AI Analysis
Final verdict: SAFE
The package exhibits very low risks across all categories with no network calls, shell executions, obfuscations, or credential harvesting attempts. The metadata risk is slightly elevated due to low effort indicators but does not suggest malicious activity.
- Low network risk
- No shell execution detected
- Minimal obfuscation risk
- No credential harvesting detected
- Metadata suggests low development effort
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 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 signs of low effort, such as having only one associated package and lacking PyPI classifiers, but there are no clear indicators of malicious intent.
Package Quality Overall: Low (3.0/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
103 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
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: uga.edu>
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 "Andreas V. Copan" 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 automol
Create a molecular visualization tool using the 'automol' Python package. This tool will allow users to input chemical formulas or SMILES strings of molecules and generate 3D visualizations of those molecules. Additionally, the tool should provide basic analysis such as calculating bond lengths, angles, and dihedral angles between atoms within the molecule. The application should also support saving the visualized molecule as a PNG file. Steps: 1. Set up a Python environment and install the 'automol' package. 2. Design a user interface where users can input a chemical formula or SMILES string. 3. Implement functionality to parse the input and convert it into a molecule object using 'automol'. 4. Use 'automol' to calculate the 3D coordinates of each atom in the molecule. 5. Integrate a visualization library (such as PyMOL or Matplotlib) to render the 3D structure of the molecule. 6. Add features to calculate and display bond lengths, angles, and dihedral angles between selected atoms. 7. Implement a save feature allowing users to export the rendered image as a PNG file. 8. Test the application with various molecules to ensure accuracy and reliability. 9. Document the code and create a README file explaining how to use the application. Features: - Input validation to ensure only valid chemical formulas or SMILES strings are processed. - Interactive selection of atoms for detailed analysis. - Option to adjust the scale and orientation of the 3D model. - Support for multiple visualization styles (e.g., ball-and-stick, space-filling). - Detailed documentation and usage instructions. How 'automol' is utilized: - For parsing chemical formulas and SMILES strings into molecule objects. - To calculate the 3D geometry of molecules based on input data. - For performing geometric calculations like distances, angles, and torsions.
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