🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low direct risks such as network calls, shell execution, obfuscation, and credential harvesting. However, the metadata quality concerns suggest potential neglect or malicious intent, warranting caution.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell executions detected, indicating no immediate risk of command injection or system compromise.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising suspicion but not definitive proof of malintent.

🔬 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 6.0

3 maintainer concern(s) found

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 acellera-openmmtorch-cu13

Your task is to create a mini-application that leverages the 'acellera-openmmtorch-cu13' Python package, which appears to be related to molecular modeling and machine learning, specifically tailored for GPU acceleration on CUDA 13. This application will serve as a tool for predicting molecular properties based on input chemical structures. Here’s a detailed breakdown of the steps and features your project should include:

1. **Setup**: Begin by setting up a virtual environment and installing necessary packages including 'acellera-openmmtorch-cu13'. Ensure you have CUDA 13 installed on your system to take full advantage of GPU acceleration.

2. **Data Input**: Develop a user-friendly interface where users can input their molecular structures either via SMILES notation or upload a file containing molecular data.

3. **Model Prediction**: Use 'acellera-openmmtorch-cu13' to preprocess the input data and run predictions through a pre-trained model. This model should predict properties such as solubility, toxicity, or other relevant chemical properties based on the molecular structure.

4. **Visualization**: Implement a visualization component that displays the predicted properties alongside the input molecule’s structure. This could be a simple 2D depiction or a more advanced 3D rendering depending on the complexity you wish to introduce.

5. **Reporting**: Finally, provide a summary report that includes the predicted properties, any warnings or errors encountered during processing, and suggestions for further analysis if applicable.

**Suggested Features**:
- Integration with popular chemistry file formats (e.g., SDF, PDB).
- Option to save visualizations and reports for later reference.
- Real-time feedback during the prediction process.
- Documentation and help resources within the application for users unfamiliar with molecular modeling.

By completing these steps, you'll have built a powerful yet accessible tool for chemists and researchers looking to quickly analyze and understand the properties of various molecules.