AtomicAI

v0.4.0 safe
3.0
Low Risk

Processing and visualization of atomic coordinates; featurizing atomic structures

🤖 AI Analysis

Final verdict: SAFE

The package AtomicAI v0.4.0 shows very low risks across all checked categories with no network calls, shell executions, obfuscations, or credential harvesting activities. The metadata risk is slightly elevated due to the maintainer's limited package history.

  • No network calls detected
  • No shell execution patterns detected
  • Maintainer has only one package
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, which could indicate a new or less active account.

🔬 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

Email domain looks legitimate: gmail.com

Suspicious Page Links

All external links appear legitimate

Git Repository History

Repository selvachandrasekaranselvaraj/AtomicAI appears legitimate

Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Selva Chandrasekaran Selvaraj" 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 AtomicAI
Create a Python-based mini-application named 'MolecularExplorer' that allows users to visualize and analyze molecular structures using the AtomicAI package. This application should enable users to input molecular structures in various formats (e.g., .xyz, .pdb), process these structures to extract key information such as bond lengths, angles, and dihedral angles, and then visualize these structures in 3D. Additionally, the app should feature tools to calculate molecular properties like the radius of gyration and surface area, which can be useful for understanding the physical properties of molecules. Users should also be able to manipulate the visualized structures through rotation, zooming, and translation within the application interface. The main steps for building this application include:
1. Setting up the project environment and installing AtomicAI.
2. Creating a user-friendly interface using a library like PyQt5 for inputting molecular data and displaying results.
3. Implementing functions to read molecular files into memory and parse them into usable data structures.
4. Utilizing AtomicAI to process the molecular structures, including calculating geometric properties and generating features that describe the structure.
5. Integrating a 3D visualization component that allows users to interactively view and manipulate the molecular structures.
6. Adding functionality to compute and display additional molecular properties based on the processed data.
7. Testing the application thoroughly to ensure all components work correctly and efficiently. By following these steps, you'll create a powerful yet accessible tool for exploring and understanding molecular structures.