🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks in terms of network, shell, and obfuscation activities but has a higher metadata risk due to a new or inactive maintainer with limited PyPI presence.

metadata risk due to new or inactive maintainer
package name mismatch in installation command

Per-check LLM notes

Network: No network calls detected, which is normal for most utility packages.
Shell: No shell execution patterns detected, indicating no immediate signs of malicious activity.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of a new or inactive maintainer with limited presence on PyPI.

🔬 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: dhigroup.com>

✓ 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 name is missing or very short
Author "" 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 abja-utils

Create a mini-application called 'DataCleaner' which leverages the functionalities provided by the 'abja-utils' package to streamline data preprocessing tasks. This application should be designed to help users efficiently clean and prepare datasets for further analysis or machine learning tasks. Here are the key steps and features your application should include:

1. **Setup and Importation**: Begin by setting up a new Python environment and installing the 'abja-utils' package. Ensure you have other necessary libraries like pandas installed as well.
2. **User Interface**: Design a simple command-line interface (CLI) where users can interact with the application. They should be able to load their dataset from a CSV file using the CLI.
3. **Data Loading**: Utilize 'abja-utils' to efficiently load and handle large datasets. Implement a function that reads CSV files into a DataFrame and handles missing values intelligently using 'abja-utils' functionalities.
4. **Data Cleaning Functions**: Develop several cleaning functions within 'DataCleaner'. These should include removing duplicates, handling missing data, converting data types, and standardizing formats (e.g., dates).
5. **Feature Engineering**: Integrate 'abja-utils' tools to perform basic feature engineering tasks such as encoding categorical variables, creating new features based on existing ones, and scaling numerical features.
6. **Visualization**: Incorporate simple visualizations to help users understand their data better before and after cleaning. Use matplotlib or seaborn for plotting, alongside 'abja-utils' for data manipulation.
7. **Saving Cleaned Data**: Finally, provide functionality to save the cleaned dataset back into a CSV file. Users should be able to specify the output filename and directory.
8. **Documentation and Testing**: Write clear documentation explaining each feature and how to use it. Include unit tests for all major functions to ensure reliability.

Your goal is to create a robust yet user-friendly tool that makes data preparation straightforward and efficient, utilizing the powerful utilities provided by 'abja-utils'.