🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell execution, obfuscation, and credential handling. However, its metadata quality and maintainer activity are concerning, indicating potential issues that warrant further investigation.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communication.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
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 package shows low maintainer activity and poor metadata quality, raising some suspicion but not strong evidence of malice.

🔬 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-cu12

Develop a molecular dynamics simulation tool using the 'acellera-openmmtorch-cu12' package. This package, while lacking a formal description, is assumed to offer capabilities related to molecular modeling and machine learning integration, leveraging PyTorch and CUDA 12 for enhanced performance.

Your task is to create a simple yet powerful application that allows users to simulate the behavior of molecules under various conditions. The application should:
- Allow users to input or select from a predefined set of molecular structures.
- Provide options to specify initial conditions such as temperature, pressure, and time steps.
- Utilize 'acellera-openmmtorch-cu12' to perform simulations, taking advantage of its computational efficiency and integration with PyTorch.
- Display results in real-time, showing trajectories of atoms, energy levels, and other relevant data.
- Save simulation results for later analysis or sharing.

Incorporate the following features to enrich the user experience:
- A graphical user interface (GUI) built with PyQt or Tkinter to make the application accessible to non-expert users.
- Support for exporting simulation data to common file formats like PDB, XYZ, or CSV for use in other software tools.
- Integration of machine learning models provided by 'acellera-openmmtorch-cu12' to predict molecular properties based on the simulation outcomes.

The project should demonstrate proficiency in handling complex scientific computations with Python, showcasing the potential of combining traditional molecular dynamics with modern machine learning techniques.