AI Analysis
Final verdict: SUSPICIOUS
The package has minimal risk indicators but exhibits low maintainer activity and poor metadata quality, which raises concerns about its reliability and potential for undetected issues.
- Low maintainer activity
- Poor metadata quality
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- 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 package shows signs of low maintainer activity and poor metadata quality, which could indicate a lack of transparency and accountability.
Package Quality Overall: Low (2.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (4640 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ 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
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 6.0
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 alobj2shp
Create a mini-application named 'BuildingFootprintConverter' using the Python package 'alobj2shp'. This application will serve as a user-friendly tool for converting 3D building models stored in OBJ format into geographic data formats such as GeoPackage (GPKG) or Shapefile (SHP). The application should be designed to handle multiple input files and allow users to specify output file types and locations. Steps to follow: 1. Install necessary packages including 'alobj2shp', 'geopandas', and 'numpy'. 2. Develop a function that reads OBJ files and extracts building footprints. 3. Implement a feature to convert these footprints into GeoDataFrame objects. 4. Add functionality to export GeoDataFrame objects into either GPKG or SHP formats based on user choice. 5. Create a simple GUI using Tkinter that allows users to select input OBJ files, choose output file type and location, and initiate the conversion process. 6. Ensure error handling for file reading/writing operations and provide informative messages to the user. 7. Include a help menu within the GUI explaining the purpose of the application and basic usage instructions. 8. Test the application with various OBJ files representing different buildings to ensure accuracy and reliability of conversions. Suggested Features: - Support for batch processing of multiple OBJ files at once. - Option to save output files in both GPKG and SHP formats simultaneously. - Provide progress indicators during file conversions. - Allow customization of CRS (Coordinate Reference System) for output files.