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Eeat Audit

Community
inhouseseo
eeat-audit

Use when auditing a page for E-E-A-T signals. The agent reads the page and scores Experience, Expertise, Authoritativeness, and Trustworthiness — then tells you exactly what to add to each dimension.

Overview

Publisherinhouseseo
Repositorysuperseo-skills
Skill nameeeat-audit
Stars
365
Forks
45
Bundled files
11
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by inhouseseo on GitHub. Read the source before you install it.

Installation

Install the Eeat Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/inhouseseo/superseo-skills/tree/main/skills/eeat-audit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/inhouseseo/superseo-skills.git /tmp/superseo-skills
mkdir -p .claude/skills
cp -r /tmp/superseo-skills/skills/eeat-audit .claude/skills/eeat-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Eeat Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Eeat Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Eeat Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

E-E-A-T Audit

Scores a page on Experience, Expertise, Authoritativeness, and Trustworthiness — the four signals Google's quality raters use to evaluate content. Tells you what's missing and how to add it.

Real E-E-A-T is demonstrated, not declared. An author bio is table stakes. What matters is whether the content feels like it was written by someone who has actually done the thing.

Input

URL of the page to audit. If the fetch fails, ask the user to paste the content directly.

Role

You are a senior content quality evaluator with 10+ years reading for Google's quality rater framework. You can tell within 30 seconds of reading whether an author has done the thing they're writing about.

Step 1: Read the Page

Fetch and read the full rendered page. Note everything that could be an E-E-A-T signal:

  • Author name, bio, credentials
  • Byline with publication date
  • First-person language ("I tried...", "we found...", "when I was at...")
  • Specific anecdotes, names, numbers, dates
  • Original photos, screenshots, diagrams
  • Quoted sources, linked references
  • Schema markup (Person, Author, Organization)
  • About page linked from the article
  • External references to the author or publication

Step 2: Score Each Dimension (1-10)

Experience (the most underrated E-E-A-T factor)

What you're looking for: evidence the author has DONE the thing, not just researched it.

Strong signals (8-10):

  • First-person observations with specific details
  • "When I tried this, X happened"
  • Original photos/screenshots from the author's own work
  • Failure stories with specific lessons
  • Details only hands-on experience would know (the "smell test")
  • A story that reveals workflow, not just outcomes

Weak signals (4-6):

  • Generic advice that anyone could write after 30 minutes of research
  • Third-person narration of other people's case studies
  • Examples that feel plucked from Google
  • "According to studies..." without identifying which ones

Absent (1-3):

  • No first-person anywhere
  • No specific stories
  • No details beyond what's already on the top 10 ranking pages

Expertise

What you're looking for: accurate facts and depth beyond surface level. Verify the claims you can; score the rest on sourcing quality and say which claims you couldn't check — don't assert accuracy you haven't confirmed.

Strong signals:

  • Every factual claim is accurate and verifiable
  • Numbers cited with primary sources (original research, not "studies show")
  • Technical details correctly used (terminology, processes, edge cases)
  • Willingness to disagree with common advice when the author has a reason
  • Depth beyond what a smart generalist could produce in 30 minutes

Weak signals:

  • Accurate but shallow
  • Secondary sources cited (blog posts citing blog posts)
  • Common advice repeated without critique

Absent:

  • Factual errors
  • Outdated information presented as current
  • Surface-level "what Google says" summary

Authoritativeness

What you're looking for: does this content and author belong in the conversation?

Strong signals:

  • Page is part of a broader topical cluster on the domain
  • Author expertise is verifiable beyond a bio paragraph (LinkedIn, talks, books, citations elsewhere)
  • External sites cite this page or author
  • Clear track record on this specific topic

Weak signals:

  • Isolated page on a broad topic site
  • Generic author with no verifiable specialty
  • No external validation

Absent:

  • Random blog post with no author attribution
  • Site has no topical focus
  • No internal linking cluster

Trustworthiness

What you're looking for: transparency and honesty.

Strong signals:

  • Transparent about limitations ("this didn't work when X")
  • Discloses conflicts of interest (affiliate links, paid placements)
  • Methodology explained
  • Willingness to recommend alternatives, even competitors
  • Factually accurate throughout
  • Recent publish/update date for time-sensitive topics

Weak signals:

  • Feels like an advertorial but doesn't disclose
  • Hides limitations
  • Methodology unclear

Absent (1-3):

  • Factual errors
  • Affiliate-driven content without disclosure
  • Misleading claims
  • Outdated information on a time-sensitive topic

Step 3: Output

E-E-A-T Scorecard

SignalScoreKey Gap
Experience/10
Expertise/10
Authoritativeness/10
Trustworthiness/10
Total/40

What's Working

Specific observations. "The screenshot in Section 3 is clearly from the author's own dashboard — this is a strong Experience signal."

What's Missing

Specific gaps with specific fixes:

  • "No first-person observations in the first 500 words. Add: 'When I first tested this at [company name], the result surprised me — [specific outcome].'"
  • "The claim about 42% improvement isn't sourced. Either cite the primary study or drop the number."
  • "No author bio links to a LinkedIn or profile page. Add: a one-sentence bio with a credential anchor and an external link to the author's profile."

Fastest Wins

Three changes you could make in under 30 minutes that would lift the E-E-A-T score materially. Ordered by impact.

Structural Recommendations

Things that require more work but would fundamentally strengthen E-E-A-T: adding a methodology section, linking to related topical cluster pages, adding Author schema markup, creating an About page for the author.

What to Ignore

  • Generic "add author bio" advice — it's table stakes, not E-E-A-T
  • Schema without substance — marking up a page with Person schema when the content shows no experience is worse than no schema
  • Gaming quality rater signals — the raters aren't fooled, and the algorithm isn't either

Next Step

To apply the fixes: use the improve-content skill with this URL, and paste the gap list as context.

Bundled references

Load from references/ only when the step calls for them.

Scoring and diagnosis:

  • ymyl-scoring-rubric.md — stricter scoring rubric for Your Money Your Life pages (finance, medical, legal) where the E-E-A-T bar is materially higher (Step 2, any YMYL page)
  • experience-detection-playbook.md — how to tell in 30 seconds whether an author has done the thing (Experience dimension, when the page looks ambiguous)
  • fastest-eeat-wins.md — ranked list of the highest-impact E-E-A-T fixes by implementation effort (Step 3, "Fastest Wins" block)
  • eeat-signal-embedding.md — how to surface experience without a bio section or fake credentials (Step 3, "Structural Recommendations")
  • author-schema-templates.md — copy-paste Person / Author / Organization JSON-LD for the schema fix (Step 3)

YMYL content-type templates (references/content-types/) — load when auditing one of these types for the type-specific E-E-A-T bar:

  • thought-leadership.md, product-reviews.md, pricing-pages.md, service-pages.md, case-studies.md, about-pages.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Eeat Audit AI skill do?

Use when auditing a page for E-E-A-T signals. The agent reads the page and scores Experience, Expertise, Authoritativeness, and Trustworthiness — then tells you exactly what to add to each dimension.

Why use Eeat Audit on TypingMind?

Because you install it once and use it with any model. Eeat Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Eeat Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/inhouseseo/superseo-skills/tree/main/skills/eeat-audit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Eeat Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Eeat Audit?

As many as you like. As long as a model supports skills, you can use Eeat Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Eeat Audit AI skill free?

Yes. It is published on GitHub by inhouseseo under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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