🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks in terms of network usage, shell execution, obfuscation, and credential handling. However, it exhibits low maintainer activity and poor metadata quality, which slightly increases its risk level.

Low network and shell risk
Poor metadata quality
No signs of malicious intent

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution patterns detected, indicating the package does not attempt to execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate a lack of transparency and potential risk.

🔬 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 short
Author "" 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 adamo

Create a real-time video streaming application using the 'adamo' Python package. This application will enable users to stream live video from a camera source and simultaneously overlay real-time sensor data (such as temperature, humidity, etc.) onto the video feed. The application will use Zenoh protocol for seamless data and video streaming.

Steps:
1. Set up a Python environment with the 'adamo' package installed.
2. Connect to a Zenoh router or peer network where the live video and sensor data streams will be published.
3. Use 'adamo' to capture live video from a camera (e.g., webcam).
4. Simulate or connect to a sensor that provides real-time environmental data.
5. Stream the live video and sensor data over the Zenoh network.
6. Implement a receiver component that subscribes to both the video and sensor data streams.
7. Overlay the sensor data onto the video frames in real-time and display it on a window.
8. Ensure the application handles errors gracefully and logs relevant information for debugging purposes.

Suggested Features:
- User interface for starting/stopping the video stream and adjusting sensor data overlay settings.
- Ability to save the video stream with overlaid sensor data locally.
- Support for multiple sensor types and dynamic data visualization options.
- Integration with a web-based dashboard for monitoring the stream remotely.

The 'adamo' package will be used extensively for capturing video, streaming data over Zenoh, and subscribing to streams. It simplifies the process of handling complex data and media streams, making it ideal for real-time applications.