SkillForge

SkillForge is a collaborative project management platform that connects students with projects through AI-powered skill matching. The platform features real-time communication, task management, and intelligent project-student pairing capabilities.

🚀 Features

Core Functionality

• AI-Powered Matching: Intelligent skill-based matching between students and projects using sentence transformers
• Real-time Communication: WebSocket-based chat, task updates, and notifications
• Project Management: Kanban-style task boards with live collaboration
• User Management: Student and project owner profiles with skill tracking

Real-time Features

• Project Chat: Group messaging within project teams
• Live Task Updates: Real-time Kanban board synchronization
• Instant Notifications: User-specific event alerts
• WebSocket Architecture: Room-based connection management for scalable real-time updates

🏗️ Architecture

Backend (Go)

• Framework: Go with MongoDB for data persistence
• Real-time: WebSocket connections with room-based architecture
• AI Integration: Sentence transformer model for skill matching
• Database: MongoDB with collections for projects, users, messages, tasks, and notifications

Frontend (SvelteKit)

• Framework: SvelteKit with TypeScript
• Real-time: WebSocket client integration
• UI: Tailwind CSS for responsive design
• Charts: Chart.js for data visualization

AI Matching System

• Model: all-MiniLM-L6-v2 sentence transformer for semantic similarity
• Purpose: Maps user skills and project requirements to 384-dimensional vectors for matching
• Training: Fine-tuned on 1B+ sentence pairs for optimal semantic understanding

🛠️ Technology Stack

Backend:

• Go
• MongoDB
• WebSocket (gorilla/websocket)
• Sentence Transformers (Python AI service)

Frontend:

• SvelteKit
• TypeScript
• Tailwind CSS
• Chart.js
• WebSocket client

AI/ML:

• Python
• Sentence Transformers
• HuggingFace Transformers

📁 Project Structure

SkillForge/
├── backend/                 # Go backend service
│   ├── internal/
│   │   ├── handlers/       # HTTP and WebSocket handlers
│   │   ├── services/       # Business logic layer
│   │   ├── repositories/   # Data access layer
│   │   ├── models/         # Data models
│   │   └── integrations/   # External service integrations
│   └── storage/            # File storage (avatars, etc.)
├── frontend/               # SvelteKit frontend
├── ai/                     # AI matching system
│   └── matching/
│       └── matching-model/ # Sentence transformer model
└── docs/                   # Documentation

🚦 Getting Started

Prerequisites

• Go 1.19+
• Node.js 18+
• MongoDB
• Python 3.8+ (for AI matching)

Run all services with Docker

docker-compose up

Backend Setup

cd backend
go mod download
go run main.go

Frontend Setup

cd frontend
npm install
npm run dev

AI Service Setup

cd ai/matching
pip install -r requirements.txt
python app.py

🔄 Real-time Communication

The platform uses a sophisticated WebSocket architecture with three main communication channels:

1. Chat Messages: Project-based group conversations using room format "message{projectID}"
2. Task Updates: Live Kanban board synchronization using room format "{projectID}"
3. Notifications: User-specific alerts using room format "notification"

Each WebSocket connection is managed through a centralized RealtimeClient that handles connection lifecycle, room management, and message broadcasting.

🤖 AI Matching Algorithm

The matching system uses semantic similarity to pair students with relevant projects:

1. Feature Extraction: User skills and project requirements are converted to text embeddings
2. Similarity Calculation: Cosine similarity between user and project vectors
3. Score Generation: Rounded similarity scores for ranking matches

📊 Database Schema

Key Collections:

• users: Student and project owner profiles
• projects: Project details and requirements
• messages: Chat communications
• tasks: Kanban board items
• notifications: User alerts
• project_student: Project membership relationships

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull reques>

📄 License

This project is licensed under the Apache 2.0 License.

Notes

The codebase demonstrates a modern full-stack architecture with real-time capabilities and AI integration. The backend follows clean architecture principles with clear separation between handlers, services, and repositories. The AI matching system leverages state-of-the-art sentence transformers for semantic understanding, while the real-time communication system provides scalable WebSocket management through room-based organization. The project structure supports both development and production deployment scenarios. For better understanding of the repo, follow this link https://deepwiki.com/iknizzz1807/SkillForge