🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential harvesting. The metadata risk suggests potential inactivity or newness from the authors but does not conclusively point towards malicious intent.

No network calls or shell executions detected.
Low risk of code obfuscation or credential harvesting.

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 risk of unauthorized command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The low number of packages and lack of PyPI classifiers suggest the authors may be new or less active, raising some suspicion but not definitive proof of malintent.

🔬 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: uni-muenster.de>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Leon Glüsing, Stefan Walter-Heßbrüggen" 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 PyDistintoX

Create a text analysis mini-app called 'TextContrastor' which utilizes the PyDistintoX package to perform contrastive text analysis. This app will take two sets of text inputs from users, analyze them using PyDistintoX's 16 statistical distinctiveness measures, and provide a comprehensive report on their similarities and differences. Here are the steps and features for your project:

1. **Setup**: Install PyDistintoX and any other necessary Python packages. Ensure the environment is set up for both command-line interface (CLI) and library usage.
2. **User Interface**: Develop a simple and user-friendly interface where users can input their text data. This could be either through a web form or a CLI prompt.
3. **Data Processing**: Implement a feature within TextContrastor to preprocess the text data (e.g., removing stop words, tokenizing, etc.) before analysis.
4. **Analysis**: Use PyDistintoX to compute the 16 statistical distinctiveness measures between the two sets of text data. Display these measures in a clear and understandable format.
5. **Visualization**: Include visual aids like graphs or charts to help users understand the differences and similarities between the texts.
6. **Report Generation**: Automatically generate a detailed report summarizing the findings from the analysis. This report should include insights derived from the statistical measures and visual aids.
7. **Customization**: Allow users to choose specific statistical measures they are interested in analyzing further.
8. **Export Options**: Provide options to export the analysis results in various formats such as PDF, CSV, or HTML.
9. **Documentation**: Create comprehensive documentation for both users and developers, detailing how to use TextContrastor effectively.

This project aims to showcase the power of PyDistintoX in performing detailed contrastive text analysis and making complex data easily accessible and understandable to its users.