AI Marketing Claude

Abstract

1. Introduction

A comprehensive marketing analysis and automation skill system for Claude Code. Audit any website's marketing, generate copy, build email sequences, create content calendars, analyze competitors, and produce client-ready PDF reports — all from your terminal.

2. Objective

AI Marketing Suite for Claude Code. 15 marketing skills with parallel subagents — audit any website, generate copy, email sequences, ad campaigns, content calendars, competitive intelligence, and client-ready PDF reports.

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/zubair-trabzada/ai-marketing-claude.git
cd ai-marketing-claude

curl -fsSL https://raw.githubusercontent.com/zubair-trabzada/ai-marketing-claude/main/install.sh | bash

git clone https://github.com/zubair-trabzada/ai-marketing-claude.git
cd ai-marketing-claude
./install.sh

./uninstall.sh

rm -rf ~/.claude/skills/market*
rm -f ~/.claude/agents/market-*.md

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 zubair-trabzada 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.