Information
This project is currently in active development.

RUGGUARD is an intelligent Twitter bot that analyzes the trustworthiness of Solana project accounts. When someone replies to a tweet with "@projectrugguard riddle me this", the bot analyzes the original tweet's author and posts a trustworthiness report.

• Comprehensive Analysis: Evaluates accounts based on multiple trust indicators
• Real-time Monitoring: Listens for mentions and responds automatically
• Detailed Reports: Provides clear, actionable insights about account trustworthiness
• Open Source: Transparent and community-driven development
• Easy Deployment: Ready to deploy on Replit or your own server
• Analyzes account metrics including:
  • Account age and activity
  • Follower/Following ratio
  • Bio content analysis
  • Engagement patterns
  • Sentiment analysis of recent tweets
  • Trusted account verification
• Posts concise trustworthiness reports
• Built-in rate limiting and duplicate detection

• Python 3.8 or higher
• Twitter Developer Account with Elevated Access
• Replit account (for deployment)

1. Clone the repository:

   git clone https://github.com/yourusername/rugguard-bot.git
   cd ruguard-bot

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables: Create a .env file in the project root with the following variables:

   API_KEY=your_twitter_api_key
   API_SECRET=your_twitter_api_secret
   ACCESS_TOKEN=your_twitter_access_token
   ACCESS_TOKEN_SECRET=your_twitter_access_token_secret
   BEARER_TOKEN=your_twitter_bearer_token
   

1. Run the bot:

   python main.py

2. To test the bot, reply to any tweet with:

   @projectrugguard riddle me this
   

1. The bot monitors Twitter for mentions of "@projectrugguard riddle me this" in replies
2. When triggered, it identifies the original tweet's author
3. It analyzes various trust indicators:
   • Account age and activity
   • Follower/Following ratio
   • Bio content
   • Engagement metrics
   • Tweet sentiment
   • Trusted account verification
4. Generates a trust score (0-100)
5. Posts a reply with the analysis

You can adjust the weights for the trust score calculation in main.py by modifying the weights dictionary in the analyze_user function.

1. Fork this repository
2. Create a new Repl and import your forked repository
3. Set up environment variables in the Replit Secrets tab
4. Run the Repl

Contributions are welcome! Please feel free to submit a Pull Request.

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