🤖 AI Analysis

Final verdict: SAFE

The package RosettaX v0.0.18 shows low individual risks across various categories such as network, shell, obfuscation, and credential handling. However, the metadata risk due to the repository's low engagement warrants some caution.

No network calls detected
No shell execution patterns found
Repository has low engagement

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access to function properly.
Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating no immediate threat to secrets or credentials.
Metadata: The repository's lack of engagement and the maintainer's sparse profile suggest potential risks, but no 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

No credential harvesting patterns detected

✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with RosettaX

Create a user-friendly desktop application using Python and the 'RosettaX' package that allows researchers to calibrate their flow cytometers more efficiently. The app should guide users through the process of setting up interactive fluorescence and scattering calibration workflows. Here’s a step-by-step breakdown of what the application should do:

1. **User Interface Setup**: Design a clean and intuitive GUI where users can input necessary parameters such as sample type, fluorophores used, and desired calibration levels.
2. **Data Importation**: Allow users to import their raw data files directly from their flow cytometer. Ensure compatibility with common file formats like FCS (Flow Cytometry Standard).
3. **Interactive Calibration Workflows**: Utilize RosettaX’s core functionalities to enable real-time adjustments and visualizations of calibration processes. Users should be able to see immediate results of their adjustments on graphs and charts within the application.
4. **Customizable Calibration Settings**: Provide options for users to customize their calibration settings according to specific research needs. This includes adjusting thresholds for different types of signals and optimizing the sensitivity of the detection system.
5. **Export Results**: Once calibrated, allow users to export their calibrated data in various formats including CSV, Excel, or back into FCS format for further analysis or archiving.
6. **Documentation and Help**: Include comprehensive documentation within the app that explains each step of the calibration process and provides troubleshooting tips.

The 'RosettaX' package will be crucial in handling the complex calculations and real-time interactions needed for accurate calibration. Your task is to integrate its capabilities seamlessly into the app’s workflow, ensuring ease of use while maintaining scientific accuracy.