SEO Skills

Abstract

1. Introduction

Production-ready Claude Agent Skills for SEO, powered by the SE Ranking remote MCP. Each skill turns raw API data into a finished SEO deliverable — content briefs, AI Search share of voice, page intelligence, structured data, drift monitoring, SXO diagnostics, competitive analysis, and more.

These Claude Skills are designed to work with the SE Ranking MCP server, but they document every API call explicitly so they can also be adapted to other SEO data providers.

The easiest setup — no terminal needed. Cowork in Claude Desktop installs the plugin from a marketplace through a UI flow.

2. Objective

Claude SEO Skills — production Claude Agent Skills for the SE Ranking MCP server. Content briefs, AI Search share of voice, audits, backlink gaps, keyword clusters, schema, sitemap, GEO, and more.

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

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/seranking/seo-skills.git
cd seo-skills

git clone --depth 1 https://github.com/seranking/seo-skills.git
bash seo-skills/install.sh

curl -fsSL https://raw.githubusercontent.com/seranking/seo-skills/main/install.sh | bash -s -- --all

claude mcp add --transport http se-ranking https://api.seranking.com/mcp

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 seranking 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.