🤖 AI Analysis

Final verdict: SAFE

The package Mopidy-Listenbrainz v0.4.0 shows minimal signs of potential risk with no indications of malicious activities such as shell execution, obfuscation, or credential harvesting. The network interaction is controlled and does not suggest harmful behavior.

Low network interaction risk
No shell execution detected
No obfuscation detected
Secure handling of credentials

Per-check LLM notes

Network: The use of a custom user agent and configurable proxy suggests network interaction but does not necessarily indicate malicious intent.
Shell: No shell execution patterns were detected, indicating low risk.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating secure handling of secrets.
Metadata: The maintainer has only one package, which might indicate a new or less active account, but there are no other red flags.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

gent(user_agent) client = httpx.Client( proxy=httpclient.format_proxy(proxy_config),

✓ 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: mcarr.one

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository suaviloquence/mopidy-listenbrainz appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "suaviloquence" 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-Listenbrainz

Create a music listening tracker app that integrates with Mopidy and ListenBrainz using the Mopidy-Listenbrainz package. This app will allow users to track their music listening habits in real-time and contribute their listening data to the ListenBrainz community database. Here’s a step-by-step guide on how to develop this mini-application:

1. **Setup Your Environment**: Ensure you have Python installed and create a virtual environment for your project.
2. **Install Dependencies**: Install Mopidy and Mopidy-Listenbrainz along with any other necessary packages like Flask for web interface if desired.
3. **Configure Mopidy**: Set up Mopidy to use the Mopidy-Listenbrainz extension by configuring the appropriate settings in mopidy.conf.
4. **Develop the User Interface**: Design a simple web-based user interface where users can log into their ListenBrainz accounts and view their listening history.
5. **Implement Real-Time Tracking**: Utilize Mopidy-Listenbrainz to automatically send listen data to ListenBrainz whenever a track is played through Mopidy.
6. **Enhance Functionality**: Add features such as a dashboard showing top artists and albums listened to, a feature to compare listening habits over time, and an option to sync data from external music services like Spotify or Last.fm.
7. **Testing**: Thoroughly test the application under various conditions to ensure it works smoothly and accurately records listen data.
8. **Deployment**: Deploy your application so users can access it online or on their local machine.

By following these steps, you’ll not only contribute valuable listening data to the music community but also enhance your skills in Python development, web design, and music player integration.