SEO Promt Master

Abstract

1. Introduction

🔍 Google SEO's full docs as an AI prompt machine — drop it into your AI assistant to map every public route, audit each page against Google's rules, and fix the gaps step by step.

2. Objective

🔍 Google SEO's full docs as an AI prompt machine — drop it into your AI assistant to map every public route, audit each page against Google's rules, and fix the gaps step by step.

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

4. Technology Stack

• One-way hreflang — a set that is not reciprocal is discarded in full, so every language in the cluster loses the signal. Only visible by comparing pages against each other.
• Streamed metadata — frameworks that flush early and emit later. Browsers hoist it; bots that stop reading at see an untitled page.

5. System Requirements

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

• Node.js (LTS) and npm
• A modern web browser
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/umutxyp/Seo-Promt-Master.git
cd Seo-Promt-Master

git clone https://github.com/umutxyp/Seo-Promt-Master.git
cd /path/to/your-project
bash /path/to/Seo-Promt-Master/install.sh

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 umutxyp 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.