What is E-E-A-T analysis?

E-E-A-T analysis evaluates content quality based on Expertise, Authoritativeness, Trustworthiness, and Experience signals, ensuring it meets standards for search and answer-engine optimization.

What does content quality mean?

Content quality refers to the assessment of factors such as E-E-A-T signals, readability, and AI citation readiness, which are crucial for optimizing search and answer-engine performance.

What is the content strategy of E-E-A-T?

The content strategy of E-E-A-T focuses on auditing content quality, evaluating expertise, authoritativeness, and trustworthiness signals, ensuring readability, and preparing for AI citation readiness to optimize for search and answer-engine performance.

What is the difference between "eat" and "eeat"?

The context does not explicitly define the difference between 'eat' and 'eeat'. However, 'E-E-A-T' refers to Expertise, Authoritativeness, Trustworthiness, which are signals evaluated in content audits for quality and optimization.

What are the 4 types of analysis?

The four types of analysis are content quality audits, E-E-A-T signals analysis, readability assessments, and AI citation readiness evaluations for search and answer-engine optimization.

Skill

Analyze Content Quality and E-E-A-T Signals

Name: Content Quality & E-E-A-T Analysis
Availability: OnlineOnly
Author: Antigravity

Audits content quality, readability, and E-E-A-T signals, scoring both search ranking and AI citation readiness.

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Works with dataforseo

Antigravity

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Spark score

out of 100

Updated 20 days ago

Version 14.1.0

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Why it matters

Audit content quality, readability, and E-E-A-T signals to ensure trustworthiness and suitability for search engines and AI citation.

Outcomes

What it gets done

Assess content against E-E-A-T framework (Experience, Expertise, Authoritativeness, Trustworthiness).

Analyze content metrics including word count, readability, and keyword optimization.

Evaluate content structure, multimedia usage, and internal/external linking.

Determine AI content quality and readiness for AI citation.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-seo-content | bash

Overview

Content Quality & E-E-A-T Analysis

A content-focused SEO audit scoring E-E-A-T signals, readability, structure, and word-count coverage, plus a separate AI citation readiness score for GEO-era search engines. Use for a content-specific quality review rather than a full technical SEO audit, especially to check trustworthiness signals and AI citation readiness.

What it does

Content Quality & E-E-A-T Analysis audits content quality, readability, thin-content risk, and E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) signals, per Google's September 2025 Quality Rater Guidelines, producing a Content Quality Score and a separate AI Citation Readiness score.

When to use - and when NOT to

Use this when auditing content quality, readability, thin content risk, or E-E-A-T signals, when the user wants a content-focused SEO review rather than a full technical audit, or when checking whether content is structured and trustworthy enough for both search ranking and AI citation.

Inputs and outputs

Scores four E-E-A-T pillars, 25 points each toward a 100-point score: Experience (original research, case studies, before/after results, personal anecdotes, unique data, first-hand photos/video); Expertise (author credentials, professional background, technical depth, well-sourced claims); Authoritativeness (external citations, backlinks from authoritative sources, brand mentions, being cited by other experts); and Trustworthiness (contact info, privacy policy, testimonials, date stamps, transparent corrections, HTTPS). It also checks word count against page-type floors - homepage 500, service page 800, blog post 1,500, product page 300+ or 400+ for complex products, location page 500-600 - explicitly framed as topical-coverage floors, not ranking targets, since Google has confirmed word count isn't a direct ranking factor; readability (Flesch Reading Ease 60-70 as a quality proxy, not a ranking metric; 15-20 word average sentences; 2-4 sentence paragraphs); keyword placement and natural density of 1-3% with no stuffing; heading hierarchy and scannability; multimedia use; internal linking density of 3-5 relevant links per 1000 words; and external citation quality. A separate AI-generated-content check looks for genuine E-E-A-T and human editing as acceptable signals versus generic phrasing, repetitive structure, and no author attribution as low-quality markers, and flags content freshness by publication/update date, calling out anything over 12 months stale on fast-changing topics.

Integrations

Covers AI citation readiness (GEO) for ChatGPT, Perplexity, and Google AI Overviews/AI Mode - quotable statements, structured data, answer-first formatting, tables for comparative data, clear source attribution - and cross-references the separate seo-geo skill for deeper GEO workflows. It optionally uses DataForSEO MCP tools when available: kw_data_google_ads_search_volume for keyword volume, dataforseo_labs_bulk_keyword_difficulty for difficulty, dataforseo_labs_search_intent for intent classification, and content_analysis_summary for content-quality analysis. It defines explicit error handling for an unreachable URL (report the error, don't guess), paywalled content (analyze only the visible meta tags/headers and note the limitation), and thin retrievable content under 100 words (report as-is and flag possible JS-rendering or gating rather than guessing).

Who it's for

SEO or content teams who need a content-specific quality and trustworthiness audit, distinct from a full technical SEO audit, covering both traditional ranking signals and AI-search citation readiness across Google AI Mode, ChatGPT, Perplexity, and Bing Copilot.

FAQ

Common questions

Discussion