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🔁 SkillSwap — A Skill Exchange Platform

SkillSwap Banner

License: MIT Next.js TypeScript ShadCN Made with Python Clerk


📌 Overview

SkillSwap is a full-stack application where users can exchange skills directly with others or use tokens to access services. It's designed to empower users to trade knowledge and abilities in a smart, AI-assisted environment.


🌟 Key Features

🔄 Skill-for-Skill & Token Exchange

  • Directly connect and exchange skills (e.g., design for coding).
  • Use platform-specific tokens to request or offer services.

🧠 Sentiment-Based Skill Recommendations

  • Uses a Python-based sentiment analysis model to rank and recommend users based on feedback and tone.

💬 AI Chat Assistant (Gemini)

  • Built-in assistant using Gemini for real-time help, suggestions, and onboarding queries.

👤 Rich Profile & Request System

  • Add your skills, certifications, ratings, and availability.
  • Make requests or post services openly on the marketplace.

🔐 Authentication System

  • Secure and simple sign-up/sign-in flow using Clerk with Google authentication.
  • Modern Next.js integration for seamless user experience.

🧪 Skill Test and Review Modules

  • Analyze peer reviews and skill tests for objective growth tracking.

📂 Project Structure

app/
├── analyzesenti/            # Sentiment analysis logic and API
├── api/                     # Backend API handlers
├── dashboard/               # Dashboard for logged-in users
├── gemini/                  # Gemini-based AI assistant
├── home/                    # Home/Landing pages
├── marketplace/             # Token-based skill trading
├── onboarding/              # Step-by-step user onboarding
├── pages/                   # Routing structure
├── portal/                  # User interaction hub
├── profile/                 # User profile, settings, history
├── request/                 # Request posting/accepting system
├── sign-in / sign-up        # Authentication pages
├── test-review-analysis/    # Skill test results & feedback logic

components/                  # Reusable UI components (ShadCN-based)
types/                       # TypeScript interfaces and types
lib/                         # Custom utility functions
public/                      # Static files (images, fonts)

⚙️ Technologies Used

Frontend:

  • Next.js 13+
  • TypeScript
  • Tailwind CSS + ShadCN/UI
  • Context API + Providers for State Management

Backend:

  • Next.js API Routes
  • Python (for ML model integration)

Authentication:

  • Clerk with Google Authentication

AI/NLP:

  • Gemini for chat assistance
  • Custom Python-based Sentiment Analysis Model

📸 Screenshots

image image image image image image image image image image image

🚀 Getting Started

1. Clone the Repo

git clone https://github.com/AnupamSingh2004/skill-swap.git
cd skill-swap

2. Install Dependencies

npm install
# or
yarn install

3. Set up Environment Variables

Create a .env.local file at the root of your project:

NEXT_PUBLIC_GEMINI_API_KEY=your_gemini_api_key
NEXT_PUBLIC_BACKEND_URL=http://localhost:3000
NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=your_clerk_publishable_key
CLERK_SECRET_KEY=your_clerk_secret_key

4. Start the Dev Server

npm run dev
# or
yarn dev

Visit http://localhost:3000 in your browser.

🧠 AI Features in Detail

Gemini Assistant

  • Built using Google's Gemini API for natural chat.
  • Responds to skill queries, profile help, matching requests.

Sentiment Analysis Model

  • Trained on peer reviews and user messages.
  • Scores users based on confidence, helpfulness, and positivity.
  • Python server processes messages and recommends top matches.

🤝 Contributing

We love contributions from the community!

How to Contribute:

  1. Fork the repo
  2. Create a new branch (git checkout -b feature/awesome-feature)
  3. Make your changes
  4. Submit a PR!

Please read the CONTRIBUTING.md for more details.

📄 License

This project is licensed under the MIT License.

👨‍💻 Team

This project was created during HacByte 3.0 by:

💬 Contact

For questions, feedback, or suggestions:

  • GitHub: @AnupamSingh2004

Built with ❤️ using Next.js, Python, and a whole lot of caffeine ☕

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