🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is deemed suspicious due to signs of low maintainer effort and unclear maintenance history, though no direct evidence of malicious intent was found.

Low maintainer activity
Unclear maintenance history

Per-check LLM notes

Metadata: The package shows signs of low maintainer effort and could be suspicious, but lacks clear indicators of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

model=MODEL_NAME, api_key=os.getenv("GITHUB_TOKEN"), base_url="https://models.github.ai/inference", t

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository amanmdesai/RooAgent appears legitimate

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with RooAgent

Create a mini-application called 'DataVisualizer' that leverages the RooAgent Python package to visualize complex data sets using ROOT graphical capabilities. The application should serve as a user-friendly tool for data scientists and analysts to explore their data visually. Here’s a detailed step-by-step guide on how to develop this application:

1. **Setup Environment**: Begin by setting up a virtual environment for your project. Install the necessary packages including RooAgent and any other dependencies such as matplotlib for additional visualization tools.
2. **Define Core Functionality**: Develop core functionalities of DataVisualizer. These include loading data from various sources (CSV, JSON, etc.), processing the data to prepare it for visualization, and using RooAgent to create visual representations.
3. **User Interface**: Design a simple yet effective GUI using Tkinter or another Python GUI toolkit. This interface should allow users to upload their data files, select types of visualizations they want (histograms, scatter plots, etc.), and customize certain aspects of these visualizations (colors, labels, etc.).
4. **Integration with RooAgent**: Utilize RooAgent’s capabilities to generate high-quality graphics. Focus on leveraging its ability to handle large datasets efficiently and provide sophisticated plotting options.
5. **Advanced Features**: Implement advanced features like real-time data streaming visualization, interactive plots where users can zoom in/out or pan across the plot area, and saving/exporting visualizations in different formats.
6. **Testing & Documentation**: Thoroughly test the application to ensure all features work as expected. Write comprehensive documentation explaining how to install, use, and customize DataVisualizer.
7. **Deployment**: Package the application into an executable file using tools like PyInstaller so that it can be easily distributed and run on different machines without requiring the installation of Python or other dependencies.

By following these steps, you will create a versatile and powerful tool for data visualization that integrates RooAgent’s unique features, making complex data exploration more accessible and intuitive.