AI Analysis
Final verdict: SAFE
The package is assessed as safe with a low risk score, primarily due to the absence of any suspicious activities such as shell execution, obfuscation, credential harvesting, and minimal metadata risk.
- Network risk due to proxy usage, but likely legitimate.
- No evidence of malicious intent or supply-chain attack.
Per-check LLM notes
- Network: The use of proxies in network requests may indicate the package is designed to access internet resources through specified proxies, which could be legitimate but also might be used for circumventing restrictions or hiding traffic origin.
- Shell: No shell execution patterns detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: Low risk due to lack of suspicious flags and no history of malicious activity.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
nt_string) session = requests.Session() session.proxies.update({"http": proxy, "https": pr
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: computer.org>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository tkem/mopidy-podcast-itunes appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Thomas Kemmer" 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 Mopidy-Podcast-iTunes
Create a podcast discovery and playback mini-application using the Mopidy-Podcast-iTunes Python package. This application will allow users to search for podcasts on Apple Podcasts, browse through categories, and stream episodes directly from their browser or terminal. Here are the steps and features you need to implement: 1. **Setup Environment**: Ensure your development environment has Python installed and install the necessary packages including Mopidy and Mopidy-Podcast-iTunes. 2. **User Interface**: Develop a simple web interface using Flask or a command-line interface (CLI) using Click. The UI should allow users to input search queries, select categories, and view podcast details. 3. **Search Functionality**: Integrate the search functionality of Mopidy-Podcast-iTunes into your application so users can search for podcasts by keyword or category. 4. **Podcast Details Page**: When a user selects a podcast, display detailed information such as episode titles, descriptions, and publication dates. 5. **Episode Streaming**: Implement streaming functionality that allows users to listen to episodes directly within the app. Use Mopidy’s built-in streaming capabilities for this purpose. 6. **Favorites List**: Allow users to save their favorite podcasts and episodes for easy access later. 7. **Notifications**: Set up a notification system that alerts users when new episodes are available for their favorite podcasts. 8. **Customization Options**: Offer customization options like theme selection and language support. 9. **Testing**: Thoroughly test the application to ensure all features work as expected and there are no bugs. 10. **Documentation**: Provide clear documentation on how to install and use the application, including setup instructions and troubleshooting tips. By following these steps, you'll create a robust podcast discovery and playback application that leverages the powerful features of Mopidy-Podcast-iTunes.