Geo Content

GEO Content Quality & 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), 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:

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?

How to Use This Skill

1. Fetch the target page(s) — homepage, key blog posts, service/product pages
2. Evaluate E-E-A-T across the 4 dimensions (25% each)
3. Assess content quality metrics (structure, readability, depth)
4. Check for AI content quality signals
5. Evaluate topical authority across the site
6. Score and generate GEO-CONTENT-ANALYSIS.md

E-E-A-T Framework (100 points total)

Experience — 25 points

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:

What to flag as weak Experience:

• Content that only summarizes what other sources say without adding new perspective
• Generic advice that could apply to any situation ("It depends on your needs")
• No mention of actual usage, testing, or direct involvement
• Hedging language that suggests lack of direct knowledge ("reportedly", "supposedly", "some say")

Expertise — 25 points

Demonstrated knowledge depth and professional competence in the subject matter.

Signals to evaluate:

What to flag as weak Expertise:

• Claims without supporting evidence or sources
• Surface-level coverage of complex topics
• Misuse of technical terminology
• No visible author or author without relevant credentials
• Content that is broad and generic rather than deep and specific

Authoritativeness — 25 points

Recognition by others as a credible source on the topic.

Signals to evaluate:

What to flag as weak Authoritativeness:

• Single-topic site with no depth of coverage
• No external validation of expertise claims
• No backlinks from authoritative sources
• Claims of authority without evidence (self-proclaimed "expert")

Trustworthiness — 25 points

Signals that the content and its publisher are reliable and transparent.

Signals to evaluate:

What to flag as weak Trustworthiness:

• No contact information or physical address
• Missing privacy policy or terms
• Undisclosed affiliate links or sponsored content
• Claims that are verifiably false or misleading
• No way to contact the publisher for corrections

Content Quality Metrics

Word Count Benchmarks

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.

Readability Assessment

• Target Flesch Reading Ease: 60-70 (8th-9th grade level)
• This is NOT a direct ranking factor but affects citability — AI platforms prefer content that is clear and unambiguous
• Overly academic writing (score < 30) reduces citability for general queries
• Overly simple writing (score > 80) may lack the depth needed for expertise signals

How to estimate without a tool:

• Average sentence length: 15-20 words is ideal
• Average paragraph length: 2-4 sentences
• Presence of jargon: should be defined when first used
• Passive voice: < 15% of sentences

Paragraph Structure for AI Parsing

AI platforms extract content at the paragraph level. Each paragraph should be a self-contained unit of meaning.

Optimal paragraph structure:

• 2-4 sentences per paragraph (1-sentence paragraphs are weak; 5+ sentences are hard to extract)
• One idea per paragraph — do not mix topics within a paragraph
• Lead with the key claim — first sentence should contain the main point
• Support with evidence — remaining sentences provide data, examples, or context
• Quotable standalone — each paragraph should make sense if extracted in isolation

Heading Structure

• One H1 per page — the primary topic/title
• H2 for major sections — should represent distinct subtopics
• H3 for subsections — nested under relevant H2
• No skipped levels — do not go from H1 to H3 without an H2
• Descriptive headings — "How to Optimize for AI Search" not "Section 2"
• Question-based headings where appropriate — these map directly to AI queries

Internal Linking

• Every content page should link to 3-5 related pages on the same site
• Links should use descriptive anchor text (not "click here")
• Create a topic cluster structure: pillar page linked to/from all related subtopic pages
• Orphan pages (no internal links pointing to them) are rarely cited by AI

AI Content Assessment

AI-Generated Content Policy

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.

Signs of Low-Quality AI Content (flag these)

High-Quality Content Signals (regardless of production method)

Content Freshness Assessment

Publication Dates

• Check for visible datePublished and dateModified in both the content and structured data
• Content without dates is treated as less trustworthy by AI platforms
• Dates should be specific (January 15, 2026) not vague ("recently")

Freshness Scoring

Evergreen Indicators

Some content remains relevant regardless of age. Flag content as evergreen if:

• It covers fundamental concepts that do not change (physics, basic math, legal definitions)
• It is clearly labeled as a reference/guide for lasting concepts
• It does not contain time-dependent claims ("the latest", "currently", "in 2024")

Topical Authority Assessment

What It Is

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.

How to Assess

1. Content breadth: Does the site have multiple pages covering different aspects of its core topic?
2. Content depth: Do individual pages go deep into subtopics?
3. Topic clustering: Are pages organized into logical groups with internal linking?
4. Content gaps: Are there obvious subtopics that the site should cover but does not?
5. Competitor comparison: Do competitors cover subtopics that this site misses?

Scoring

Overall Scoring (0-100)

Score Composition

Score Interpretation

• 85-100: Exceptional — strong AI citation candidate across platforms
• 70-84: Good — solid foundation, specific improvements will increase citability
• 55-69: Average — multiple E-E-A-T gaps reducing AI visibility
• 40-54: Below Average — significant content quality and trust issues
• 0-39: Poor — fundamental content strategy overhaul needed

Output Format

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]