🤖 AI Analysis

Final verdict: SAFE

The package PBstats has minimal risks associated with network, shell, and obfuscation activities. However, it exhibits some concerns regarding metadata quality and maintainer activity.

Low risk in network, shell, and obfuscation activities
Metadata quality and maintainer activity are questionable

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 detected, reducing the risk of malicious activities like code injection.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, but there are no clear indicators of malicious intent.

🔬 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 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 PBstats

Your task is to develop a small but impactful application that leverages the 'PBstats' library for signal-aware data analysis and preprocessing. This application will serve as a tool for researchers and data analysts who work with complex signal data, such as audio signals or sensor data from IoT devices. The app will allow users to upload their signal data, apply various preprocessing techniques, perform statistical analyses, and visualize the results.

Here are the key steps and features your application should include:
1. **Data Upload**: Users should be able to upload their signal data files (e.g., .wav for audio, .csv for sensor data).
2. **Preprocessing**: Implement basic preprocessing functionalities provided by 'PBstats', such as normalization, filtering, and segmentation of the signal data.
3. **Statistical Analysis**: Use 'PBstats' to perform advanced statistical analyses on the preprocessed data. This could include calculating signal-to-noise ratios, identifying peaks and troughs, or conducting spectral analysis.
4. **Visualization**: Create visualizations of the original data, preprocessed data, and analysis results using matplotlib or another plotting library. These visualizations should clearly show the differences between the raw and processed data, as well as highlight any significant findings from the statistical analysis.
5. **Report Generation**: Allow users to generate a comprehensive report summarizing their data, the preprocessing steps taken, the statistical analyses performed, and key visualizations. This report should be exportable as a PDF or HTML file.
6. **User Interface**: Develop a simple yet intuitive user interface using a web framework like Flask or Dash to make the application accessible to non-technical users.

Your goal is to create a versatile tool that not only showcases the capabilities of 'PBstats' but also provides real value to its users through practical applications. Ensure that your application is well-documented, easy to install, and includes example datasets and instructions for use.