🤖 AI Analysis

Final verdict: SAFE

The package does not exhibit any signs of malicious activity, with low risks across all assessed categories including network, shell execution, obfuscation, and credential handling.

No network calls detected
No shell execution observed
No obfuscation techniques used
No credential harvesting attempts found

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external API access.
Shell: No shell execution detected, indicating no immediate risk of command injection or system compromise.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.

🔬 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 "Artsem Bobr" 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 BobrTools

Create a data analysis mini-app named 'DataSquirrel' using the Python package 'BobrTools'. This app should serve as a user-friendly interface for analysts to load, preprocess, analyze, and visualize datasets efficiently. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Ensure you have Python installed along with BobrTools. You might also need additional packages like Pandas, Matplotlib, and Seaborn for data manipulation and visualization.
2. **User Interface Design**: Develop a simple GUI using Tkinter or Streamlit where users can upload their CSV files. The UI should be intuitive, guiding users through the process of loading data, selecting columns for analysis, and choosing visualizations.
3. **Data Loading & Preprocessing**: Utilize BobrTools’ functions to streamline the data loading and preprocessing steps. Include options for handling missing values, converting data types, and filtering rows based on user-specified criteria.
4. **Statistical Analysis**: Implement features within BobrTools to perform basic statistical analyses such as calculating mean, median, mode, standard deviation, etc., directly from the loaded dataset. Allow users to select specific columns for these operations.
5. **Visualization Tools**: Leverage BobrTools’ visualization capabilities to create interactive plots including bar charts, line graphs, scatter plots, and histograms. Enable users to customize these visuals based on their preferences.
6. **Exporting Results**: Provide an option for users to export the processed data and generated visualizations in formats like CSV, PNG, or PDF.
7. **Error Handling & Feedback**: Integrate robust error handling to manage exceptions gracefully and provide meaningful feedback to users about any issues encountered during the process.

Throughout the development process, focus on making 'DataSquirrel' accessible and powerful for analysts looking to expedite their workflow with minimal coding.