Content E-E-A-T Review

Attribution: Original skill by AgriciDaniel, MIT licensed. Cendis hosts this entry for discovery and install convenience.

What it does

Reviews content the way a Google Quality Rater would, against the 2025 Quality Rater Guidelines. Scores each page on Experience, Expertise, Authoritativeness, and Trust signals; flags thin content; checks readability and structure; and evaluates whether the content is “citable” by AI Overviews and ChatGPT-style assistants.

When to use it

• Auditing a content library before a major SEO push
• Reviewing AI-generated content before publishing
• Diagnosing why a page with high keyword relevance still doesn’t rank
• Preparing content to be cited by AI search (GEO — Generative Engine Optimization)

What it evaluates

Experience signals

• First-hand evidence (screenshots, original photos, “I tested X and found Y”)
• Author bio with proven background in the topic
• Specific dates, costs, brands, model numbers — not vague generalities

Expertise signals

• Author credentials visible on the page
• Author archive page with related work
• Schema.org Person and Article.author markup
• Citations to primary sources (papers, official docs, original research)

Authoritativeness signals

• Inbound links from topical authorities
• Mentions of the author/site on other authoritative sites
• Wikipedia or knowledge-graph presence

Trust signals

• HTTPS, clear ownership, contact information
• Editorial policy, corrections policy, fact-checking process
• Distinction between opinion, sponsored, and reported content

Readability + structure

• Reading level appropriate for the audience
• Scannable structure (H2/H3, lists, callouts)
• Average sentence length, paragraph length
• Passive voice ratio

AI citation readiness

• Self-contained passages that answer a question in 50–100 words
• Direct answers near the top of relevant sections
• Schema markup that helps LLMs understand the page

Output

A scored review per page (0-100 across each E-E-A-T axis), a thin-content flag list, and a prioritized list of edits. For multi-page audits, a portfolio-level summary identifying which pages most need attention.

Why it matters

Search and AI both reward content that demonstrates real expertise and lived experience — and both punish content that reads like it was written without one. E-E-A-T is no longer a nice-to-have; it’s the primary lever separating pages that get cited from pages that get ignored. This skill turns a fuzzy quality concept into a concrete, fixable checklist.

License

MIT — original work by AgriciDaniel. Attribution preserved.