🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network calls, shell execution, obfuscation, and credential harvesting. However, its low maintainer activity and poor metadata quality raise concerns about its legitimacy and maintenance.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution 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 low maintainer activity and poor metadata quality, which may indicate it's not well-maintained or legitimate.

🔬 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 acmdecoder

Create a Python-based mini-application called 'ACM Data Visualizer' that leverages the 'acmdecoder' package to decode and visualize raw binary data from an ACM (Acquisition and Control Module) board. The application should be designed to facilitate real-time data analysis and provide users with an intuitive interface to explore decoded data. Here’s a detailed breakdown of the application’s requirements and features:

1. **Data Decoding**: Utilize the 'acmdecoder' package to decode incoming raw binary data streams from the ACM board. Ensure the decoder can handle various data formats supported by the ACM board.
2. **Real-Time Visualization**: Implement a feature to visualize decoded data in real-time using libraries such as Matplotlib or Plotly. The visualization should allow users to select which data points they want to plot, such as temperature readings, voltage levels, etc.
3. **Data Logging**: Provide an option for users to log decoded data into a CSV file for later analysis. This feature should allow users to specify the frequency of logging and the format of the data.
4. **User Interface**: Develop a simple yet effective graphical user interface (GUI) using Tkinter or PyQt. The GUI should include controls to start/stop data decoding, toggle real-time visualization, and manage data logging.
5. **Customizable Alerts**: Integrate alert notifications based on predefined thresholds for specific data points. For example, if a temperature reading exceeds a certain threshold, the application should notify the user via a pop-up message or sound alert.
6. **Configuration Settings**: Allow users to configure settings such as baud rate, data format, and alert thresholds through a settings menu within the GUI.
7. **Help and Documentation**: Include comprehensive documentation within the application and provide a help section within the GUI that explains how to use each feature.

Your task is to design and implement this mini-application, ensuring it is well-structured, modular, and easy to extend. Focus on making the application user-friendly and efficient in handling real-time data streams.