SEO Analyzer

Abstract

1. Introduction

The analyzer is split into focused subpackages: on_page, technical, content, scoring, and site_audit. Each module exposes a small, well-defined surface and can be extended independently. The CLI supports both single-page analysis and site-wide crawling with concurrency and filters. Results are JSON-first with optional CSV exports for pages, issues, and link edges.

A modern, modular SEO analysis toolkit for Python. Run focused page-level audits or full site crawls, capture technical/content issues with clear severities, and export structured data for reporting. Built with extensibility in mind and designed for practical, actionable insights.

2. Objective

Powerful SEO Analyzer for comprehensive on-page, technical, and content audits. Features CLI & Flask API. Ideal for developers & SEOs. #Python #SEO

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

3. Key Features / Modules

• On-page, Technical, and Content analyzers with unified scoring
• Full Site Audit (Ahrefs-style) with concurrency, filtering, and exports
• LLM/AI directives checklist (llms.txt / ai.txt)
• Optional Lighthouse/CrUX metrics via PageSpeed Insights API
• Duplicate detection across titles, descriptions, and visible text
• Link graph, redirect chains/loops, status distribution, and internal link suggestions
• REST API (Flask) and rich CLI with mobile-first and JS rendering options
• On-Page Analysis
• Title/meta description presence and lengths, duplication hints
• Heading structure (H1–H6), multiple H1 detection

4. Technology Stack

• pyspellchecker: content spell checks
• dnspython: SPF lookup
• Pillow: optional image-related utilities
• flask: API mode
• playwright: optional JS rendering for discovery (--render-js)
• PageSpeed Insights: requires Google API key (enable_pagespeed_insights)

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/ihuzaifashoukat/seo-analyzer.git
cd seo-analyzer

1. Python 3.8+
2. Optional: python -m venv venv && source venv/bin/activate
3. pip install -r requirements.txt
4. Optional dependencies:
5. Playwright (JS rendering): pip install playwright && playwright install
6. PSI (Lighthouse/CrUX): needs a Google API key (config below)
7. python app.py https://www.example.com
8. Saves report to reports/seo_report.json

python app.py https://www.example.com \
  --full-audit --max-pages 200 --max-depth 3 \
  --respect-robots --rate-limit 1.5 --workers 6 --mobile \
  --export-csv reports/example_audit \
  --include-path /blog --exclude-path re:^/admin --render-js

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 ihuzaifashoukat 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.