🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits unusually low engagement in terms of metadata and external links, raising suspicion about its legitimacy and intent.

New package with incomplete metadata
Lack of author details and PyPI classifiers

Per-check LLM notes

Network: No network calls detected, which is normal for a CPU-only library without online dependencies.
Shell: No shell execution patterns detected, aligning with expectations for a standard Python library.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
Credentials: No credential harvesting patterns detected, suggesting no immediate threat related to secret or credential theft.
Metadata: The package is new, lacks author details and PyPI classifiers, and has no associated GitHub repository, indicating low effort or potentially suspicious activity.

🔬 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 8.0

4 maintainer concern(s) found

Package is very new: uploaded 2 day(s) ago
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-cpu

Your task is to develop a molecular modeling utility named 'MolModeler' using the Python package 'acellera-openmmtorch-cpu'. This utility will allow users to perform basic molecular dynamics simulations and energy calculations on their computer without the need for high-performance computing resources. The application should have a user-friendly command-line interface and provide visual outputs of the simulation results.

Key Features:
1. **Input Molecular Structure**: Users should be able to input a molecular structure in PDB format. The utility will parse the file and prepare it for further analysis.
2. **Energy Calculation**: Implement functionality to calculate the potential energy of the molecular system using methods supported by 'acellera-openmmtorch-cpu'. Display the total energy value.
3. **Molecular Dynamics Simulation**: Allow users to run short molecular dynamics simulations (e.g., 100 steps). Visualize the trajectory of molecules over time.
4. **Visualization**: Provide graphical representations of the initial structure, final state after energy calculation, and the entire simulation process. Use simple plotting libraries like matplotlib for visualization.
5. **Output Files**: Save the simulation data and visualizations as files for future reference or further analysis.

How to Utilize 'acellera-openmmtorch-cpu':
- Use 'acellera-openmmtorch-cpu' for parsing the molecular structure and preparing it for energy calculations and simulations.
- Leverage its capabilities to perform fast and accurate energy calculations.
- Employ the package's simulation functionalities to run molecular dynamics simulations efficiently.

Step-by-Step Guide:
1. Install 'acellera-openmmtorch-cpu' and any additional Python packages you might need (e.g., numpy, matplotlib).
2. Develop the input parser to read PDB files and convert them into a format suitable for processing by 'acellera-openmmtorch-cpu'.
3. Implement functions to calculate the potential energy of the molecular system.
4. Create a function to run molecular dynamics simulations for a specified number of steps.
5. Integrate visualization tools to display the molecular structures and trajectories.
6. Add functionality to save the output data and visualizations to files.
7. Test the utility thoroughly with different molecular structures to ensure reliability and accuracy.
8. Document your code and provide clear instructions on how to use the utility.

This project aims to demonstrate the power of 'acellera-openmmtorch-cpu' in simplifying complex molecular modeling tasks and making them accessible to a broader audience.