content through two lenses:
First-hand knowledge and direct involvement with the topic. AI platforms increasingly distinguish between content that reports on a topic and content from someone who has DONE it.
Signals to evaluate:
| Signal | Points | How to Score |
|---|---|---|
| First-person accounts ("I tested...", "We implemented...") | 5 | 5 if present and specific, 3 if generic, 0 if absent |
| Original research or data not available elsewhere | 5 | 5 if original data, 3 if references original work, 0 if none |
| Case studies with specific results | 4 | 4 if detailed with numbers, 2 if general, 0 if none |
| Screenshots, photos, or evidence of direct use | 3 | 3 if authentic evidence, 1 if stock/generic, 0 if none |
| Specific examples from personal experience | 4 | 4 if specific and unique, 2 if somewhat specific, 0 if generic |
| Demonstrations of process (not just outcome) | 4 | 4 if step-by-step from experience, 2 if partial, 0 if none |
What to flag as weak Experience:
Demonstrated knowledge depth and professional competence in the subject matter.
Signals to evaluate:
| Signal | Points | How to Score |
|---|---|---|
| Author credentials visible (bio, degrees, certifications) | 5 | 5 if full credentials, 3 if basic bio, 0 if no author |
| Technical depth appropriate to topic | 5 | 5 if thorough technical treatment, 3 if adequate, 0 if superficial |
| Methodology explanation (how conclusions were reached) | 4 | 4 if clear methodology, 2 if some explanation, 0 if none |
| Data-backed claims (statistics, research citations) | 4 | 4 if well-sourced, 2 if some data, 0 if unsupported claims |
| Industry-specific terminology used correctly | 3 | 3 if accurate specialized language, 1 if basic, 0 if errors |
| Author page with detailed professional background | 4 | 4 if dedicated author page, 2 if brief bio, 0 if none |
What to flag as weak Expertise:
Recognition by others as a credible source on the topic.
Signals to evaluate:
| Signal | Points | How to Score |
|---|---|---|
| Inbound citations from authoritative sources | 5 | 5 if cited by major sources, 3 if some citations, 0 if none |
| Author quoted or cited in press/media | 4 | 4 if media mentions, 2 if industry mentions, 0 if none |
| Industry awards or recognition mentioned | 3 | 3 if relevant awards, 1 if tangential, 0 if none |
| Speaker credentials (conferences, events) | 3 | 3 if listed, 0 if none |
| Published in peer-reviewed or respected outlets | 4 | 4 if tier-1 publications, 2 if industry outlets, 0 if none |
| Comprehensive topic coverage (topical authority) | 3 | 3 if site covers topic thoroughly, 1 if some coverage, 0 if isolated |
| Brand mentioned on Wikipedia or authoritative references | 3 | 3 if Wikipedia, 2 if other encyclopedic refs, 0 if none |
What to flag as weak Authoritativeness:
Signals that the content and its publisher are reliable and transparent.
Signals to evaluate:
| Signal | Points | How to Score |
|---|---|---|
| Contact information visible (address, phone, email) | 4 | 4 if full contact info, 2 if email only, 0 if none |
| Privacy policy present and linked | 2 | 2 if present, 0 if absent |
| Terms of service present | 1 | 1 if present, 0 if absent |
| HTTPS with valid certificate | 2 | 2 if valid HTTPS, 0 if not |
| Editorial standards or corrections policy | 3 | 3 if documented, 1 if implicit, 0 if none |
| Transparent about business model and conflicts | 3 | 3 if clear disclosures, 1 if some, 0 if none |
| Reviews and testimonials from real customers | 3 | 3 if verified reviews, 1 if testimonials, 0 if none |
| Accurate claims (no misinformation detected) | 4 | 4 if all claims accurate, 2 if mostly accurate, 0 if errors found |
| Clear affiliate/sponsorship disclosures | 3 | 3 if properly disclosed, 0 if undisclosed or absent |
What to flag as weak Trustworthiness:
These are floors, not targets. More words does not mean better content. The benchmark is the minimum length to adequately cover a topic for AI citability.
| Page Type | Minimum Words | Ideal Range | Notes |
|---|---|---|---|
| Homepage | 500 | 500-1,500 | Clear value proposition, not a wall of text |
| Blog post | 1,500 | 1,500-3,000 | Thorough but focused |
| Pillar content / Ultimate guide | 2,000 | 2,500-5,000 | Comprehensive topic coverage |
| Product page | 300 | 500-1,500 | Descriptions, specs, use cases |
| Service page | 500 | 800-2,000 | What, how, why, for whom |
| About page | 300 | 500-1,000 | Company/person story and credentials |
| FAQ page | 500 | 1,000-2,500 | Thorough answers, not one-liners |
How to estimate without a tool:
AI platforms extract content at the paragraph level. Each paragraph should be a self-contained unit of meaning.
Optimal paragraph structure:
AI-generated content is acceptable per Google's guidance (March 2024 clarification) as long as it demonstrates genuine E-E-A-T signals and has human oversight. The concern is not HOW content is created but WHETHER it provides value.
| Signal | Description |
|---|---|
| Generic phrasing | "In today's fast-paced world...", "It's important to note that...", "At the end of the day..." |
| No original insight | Content that only rephrases widely available information |
| Lack of first-hand experience | No personal anecdotes, case studies, or specific examples |
| Perfect but empty structure | Well-formatted headings with shallow content beneath them |
| No specific examples | Uses abstract explanations without concrete instances |
| Repetitive conclusions | Each section ends with a variation of the same point |
| Hedging overload | "Generally speaking", "In most cases", "It depends on various factors" without specifying which factors |
| Missing human voice | No opinions, preferences, or professional judgment expressed |
| Filler content | Paragraphs that could be deleted without losing information |
| No data or sources | Claims presented as facts without attribution or evidence |
| Signal | Description |
|---|---|
| Original data | Surveys, experiments, benchmarks, proprietary analysis |
| Specific examples | Named products, companies, dates, numbers |
| Contrarian or nuanced views | Disagreement with conventional wisdom, backed by reasoning |
| First-person experience | "When I tested this..." or "Our team found..." |
| Updated information | References to recent events, current data |
| Expert opinion | Clear professional judgment, not just facts |
| Practical recommendations | Specific, actionable advice, not vague guidance |
| Trade-offs acknowledged | "This approach works well for X but not for Y because..." |
datePublished and dateModified in both the content and structured data| Criterion | Score |
|---|---|
| Updated within 3 months | Excellent — current and relevant |
| Updated within 6 months | Good — still reasonably current |
| Updated within 12 months | Acceptable — may need refresh |
| Updated 12-24 months ago | Warning — review for accuracy |
| No date or 24+ months old | Critical — AI platforms may deprioritize |
Some content remains relevant regardless of age. Flag content as evergreen if:
Topical authority measures whether a site comprehensively covers a topic rather than touching on it superficially. AI platforms prefer citing sites that are recognized authorities on their topics.
| Level | Description | Score Impact |
|---|---|---|
| Authority | 20+ pages covering topic comprehensively, strong clustering | +10 bonus |
| Developing | 10-20 pages with some clustering | +5 bonus |
| Emerging | 5-10 pages on topic, limited clustering | +0 |
| Thin | < 5 pages, no clustering | -5 penalty |
| Component | Weight | Max Points |
|---|---|---|
| Experience | 25% | 25 |
| Expertise | 25% | 25 |
| Authoritativeness | 25% | 25 |
| Trustworthiness | 25% | 25 |
| Subtotal | 100 | |
| Topical Authority Modifier | +10 to -5 | |
| Final Score | Capped at 100 |
Provenance (THL): tag the score
[scan](data fetched this run),[partial-scan],[heuristic](judgement, no data), or[unmeasured]— and emit—instead of a number when[unmeasured]or pure[heuristic]. A number with weak provenance still reads as hard data. See the GEO Method.
Generate GEO-CONTENT-ANALYSIS.md with:
# GEO Content Quality & E-E-A-T Analysis — [Domain]
Date: [Date]
## Content Score: XX/100
## E-E-A-T Breakdown
| Dimension | Score | Key Finding |
|---|---|---|
| Experience | XX/25 | [One-line summary] |
| Expertise | XX/25 | [One-line summary] |
| Authoritativeness | XX/25 | [One-line summary] |
| Trustworthiness | XX/25 | [One-line summary] |
## Topical Authority Modifier: [+10 to -5]
## Pages Analyzed
| Page | Word Count | Readability | Heading Structure | Citability Rating |
|---|---|---|---|---|
| [URL] | [Count] | [Score] | [Pass/Warn/Fail] | [High/Medium/Low] |
## E-E-A-T Detailed Findings
### Experience
[Specific passages and pages with strong/weak experience signals]
### Expertise
[Author credentials found, technical depth assessment, specific gaps]
### Authoritativeness
[External validation found, topical authority assessment, gaps]
### Trustworthiness
[Trust signals present/missing, accuracy concerns if any]
## Content Quality Issues
[Specific passages flagged with reasons and rewrite suggestions]
## AI Content Concerns
[Any low-quality AI content patterns detected, with specific examples]
## Freshness Assessment
| Page | Published | Last Updated | Status |
|---|---|---|---|
| [URL] | [Date] | [Date] | [Current/Stale/No Date] |
## Citability Assessment
### Most Citable Passages
[Top 5 passages that AI platforms are most likely to cite, with reasons]
### Least Citable Pages
[Pages with lowest citability, with specific improvement recommendations]
## Improvement Recommendations
### Quick Wins
[Specific content changes that can be made immediately]
### Content Gaps
[Topics the site should cover to strengthen topical authority]
### Author/E-E-A-T Improvements
[Specific steps to strengthen E-E-A-T signals]
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