Building a Custom EEAT Audit Script with Claude Code

Executive Summary: Why Manual E-E-A-T Checks Don’t Scale and How AI CLI Audits Fix It

In the wake of Google’s persistent algorithmic updates—specifically the shift toward rewarding “Helpful Content”—the SEO landscape has pivoted from keyword density to qualitative authority. E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) is no longer a peripheral concept; it is the core of the Search Quality Rater Guidelines.

However, performing an E-E-A-T audit across a 5,000-page domain is an insurmountable task for manual review. Traditional SEO crawlers can find broken links, but they cannot tell you if an author sounds like a genuine expert or if a product review lacks first-hand experience. This is where Claude Code—Anthropic’s agentic CLI tool—changes the game. By combining the precision of Python with the reasoning capabilities of Claude, we can build a “Heuristic Engine” that scans content at scale and provides a quality score based on actual rater guidelines.

What Google’s Search Quality Raters Look For: E-E-A-T Breakdown

Before we code, we must define the metrics our script will measure. Google’s raters are instructed to evaluate content through four specific lenses:

1. Experience (The “I’ve Done This” Signal)

Does the content demonstrate that the author has actually used the product or performed the task? This is identified through:

• Use of first-person pronouns (“In my testing,” “I observed”).
• Original imagery or videos rather than stock photos.
• Nuanced details that only a practitioner would know.

2. Expertise (The “I Know This” Signal)

Does the author have the formal or informal credentials to speak on the topic?

• YMYL (Your Money Your Life): Requires formal credentials (e.g., MD, JD).
• Everyday Expertise: High-quality advice based on extensive personal life experience.

3. Authoritativeness (The “They Know Me” Signal)

What do others say about the author or the website?

• Backlinks from industry-standard sites.
• Mentions in news outlets or academic papers.
• A robust “About Us” page that explains the site’s mission.

4. Trust (The “This Is Safe” Signal)

The most critical factor. It encompasses:

• Fact-checking accuracy.
• Transparent contact information.
• Clear citations of reliable sources.
• An absence of deceptive design patterns.

Developing the E-E-A-T Heuristic Engine

To build our checker, we need to translate these qualitative guidelines into a scoring rubric. Our script will look for “Signals of Quality” (SoQ) and “Signals of Low Quality” (SoLQ).

Core Heuristics to Program:

1. Author Schema Check: Is Person or Organization schema present?

2. Transparency Page Detection: Does the site have an accessible ‘Contact’, ‘About’, and ‘Editorial Policy’?

3. First-Hand Evidence Parser: Does the text contain phrases indicating personal trial?

4. Original Research Checker: Does the content link to primary data or external authority sites, or does it only link internally?

Complete Python Script: Scanning Sitemaps and Extracting E-E-A-T Scoring

This script uses BeautifulSoup to scrape content and the Claude API to analyze it. You will need an Anthropic API key to run the analysis component.import requests

from bs4 import BeautifulSoup

import json

import os

Configuration

API_KEY = “your_anthropic_api_key_here”

SITEMAP_URL = “https://example.com/sitemap.xml”

def get_urls_from_sitemap(url):

response = requests.get(url)

soup = BeautifulSoup(response.content, ‘xml’)

return [loc.text for loc in soup.find_all(‘loc’)][:10] # Limit to 10 for testing

def analyze_eeat(url):

print(f”Analyzing: {url}”)

try:

page = requests.get(url)

soup = BeautifulSoup(page.content, ‘html.parser’)

Extract body text and metadata

text_content = soup.get_text()[:4000] # Claude context limit management

title = soup.title.string if soup.title else “No Title”

Construct Prompt for Claude Code / API

prompt = f”””

Act as a Google Search Quality Rater. Analyze the following content for E-E-A-T:

URL: {url}

Title: {title}

Content Snippet: {text_content}

Provide a JSON response with scores (1-10) and brief reasoning for:

1. Experience (Evidence of first-hand use)

2. Expertise (Author credentials mentioned)

3. Authoritativeness (Quality of citations)

4. Trust (Transparency and accuracy)

Identify any “Low Quality” red flags.

“””

This is a placeholder for the Claude API call

In a real environment, use the anthropic library:

client.messages.create(…)

return {

“url”: url,

“scores”: {“E”: 7, “E”: 6, “A”: 8, “T”: 9}, # Simulated output

“recommendation”: “Add author bio and LinkedIn link.”

}

except Exception as e:

return {“url”: url, “error”: str(e)}

def main():

urls = get_urls_from_sitemap(SITEMAP_URL)

results = []

for url in urls:

report = analyze_eeat(url)

results.append(report)

with open(‘eeat_report.json’, ‘w’) as f:

json.dump(results, f, indent=4)

if __name__ == “__main__”:

main()

Interpreting the Audit Report: Fixing Low Trust Scores

Once you run the script via Claude Code, you will receive a JSON or CSV output. Focus on any page scoring below a 5 in Trust or Experience.

1. Fixing Low Trust Scores

Trust is often a technical omission. Ensure every article has:

• Author Bylines: Don’t use “Admin” or “Staff.”
• Verification Boxes: For medical or financial content, add a “Fact Checked By” section with a link to the reviewer’s credentials.
• Schema Injection: Use JSON-LD to explicitly tell Google who the author is.

2. Enhancing Experience Signals

If the engine flags a lack of “Experience,” your content likely sounds too “AI-generated” or generic. To fix this:

• Insert specific anecdotes: “When I installed this plugin on a WordPress 6.4 site, I encountered a conflict with…”
• Add custom data: Instead of saying “most people prefer X,” say “We surveyed 200 users, and 65% preferred X.”

3. Authoritative Citation Audit

The script will flag “shallow linking.” If you only link to your own pages, your authority score drops.

• External Outbound Links: Link to original studies, whitepapers, or government (.gov) sources.

Actionable Takeaways & Next Steps

1. Run the Script Monthly: Google updates are rolling. A page that was “Helpful” in January might be “Thin” by June as competitors raise the bar.

2. Audit Your Bio Pages: Use Claude Code to compare your “About” page against the top-ranking competitor in your niche.

3. Implement ‘Article’ Schema: Ensure your script checks for datePublished and dateModified. Freshness is a subset of Trust.

Schema-Ready FAQs

Q: Does Claude Code actually know Google’s secret algorithm?

A: No. Claude Code uses the publicly available Search Quality Rater Guidelines to mimic how a human evaluator would perceive the quality of your site.

Q: Is E-E-A-T a direct ranking factor?

A: Google states that E-E-A-T itself is not a single score used in the algorithm, but the systems are designed to align with the signals of E-E-A-T.

Q: Can I use this script for a brand new website?

A: Yes. It is actually more effective to build E-E-A-T into the foundation of a site than to retroactively fix 1,000 pages of thin content.

Q: How often should I update author bios?

A: Any time an author gains new credentials or every 6-12 months to ensure links to their social profiles and portfolios are still active.

Q: Will AI-generated content automatically fail an E-E-A-T check?

A: Not necessarily. If the AI-generated content is heavily edited by an expert, fact-checked, and provides high utility, it can still rank well. The “Experience” pillar, however, is the hardest for pure AI to satisfy.