🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories checked. It does not engage in potentially harmful activities such as making network calls, executing shell commands, or using obfuscation techniques.

Low metadata risk
No network calls detected
No shell execution detected

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access to function.
Shell: No shell execution detected, reducing risk of command injection or privilege escalation.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating safe handling of secrets and credentials.
Metadata: The maintainer has only one package, which could indicate a new or less active account, but no other red flags are present.

🔬 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 2.0

1 maintainer concern(s) found

Author "Izak" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aasu

Create a mini-application called 'QuickStats' that leverages the Python package 'aasu' to provide users with quick and easy access to statistical data and services. The app should allow users to input a dataset or URL to a CSV file, and then perform various statistical analyses on the data, including but not limited to mean, median, mode, standard deviation, and correlation coefficients. Additionally, the application should offer the ability to visualize the data through simple plots like histograms and scatter plots. Here’s how you can structure the development process:

1. **Setup**: Install the necessary packages, including 'aasu', pandas, matplotlib, and seaborn.
2. **Data Input**: Develop a user-friendly interface that allows users to either upload a local CSV file or enter a URL to a CSV file online. Ensure error handling for invalid inputs.
3. **Data Processing**: Use 'aasu' to clean and preprocess the data, ensuring it's ready for analysis. This includes handling missing values, outliers, and data type conversions.
4. **Statistical Analysis**: Implement functions within your application that use 'aasu' to calculate key statistical measures such as mean, median, mode, standard deviation, and correlation coefficients. Display these results clearly to the user.
5. **Visualization**: Create visual representations of the data using matplotlib and seaborn, such as histograms, box plots, and scatter plots. Use 'aasu' to enhance these visualizations with additional utilities if available.
6. **Reporting**: Allow users to export their analysis and visualizations into PDF or HTML format, complete with a summary of the statistical findings.
7. **User Interface**: Design an intuitive and interactive UI using a web framework like Streamlit or Flask, making sure the app is accessible via a web browser.
8. **Testing**: Rigorously test your application to ensure accuracy in calculations, functionality in visualizations, and usability in the interface.
9. **Documentation**: Provide comprehensive documentation detailing how to install, use, and extend the application, including examples and best practices.