ForgeFF

v1.1.6 safe
3.0
Low Risk

Semi-empirical potential fitting in Python

🤖 AI Analysis

Final verdict: SAFE

The ForgeFF package exhibits minimal risk across all assessed categories, with no indications of malicious activity or network/shell interactions. However, the novelty and lack of activity in the repository slightly elevate the metadata risk.

  • No network calls detected
  • Repository is new and inactive
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 direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The repository is new and inactive, with low visibility indicators, which may suggest potential risk but lacks clear malicious intent signals.

🔬 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 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 "Pranav Kumar" 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 ForgeFF
Create a molecular simulation tool called 'MolSim' using the Python package 'ForgeFF'. This tool aims to simulate the behavior of molecules under various conditions by utilizing semi-empirical potentials for accurate predictions. Your task is to develop a user-friendly command-line interface (CLI) application that allows users to input molecular structures and conditions, then runs simulations based on these inputs.

Key Features:
1. **Molecule Input**: Users should be able to upload or input molecular structures in common formats like SMILES, PDB, or XYZ.
2. **Simulation Parameters**: Allow users to specify parameters such as temperature, pressure, and time steps for the simulation.
3. **Visualization**: Integrate a simple visualization component that displays the molecular structure and its evolution over time.
4. **Output Data**: Provide options for exporting simulation results in various formats (CSV, JSON, etc.) and visualizing them through plots or graphs.
5. **Documentation**: Ensure comprehensive documentation is available both within the codebase and as external README files.

Utilization of 'ForgeFF':
- Use 'ForgeFF' for defining and applying the semi-empirical potentials necessary for the simulation. This includes fitting the potential energy surfaces based on experimental data or theoretical calculations provided by the user.
- Implement functions that leverage 'ForgeFF' to optimize the molecular dynamics calculations, ensuring accuracy and efficiency.
- Include examples in the documentation demonstrating how different potentials affect the simulation outcomes.

Your goal is to create a tool that not only serves as a practical application but also showcases the capabilities of 'ForgeFF' in simulating molecular systems.