MakeMeGuud.LinkedIn - AI Profile Coach

A personal AI agent that analyzes your LinkedIn profile and provides actionable feedback to enhance your professional presence. This project was developed as a final project for an internship with OpenX and BlockSeBlock.

💡 The Idea

In today's competitive professional landscape, a strong LinkedIn profile is crucial. However, many individuals, especially students and early-career professionals, struggle to effectively showcase their skills and achievements. They may have impressive projects and experiences but fail to present them in a compelling way.

MakeMeGuud.LinkedIn solves this by acting as a personalized AI coach. By providing a LinkedIn profile, the agent performs a comprehensive analysis of the user's activity, bio, and experience. It then delivers two types of feedback:

1. Immediate Steps: Tactical, actionable changes the user can make right now to better reflect their existing achievements.
2. Suggestions: A strategic, long-term roadmap for profile growth, content strategy, and networking.

The agent is designed to be adaptive, providing a guided questionnaire for users with sparse profiles to help them build a strong foundation from scratch.

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🛠️ Technology Stack

This project is a full-stack application composed of a Python backend, a Next.js web application for onboarding, and a React-based Chrome Extension.

Backend

• Language: Python 3.11+
• Framework: FastAPI for creating a high-performance, asynchronous API.
• AI Integration: Google Gemini API for generative AI capabilities.
• Data Validation: Pydantic for robust data validation and settings management.
• Server: Uvicorn as the ASGI server.

Extension (Side Panel UI)

• Framework: React with TypeScript for a modern, type-safe user interface.
• Build Tool: Vite for a fast and efficient development and build process.
• Browser APIs: Chrome Extension APIs (Manifest V3), including sidePanel, scripting, and storage.

Web App (Onboarding - now deprecated)

• Framework: Next.js (App Router) with TypeScript.
• Authentication: Supabase Auth for secure user management.
• Styling: Tailwind CSS for utility-first styling.

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🚀 How It Works: The Pipeline

1. User Interaction: The user navigates to a LinkedIn profile and opens the extension's side panel.
2. Scraping: The extension's content script (content.js) is programmatically injected into the page. It intelligently waits for dynamic content to load, scrolls to reveal all posts, and scrapes key information like the headline, bio, and post text.
3. Data Transmission: The scraped data is sent to the backend API endpoint (/analyze).
4. Backend Logic:
   • The FastAPI server receives the data.
   • It first runs a completeness check. If the profile data is too sparse (e.g., no posts), it immediately returns a questionnaire_needed response.
   • If the profile is complete, the backend constructs a detailed, engineered prompt containing the user's data and specific instructions.
5. AI Analysis: The prompt is sent to the Google Gemini API. The LLM analyzes the data, identifies gaps, and generates "Immediate Steps" and "Suggestions" in a structured format.
6. Response & Display: The backend parses the AI's response and sends the structured JSON back to the extension. The React UI then dynamically renders this information in the side panel for the user.

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📋 How to Run This Project Locally

To set up and run this project on your local machine, you will need to start three separate services.

Prerequisites:

• Node.js (v18+)
• Python (v3.11+)
• A Google Gemini API Key

1. Backend Setup

# Navigate to the backend directory
cd backend

# Create and activate a Python virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate

# Install all required packages
pip install -r requirements.txt

# Create a .env file and add your API key
# GOOGLE_API_KEY="YOUR_GEMINI_API_KEY"

# Start the server
uvicorn main:app --reload

The backend will be running at http://127.0.0.1:8000.

2. Extension Setup

# Navigate to the extension directory in a new terminal
cd extension

# Install all dependencies
npm install

# Create a production build of the extension
npm run build

3. Loading the Extension in Chrome

1. Open Chrome and navigate to chrome://extensions.
2. Enable "Developer mode" in the top-right corner.
3. Click "Load unpacked".
4. Select the extension/dist folder.
5. Pin the "LinkedIn AI Profile Coach" extension to your toolbar.

You are now ready to use the extension! Navigate to any LinkedIn profile to begin.

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🔮 Future Enhancements

This MVP provides a solid foundation. Future enhancements could include:

• Re-integrating User Accounts: Storing analysis history for registered users.
• Custom Prompts: Allowing users to specify their career goals or target industry to receive more tailored advice.
• Deeper Analysis: Scraping and analyzing comments, skills, and endorsements for a more holistic profile view.
• UI/UX Polish: Adding more advanced data visualizations and a more polished design system.
• Firefox Support: Porting the extension to be compatible with Mozilla Firefox.