🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of obfuscation and has low engagement metrics, raising concerns about its reliability and potential for supply-chain attacks.

Observed obfuscation patterns
Low engagement metrics on Git repository

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell execution patterns detected, indicating the package does not execute system commands that could pose a risk.
Obfuscation: The observed patterns suggest some form of obfuscation, but without additional context, it's unclear if this is malicious or simply part of a normal process like model evaluation and saving.
Credentials: No clear patterns indicative of credential harvesting were detected.
Metadata: The package has low engagement on its Git repository and the maintainer has only one package on PyPI, suggesting potential unreliability.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

te_dict(state_dict) model.eval() return model def save_mrc_with_origin(data, filepath,

✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Nandan Haloi" 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 CryoLigate

Create a mini-application that enhances the visualization and analysis of cryo-EM maps for protein-ligand complexes using the CryoLigate package. This application should allow users to upload their cryo-EM density maps and receive enhanced versions that improve the clarity and detail of the protein-ligand interactions. Here are the key steps and features for your application:

1. **User Interface**: Develop a user-friendly interface where users can upload their cryo-EM density map files.
2. **Data Processing**: Implement functionality to preprocess the uploaded data to ensure it meets the requirements for processing with CryoLigate.
3. **Enhancement Using CryoLigate**: Utilize the CryoLigate package to enhance the resolvability of the cryo-EM maps. Ensure that the application leverages the deep learning models provided by CryoLigate to improve the resolution and detail of the protein-ligand complexes.
4. **Visualization**: Provide tools within the application to visualize both the original and enhanced cryo-EM maps side by side, allowing users to compare the improvements visually.
5. **Report Generation**: Enable users to generate detailed reports summarizing the enhancements made to their cryo-EM maps, including statistical analyses and visual comparisons.
6. **Export Options**: Offer options for users to export the enhanced cryo-EM maps in various formats suitable for further scientific analysis or publication.
7. **Documentation and Help**: Include comprehensive documentation and a help section that explains how to use the application effectively, as well as any technical details about the CryoLigate package and its usage within the application.

This application will serve as a valuable tool for researchers working in structural biology, providing them with a powerful way to analyze and enhance cryo-EM data.