SEO Content

Content Quality & E-E-A-T Analysis

Google's "Who / How / Why" Test (canonical heuristic)

Before scoring E-E-A-T sub-factors, every page audit should pass Google's own three-question heuristic from the helpful-content guide:

Primary source: https://developers.google.com/search/docs/fundamentals/creating-helpful-content

When all three answers are weak, the page is at risk under the core ranking system's helpfulness signals (formerly the standalone Helpful Content System, merged into core during the March 2024 update).

E-E-A-T Framework (updated Sept 2025 QRG)

Read skills/seo/references/eeat-framework.md for full criteria.

Experience (first-hand signals)

• Original research, case studies, before/after results
• Personal anecdotes, process documentation
• Unique data, proprietary insights
• Photos/videos from direct experience

Expertise

• Author credentials, certifications, bio
• Professional background relevant to topic
• Technical depth appropriate for audience
• Accurate, well-sourced claims

Authoritativeness

• External citations, backlinks from authoritative sources
• Brand mentions, industry recognition
• Published in recognized outlets
• Cited by other experts

Trustworthiness

• Contact information, physical address
• Privacy policy, terms of service
• Customer testimonials, reviews
• Date stamps, transparent corrections
• Secure site (HTTPS)

Content Metrics

Word Count Analysis

Compare against page type minimums:

Important: These are topical coverage floors, not targets. Google has confirmed word count is NOT a direct ranking factor. The goal is comprehensive topical coverage; a 500-word page that thoroughly answers the query will outrank a 2,000-word page that doesn't. Use these as guidelines for adequate coverage depth, not rigid requirements.

Readability

• Flesch Reading Ease: target 60-70 for general audience

Note: Flesch Reading Ease is a useful proxy for content accessibility but is NOT a direct Google ranking factor. John Mueller has confirmed Google does not use basic readability scores for ranking. Yoast deprioritized Flesch scores in v19.3. Use readability analysis as a content quality indicator, not as an SEO metric to optimize directly.

• Grade level: match target audience
• Sentence length: average 15-20 words
• Paragraph length: 2-4 sentences

Keyword Optimization

• Primary keyword in title, H1, first 100 words
• Natural density (1-3%)
• Semantic variations present
• No keyword stuffing

Content Structure

• Logical heading hierarchy (H1 -> H2 -> H3)
• Scannable sections with descriptive headings
• Bullet/numbered lists where appropriate
• Table of contents for long-form content

Multimedia

• Relevant images with proper alt text
• Videos where appropriate
• Infographics for complex data
• Charts/graphs for statistics

Internal Linking

• 3-5 relevant internal links per 1000 words
• Descriptive anchor text
• Links to related content
• No orphan pages

External Linking

• Cite authoritative sources
• Open in new tab for user experience
• Reasonable count (not excessive)

AI Content Assessment (Sept 2025 QRG addition)

Google's raters assess low-quality, scaled, copied, or AI-generated main content patterns rather than AI authorship as a standalone issue.

Acceptable AI Content

• Demonstrates genuine E-E-A-T
• Provides unique value
• Has human oversight and editing
• Contains original insights

Low-Quality AI Content Markers

• Generic phrasing, lack of specificity
• No original insight
• Repetitive structure across pages
• No author attribution
• Factual inaccuracies

Helpful Content System (March 2024): The Helpful Content System was merged into Google's core ranking algorithm during the March 2024 core update. It no longer operates as a standalone classifier. Helpfulness signals are now weighted within every core update. The same principles apply (people-first content, demonstrating E-E-A-T, satisfying user intent), but enforcement is continuous rather than through separate HCU updates. Google now also documents continuous, smaller unannounced core updates between major ones (changelog 2025-12-09).

Gen-AI optimization is SEO (Google docs, 2026-06-29): the official "optimizing for generative AI features" guide states you do not need new AI files, markup, Markdown, content chunking, or AI-specific rewrites; chasing inauthentic "mentions" is unhelpful. AEO/GEO is rebranded SEO rooted in core ranking/quality.

Honest scoping (Google docs, 2026-06-05): per "Using third-party SEO tools, services, and advice," no tool guarantees rankings and third-party tools have no access to Google's internal ranking data. claude-seo's scores are heuristics, not Google-internal signals, so say so in reports, and validate GEO/AEO findings against Google's official guidance (Search Console is the first-party source).

Draft Cleanup: AI Phrasing & Invisible Watermarks

For "humanize this", "remove watermarks", or "clean up this draft", run the bundled cleanup script on the user's own content:

"${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run content_humanize.py draft.md -o cleaned.md
cat draft.md | "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run content_humanize.py --json

Two deterministic passes, both logged in the JSON output:

1. Invisible characters (invisible_removed): strips zero-width codepoints, directional marks/overrides, Unicode tag characters (hidden text smuggling), and normalizes exotic spaces. Emoji sequences (ZWJ, variation selectors next to emoji) are preserved.
2. AI-typical phrasing (changes): conservative 1:1 swaps from the replacement table ("delve into" → "explore", etc.). Nothing is paraphrased or added.

Scope honesty: statistical watermarks (SynthID-style token-probability schemes) live in word choice, not codepoints. No tool reliably detects or removes them; do not claim otherwise in reports. This cleanup is for editing the user's own drafts, not for laundering third-party content — decline requests to strip provenance from content the user doesn't own.

AI Citation Readiness (GEO signals)

Optimize for AI search engines (ChatGPT, Perplexity, Google AI Overviews):

• Clear, quotable statements with statistics/facts
• Structured data (especially for data points)
• Strong heading hierarchy (H1->H2->H3 flow)
• Answer-first formatting for key questions
• Tables and lists for comparative data
• Clear attribution and source citations

AI Search Visibility & GEO (2025-2026)

Google AI Mode is Google's conversational AI search surface. Google's last official model naming for AI Mode / AI Overviews is a custom version of Gemini 2.5. Treat third-party AI Mode usage, citation, and link-share figures as methodology-dependent unless primary-sourced, and optimize for both AI Mode and AI Overviews (see the seo-geo skill).

Key optimization strategies for AI citation:

• Structured answers: Clear question-answer formats, definition patterns, and step-by-step instructions that AI systems can extract and cite
• First-party data: Original research, statistics, case studies, and unique datasets are highly cited by AI systems
• Schema markup: Article and other relevant structured content. FAQPage no longer produces Google FAQ rich results; use QAPage only for genuine user Q&A where appropriate
• Topical authority: AI systems preferentially cite sources that demonstrate deep expertise. Build content clusters, not isolated pages
• Entity clarity: Ensure brand, authors, and key concepts are clearly defined with structured data (Organization, Person schema)
• Multi-platform tracking: Monitor visibility across Google AI Overviews, AI Mode, ChatGPT, Perplexity, and Bing Copilot, not just traditional rankings. Treat AI citation as a standalone KPI alongside organic rankings and traffic.

Generative Engine Optimization (GEO): Per Google's AI optimization guide, "AEO" and "GEO" are rebranded labels for SEO: AI Overviews and AI Mode are grounded in the same ranking and quality systems as classic Search. The optimization signals that matter (quotability, attribution, heading hierarchy, freshness) are SEO fundamentals applied to AI-search surfaces, not a separate discipline. Cross-reference the seo-geo skill for detailed workflows; both surfaces share the primary-source synthesis in skills/seo-geo/references/google-ai-optimization-guide.md.

Content Freshness

• Publication date visible
• Last updated date if content has been revised
• Flag content older than 12 months without update for fast-changing topics

Output

Content Quality Score: XX/100

E-E-A-T Breakdown

Weights are this skill's own scoring model, ordered to reflect Google's stated hierarchy: Trust is most important (30), then Expertise/ Authoritativeness (25 each), then Experience (20); maxima sum to 100. Google publishes no numeric E-E-A-T weights (only that trust is most important), so treat the split as our internal model. Do not use an equal 25/25/25/25 split (it contradicts Google's "trust is most important").

AI Citation Readiness: XX/100

Issues Found

Recommendations

DataForSEO Integration (Optional)

If DataForSEO MCP tools are available, use kw_data_google_ads_search_volume for real keyword volume data, dataforseo_labs_bulk_keyword_difficulty for difficulty scores, dataforseo_labs_search_intent for intent classification, and content_analysis_summary for content quality analysis.

Error Handling

FLOW Framework Integration

For prompt-guided content optimization, use /seo flow optimize <url> and /seo flow win <url>: FLOW's optimize and win prompts provide structured E-E-A-T improvement and BOFU conversion workflows.