AI-Driven Resume and Cover Letter Generator

Disclaimer: This implementation is for educational purposes and may require further development for real-world applications.

This project is a multi-agent AI platform that generates tailored resumes and cover letters based on job descriptions and user input, leveraging CrewAI for agent collaboration.

---

Installation and Usage

1. Clone the Repository

   git clone https://github.com/mattc-try/Letter-Resume_MultiagentAssistant.git
   cd Letter-Resume_MultiagentAssistant

2. Create a Virtual Environment

   python3 -m venv venv
   source venv/bin/activate

3. Install Dependencies

   pip install -r requirements.txt

4. Download NLP Models

   python -m spacy download en_core_web_sm

5. Set the OpenAI API Key Export your OpenAI API key as an environment variable (get your own not a charity, the one in old commits was obviously deleted):

   export OPENAI_API_KEY="your_openai_api_key_here"

   Verify the key has been set correctly:

   echo $OPENAI_API_KEY

6. Run the Application

   python app.py

7. Access the Web Interface Open your browser and navigate to:

   http://localhost:5000
   

---

Features

1. Skill Matching Crew

• Job Researcher Agent : Gathers job descriptions from input.
• Candidate Profiler Agent : Analyzes user skills and experience.
• Skill Matcher Agent : Matches candidate skills to job requirements for relevance.

2. Content Generation Crew

• Resume Strategist Agent : Creates a tailored resume that highlights matched skills and experience.
• Cover Letter Strategist Agent : Generates a customized cover letter aligned with the job description.

3. Feedback and Refinement Crew

• Feedback Compiler Agent : Aggregates feedback based on readability, grammar, sentiment, and structure.
• Resume Refiner Agent : Refines the resume to improve clarity, grammar, and readability.
• Cover Letter Refiner Agent : Optimizes the cover letter for grammar, sentiment, and overall quality.

4. Feedback Metrics

• Readability Analysis : Scores generated content for reading ease (e.g., Flesch Reading Ease, Gunning Fog).
• Grammar Correction : Corrects errors using a transformer-based grammar model.
• Sentiment Analysis : Measures subjectivity and emotional tone.
• Structural Analysis : Segments content and ensures keyword matching.

---

Project Structure

project/
├── agents/
│   ├── content_generation_agent.py
│   ├── skill_matching_agent.py
│   ├── crewai_orchestrator.py
│   ├── feedback_refinement_agent.py
├── app.py
├── templates/
│   └── index.html
├── static/
│   └── styles.css
├── requirements.txt
└── README.md

---

License

MIT