Comprehensive SEO Analyzer Agent Skill

Abstract

1. Introduction

A production-grade, open-source SEO audit engine that evaluates websites across three pillars — Technical SEO, Content SEO, and Backlink/Authority — against named search-quality standards including Google Search Essentials, Core Web Vitals, EEAT, schema.org, and Google's spam policies.

The analyzer scores ranking potential, rejects manipulative (black-hat) tactics, and emits a prioritized white-hat optimization roadmap.

2. Objective

🔍📈 Comprehensive SEO Analyzer & Optimizer | Production-grade SEO audit engine scoring Technical, Content (EEAT), and Backlink vectors against Google Search Essentials and Core Web Vitals using a crawl4ai pipeline.

This project demonstrates how Python can be applied to a real-world Digital Marketing & SEO problem.

3. Key Features / Modules

• Three-pillar scoring with weighted aggregation.
• Page-anchored findings: every issue is tied to a specific page or resource.
• Standards-grounded: Google Search Essentials, Core Web Vitals (LCP/INP/CLS), EEAT, schema.org, spam policies.
• White-hat quality gate: automatically rejects cloaking, PBNs, keyword stuffing, link schemes, and AI-spam-at-scale.
• Prioritized roadmap: ranks fixes by impact × effort.
• Knowledge pipeline: knowledge_updater.py appends deduplicated updates from Search Central, web.dev, schema.org, and ArXiv cs.IR.
• CLI + library: use as a command-line tool or embed in your own Python services.
• Fully typed & tested: Pydantic models, pytest suite, ≥85% coverage target.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/dungnotnull/comprehensive-seo-analyzer-agent-skill.git
cd comprehensive-seo-analyzer-agent-skill

# Install
pip install -e ".[dev,crawl]"

# Run an audit
seo-audit https://example.com --output report.md

# Or via module
python -m comprehensive_seo_analyzer https://example.com --output report.md

# Run tests
pytest

# Update knowledge brain (dry-run)
python tools/knowledge_updater.py --dry-run

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Export reports to Google Sheets or PDF
• Schedule weekly automated reports
• Add competitor comparison

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which marketing or SEO problem does this tool solve?
2. Which data sources or APIs does it use (Search Console, Analytics, social platforms)?
3. Which metrics or KPIs does it report and how are they calculated?
4. How could the output help a business make decisions?
5. How would you schedule it to run automatically?

9. Source Code & License

This project is developed by dungnotnull and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.