danielhafezi/cv_enhancer

JobMatchCV is an innovative multi-agent system that leverages the AutoGen framework to enhance CVs based on specific job advertisements, providing a tailored markdown output.

JobMatchCV: AI-Powered Resume Optimizer

JobMatchCV is an innovative multi-agent system that leverages the AutoGen framework to enhance CVs based on specific job advertisements, providing a tailored markdown output.

Table of Contents

• Overview
• Key Features
• Installation
• Usage
• Customization
• Contributing
• License

Overview

JobMatchCV employs a sophisticated multi-agent system to analyze, enhance, and format CVs:

• User Input Agent: Manages initial input and text extraction from CVs and job ads.
• CV Analysis Agent: Examines CV structure, content, and formatting.
• Job Analysis Agent: Dissects job requirements and desired qualifications.
• ATS Standards Agent: Provides expertise on Applicant Tracking System standards.
• CV Enhancement Agent: Generates improvement suggestions based on job-CV comparison.
• User Output Agent: Presents enhancement suggestions clearly and actionably.
• Markdown Conversion Agent: Transforms the enhanced CV into a professional markdown document.

Key Features

• Multi-agent collaboration for comprehensive CV optimization
• Support for PDF and DOCX input formats
• Web scraping for job description extraction
• ATS-compliant enhancement suggestions
• Automatic conversion to markdown format
• Final output saved as cv.md

Usage

1. Open the JobMatchCV.ipynb Jupyter notebook.
2. Follow the step-by-step instructions to:
   • Set up your environment and API keys
   • Upload your CV (PDF or DOCX)
   • Provide the job advertisement link
   • Initiate the CV enhancement process
3. Retrieve your optimized CV as a markdown file (cv.md)

Customization

JobMatchCV offers flexibility in model selection:

• Default: GPT-4 for analysis, Claude 3.5 Sonnet for enhancements
• Alternative options: Gemini, Mistral, and Codestral

Adjust the llm_config settings in the notebook to experiment with different models.

Contributing

We welcome contributions! For suggestions or new features, please open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.