Building a Custom EEAT Audit Script with Claude Code

1. The Algorithmic Enforcement of E-E-A-T

Following Google's recent core algorithm updates and the widespread deployment of the Helpful Content System, generic, commoditized content generated by un-reviewed AI pipelines has suffered catastrophic ranking collapses. Search engines have elevated E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) from an advisory guideline for human Quality Raters into an algorithmically quantified ranking factor. Websites that publish un-authored articles, hide corporate ownership, omit editorial revision policies, or summarize products without proving first-hand testing are systematically demoted. Conversely, domains with verified author credentials, visible testing methodologies, and rich schema markup capture disproportionate SERP real estate. To audit hundreds of enterprise URLs at scale, manual checklists are insufficient. By utilizing Claude Code—Anthropic's terminal-native autonomous developer tool—we can build and deploy a Custom E-E-A-T Audit CLI Tool in Python.

2. The Technical E-E-A-T Audit Metric Framework

| E-E-A-T Dimension | Auditable Technical Signals | Script Scoring Mechanism | | :—- | :—- | :—- | | Experience (First-Hand) | Original testing language ('we tested', 'our benchmark'), original images | Regex experiential phrasing density + media EXIF analysis | | Expertise (Credentials) | Named author byline, author bio page URL, Person schema | HTML byline selector verification + JSON-LD author schema parser | | Authoritativeness (Reputation) | External citations to primary academic/governmental sources | Outgoing external link audit filtering for trusted authority TLDs | | Trustworthiness (Transparency) | HTTPS, public editorial policy, physical address, corrections policy | Status code 200 checks on /editorial-guidelines/ & /contact/ |

3. Leveraging Claude Code for Terminal-Native Tool Development

Claude Code transforms script creation into a rapid conversational loop. By passing the prompt: claude "Build a Python CLI script that audits URLs for E-E-A-T signals. It must parse JSON-LD Person schema, check for experiential language markers, verify author bio links, and output a structured JSON quality scorecard." Claude Code inspects your local repository, creates the Python modules, configures virtual environments, runs test URLs, and refactors edge cases automatically.

4. Parsing JSON-LD Schema: The Machine-Readable Trust Layer

Search crawlers don't just read visible HTML; they validate JSON-LD structured data. The script extracts schema.org/Person tags, verifying that the sameAs attribute contains verifiable links to external professional profiles (LinkedIn, Google Scholar, Twitter), mathematically connecting the author to established entity graphs.

5. Production Python Implementation: Complete E-E-A-T Auditor CLI

Below is a production-grade Python script that crawls technical articles and generates an objective E-E-A-T compliance score.

Production Code / Implementation Architecture

import json import re from typing import Dict, Any, List class EEATAuditEngine: def __init__(self): self.experience_markers = [ "in our testing", "we benchmarked", "in my experience", "we deployed", "our tests showed", "hands-on review", "we measured", "in our lab" ] def audit_article(self, html_text: str, json_ld_data: List[Dict[str, Any]]) -> Dict[str, Any]: text_lower = html_text.lower() # 1. Experience Check exp_matches = sum(text_lower.count(marker) for marker in self.experience_markers) has_experience_signals = exp_matches >= 2 # 2. Expertise & Author Schema Check has_author_schema = False author_name = "UNKNOWN" has_external_same_as = False for block in json_ld_data: if block.get("@type") == "TechArticle" or block.get("@type") == "Article": author = block.get("author", {}) if isinstance(author, dict) and author.get("@type") == "Person": has_author_schema = True author_name = author.get("name", "UNNAMED") if author.get("sameAs"): has_external_same_as = True # 3. Trust Scoring (0 to 100) score = 0 if has_experience_signals: score += 35 if has_author_schema: score += 35 if has_external_same_as: score += 30 return { "author_detected": author_name, "has_experience_phrasing": has_experience_signals, "experience_marker_count": exp_matches, "person_schema_valid": has_author_schema, "external_profiles_linked": has_external_same_as, "overall_eeat_score": score, "status": "EEAT_COMPLIANT" if score >= 70 else "AT_RISK_OF_HELPFUL_CONTENT_PENALTY" } if __name__ == "__main__": engine = EEATAuditEngine() mock_html = ( "

Local LLM Benchmark

By Rajesh Arora " "

In our testing, we deployed Qwen 2.5 on an RTX 4090 and we benchmarked inference latency. " ) mock_json_ld = [{ "@type": "TechArticle", "author": { "@type": "Person", "name": "Rajesh Arora", "sameAs": ["https://linkedin.com/in/rajesharora", "https://x.com/rajesharora"] } }] report = engine.audit_article(mock_html, mock_json_ld) print("[SUCCESS] E-E-A-T Compliance Audit Result:") print(json.dumps(report, indent=2))