AI Analysis
The package shows low risks in terms of network, shell, obfuscation, and credential activities. However, the metadata risk is high due to suspicious git repository activity and maintainer history, which raises concerns about potential supply-chain attack.
- High metadata risk
- Suspicious git repository activity
- Unclear maintainer history
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
- Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
- Metadata: High risk due to suspicious git repository activity and maintainer history.
Package Quality Overall: Medium (5.0/10)
Test suite present — 1 test file(s) found
Test runner config found: pyproject.toml1 test file(s) detected (e.g. test_client.py)
Some documentation present
Detailed PyPI description (2240 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
Classifier: Typing :: Typed10 type-annotated function signatures detected in source
Single-author or unverifiable project
1 unique contributor(s) across 1 commits in sli-cka/aiokarakeepSingle author with few commits — possibly a personal or throwaway project
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
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forksVery few commits: 1 totalSingle contributor with only 1 commit(s) — possibly throwaway account
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "sli-cka" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a fully functional asynchronous Python application named 'KaraokePartyPlanner' that leverages the 'aiokarakeep' package to manage karaoke tracks and playlists. This application will allow users to interact with their personal karaoke library in real-time, offering features such as searching for tracks, adding new tracks, deleting tracks, and managing playlists. Here are the key functionalities and steps you need to implement: 1. **Setup Environment**: Begin by setting up your development environment with Python 3.8 or higher and installing the necessary packages including 'aiokarakeep'. 2. **User Authentication**: Implement user authentication to ensure only authorized users can access their karaoke libraries. 3. **Track Management**: Allow users to search for tracks by title, artist, or genre. They should also be able to add new tracks to their library and delete existing ones. 4. **Playlist Creation**: Users must be able to create new playlists and add/remove tracks from these playlists. 5. **Real-Time Updates**: Ensure all operations are performed asynchronously using 'aiokarakeep' to provide smooth, real-time updates. 6. **User Interface**: Develop a simple command-line interface (CLI) for interacting with the application. 7. **Testing**: Write tests to verify each functionality works as expected. 8. **Documentation**: Provide comprehensive documentation on how to use the application, including setup instructions and usage examples. Throughout the development process, utilize 'aiokarakeep' to handle all interactions with the Karakeep API asynchronously. The goal is to create an efficient, user-friendly tool for managing a personal karaoke library.
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