🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network calls, shell execution, and obfuscation. However, the metadata risk score indicates potential issues with maintenance and transparency, making it suspicious.

Low metadata maintenance
Potential lack of transparency

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external communication.
Shell: No shell execution detected, indicating the package does not execute system commands, which is safe unless it's supposed to.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintenance and potential lack of transparency, raising suspicion.

🔬 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

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 Arago

Create a mini-application named 'MoonTracker' using the Python package 'Arago', which specializes in astrometric reduction of moons in space images. This application will allow users to upload their own space images and automatically detect and track the position of moons within those images. Here’s a detailed step-by-step guide on how to develop this application:

1. **Setup Environment**: Begin by setting up your Python environment and installing necessary packages including 'Arago'. Ensure you also have other required dependencies like image processing libraries.
2. **User Interface Design**: Develop a simple yet effective user interface where users can upload their space images. This could be a web-based UI or a desktop application depending on your preference.
3. **Image Processing**: Use 'Arago' to process the uploaded images. Implement functionality to identify potential moon locations based on the package's capabilities for astrometric reduction.
4. **Data Visualization**: Once moons are detected, visualize their positions on the uploaded images. Provide options to zoom in/out, highlight detected moons, and display relevant data points such as coordinates and brightness levels.
5. **Report Generation**: Allow users to generate reports detailing the analysis performed on their images. Include findings like the number of moons detected, their positions, and any anomalies noted during the analysis.
6. **Integration with External Databases**: Optionally, integrate the application with external databases to store and retrieve historical data about celestial bodies, enhancing the application's analytical capabilities.
7. **Testing and Optimization**: Rigorously test the application to ensure accuracy and efficiency. Optimize the performance of 'Arago' functions to handle large datasets effectively.

Suggested Features:
- User-friendly interface for uploading images.
- Real-time feedback on moon detection progress.
- Interactive visualization tools.
- Customizable report templates.
- Integration with astronomical databases for additional context.

How 'Arago' is Utilized:
'Arago' will primarily be used for the astrometric reduction phase where it processes the uploaded images to accurately determine the positions of moons. This involves complex calculations and adjustments to account for various factors affecting the images, such as atmospheric conditions and camera settings. By leveraging 'Arago', your application can provide highly accurate and reliable results, making it a valuable tool for both amateur astronomers and professionals alike.