🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risks in terms of network usage, shell execution, and obfuscation but raises concerns due to the maintainer's limited experience indicated by having only one package and no linked GitHub repository.

Low risk in network calls, shell execution, and obfuscation.
Metadata risk due to inexperienced maintainer

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
Metadata: The maintainer has only one package and no linked GitHub repository, which might indicate a less experienced or potentially suspicious maintainer.

🔬 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: crbm.cnrs.fr

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Benjamin GALLEAN" 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 AstecManager

Create a mini-application called 'LiveImagingAnalyzer' that leverages the AstecManager package to process and analyze live imaging data from developmental biology experiments. This application will serve as a tool for researchers to efficiently manage and analyze large datasets generated from live imaging studies of developmental processes. Here are the key steps and features you should include in your project:

1. **Setup**: Begin by installing the AstecManager package and setting up a virtual environment for your project. Ensure all dependencies are properly installed.
2. **Data Import**: Develop a feature that allows users to import live imaging data files into the application. Support common file formats such as TIFF, JPEG, and RAW.
3. **Preprocessing**: Implement a preprocessing module that cleans and normalizes the imported data using functions provided by AstecManager. This could include noise reduction, background subtraction, and contrast enhancement.
4. **Algorithm Application**: Utilize AstecManager to apply various ASTEC algorithms to the preprocessed images. These algorithms should help in identifying specific cellular structures or developmental stages within the images.
5. **Visualization**: Create an interactive visualization interface where users can view the original images alongside the processed ones. Highlight areas of interest detected by the ASTEC algorithms.
6. **Analysis Reports**: Enable users to generate detailed analysis reports based on the processed data. Include metrics like cell count, growth rates, and any other relevant biological parameters extracted from the images.
7. **User Interface**: Design a user-friendly GUI using a library like PyQt or Tkinter to make the application accessible to non-technical users.
8. **Documentation**: Write comprehensive documentation detailing how to use each feature of LiveImagingAnalyzer, including examples and tutorials.

By following these steps and incorporating these features, you'll create a powerful yet easy-to-use tool for analyzing live imaging data in developmental biology research.