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.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!