🤖 AI Analysis

Final verdict: SAFE

The package does not exhibit any significant risks such as network calls, shell execution, or credential harvesting. However, it has a slightly higher metadata risk due to low-effort signs, which suggests a need for further scrutiny.

Low network and shell risk
No signs of obfuscation or credential harvesting
Some low-effort signs in metadata

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.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some low-effort signs, but lacks clear malicious indicators.

🔬 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 4.0

2 maintainer concern(s) found

Author "Peter Eastman" 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 OpenMM-HIP-7

Create a molecular simulation tool using the 'OpenMM-HIP-7' package that allows users to simulate and visualize the behavior of simple biological molecules under various conditions. This tool will enable scientists and students to better understand molecular dynamics without requiring extensive computational resources. Here’s a detailed breakdown of the project steps and features:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have installed 'OpenMM-HIP-7', along with any necessary dependencies like NumPy and Matplotlib.
2. **Molecule Input**: Design a user-friendly interface where users can input their molecule of interest. Support for common formats such as PDB and SMILES would be ideal.
3. **Simulation Parameters**: Allow users to customize simulation parameters including temperature, pressure, and duration. Implement validation checks to ensure these values are within reasonable bounds.
4. **Simulation Execution**: Use 'OpenMM-HIP-7' to execute the molecular dynamics simulations based on user inputs. Leverage its capabilities to run simulations efficiently on HIP-enabled hardware if available.
5. **Visualization**: Integrate a visualization component that displays the trajectory of the molecule over time. Consider using Matplotlib or similar libraries to plot 3D trajectories.
6. **Analysis Tools**: Provide basic analysis tools such as RMSD calculation, radius of gyration, and potential energy distribution. These tools should help users interpret the simulation results effectively.
7. **Report Generation**: Enable users to generate reports summarizing the simulation outcomes. Include visual plots and key metrics calculated during the analysis phase.
8. **User Interface**: Develop a clean, intuitive GUI using Tkinter or another suitable library. Ensure the UI is responsive and easy to navigate.

By completing this project, you will not only gain hands-on experience with 'OpenMM-HIP-7' but also contribute to the scientific community by providing a valuable tool for molecular dynamics research.