AI Analysis
The package has low risks associated with network calls, shell execution, obfuscation, and credential harvesting. However, its low maintainer activity and poor metadata quality raise concerns about its overall reliability and transparency.
- 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 no direct system command execution from the package.
- 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 may indicate a lack of transparency or effort.
Package Quality Overall: Low (2.0/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (2225 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
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)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a mini-application called 'DracoMeshViewer' that allows users to visualize and manipulate 3D models compressed using Draco compression. This application will leverage the 'aldraco' Python package to handle Draco-compressed files efficiently. The goal is to provide a user-friendly interface where users can upload Draco-compressed 3D model files (.drc), view them in real-time, and apply basic transformations such as rotation, scaling, and translation. Additionally, the application should support exporting the decompressed model back into a standard 3D file format like .obj. Steps to develop 'DracoMeshViewer': 1. Set up a basic Python environment with necessary dependencies including 'aldraco', 'PyOpenGL', and 'pygame'. 2. Implement a function to load Draco-compressed files using 'aldraco', which will decode the mesh data into vertices and indices. 3. Integrate PyOpenGL to render the decoded 3D model in real-time within the pygame window. 4. Develop a simple GUI using pygame for file input/output operations, displaying the loaded 3D model, and allowing users to interact with it through mouse and keyboard inputs. 5. Add functionality to apply transformations (rotation, scaling, translation) to the 3D model in real-time based on user interactions. 6. Include an export feature that saves the current state of the 3D model (after any applied transformations) back into a standard 3D file format like .obj. 7. Ensure the application handles errors gracefully, providing informative messages when issues occur during file loading or saving processes. 8. Optimize performance for smooth real-time rendering and interaction. 9. Document the code thoroughly and create a README.md file explaining how to install and run the application, along with a brief description of the 'aldraco' package and its role in the application.
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