🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network usage, shell execution, and obfuscation. However, due to its lack of metadata and description, there are concerns regarding its legitimacy.

Minimal metadata provided
No package description

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interactions for its functionality.
Shell: No shell execution patterns detected, indicating no immediate signs of executing external commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new with minimal information provided by the author, raising concerns about its legitimacy.

🔬 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

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 10.0

5 maintainer concern(s) found

Only one version has ever been released — brand new package
Package is very new: uploaded 1 day(s) ago
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 SPPPPGNet

Create a mini-application named 'SleepPhaseGuardian' using the Python package 'SPPPPGNet'. This application will serve as a tool for individuals interested in understanding their sleep phases more deeply. Here’s a step-by-step guide on how to develop it:

1. **Setup Environment**: Begin by setting up a virtual environment for your project and install the necessary packages including 'SPPPPGNet'. Ensure you have all dependencies correctly installed.
2. **Data Collection**: Design a user-friendly interface where users can upload their PPG (Photoplethysmogram) data, which is typically collected from wearable devices like smartwatches. Ensure data privacy and security measures are in place.
3. **Integration with SPPPPGNet**: Utilize the 'SPPPPGNet' package to process the uploaded PPG data. This involves loading the model and applying it to the dataset to predict sleep phases such as REM, NREM, and Wake states.
4. **Visualization**: Develop a feature within the application that visualizes the sleep phase predictions over time. Users should be able to see their sleep patterns clearly, possibly through graphs or charts.
5. **Insight Generation**: Based on the predicted sleep phases, generate insights for the user. For example, inform them about the total time spent in each sleep phase and any potential issues such as insufficient deep sleep.
6. **User Feedback**: Implement a feedback mechanism where users can provide input on the accuracy of the predictions and suggest improvements.
7. **Reporting Tool**: Finally, include a reporting tool that allows users to export their sleep analysis into a PDF or CSV format for personal records or consultation with healthcare professionals.

By following these steps, you'll create a valuable tool for anyone looking to improve their sleep quality using advanced AI techniques.