🤖 AI Analysis

Final verdict: SUSPICIOUS

The package appears generally safe but raises concerns due to its low metadata score and new/inactive maintainer status.

Low metadata score
New or inactive maintainer account

Per-check LLM notes

Network: The network call is likely for checking URL availability or fetching updates, which is not inherently suspicious but should be reviewed against the package's stated purpose.
Shell: No shell execution patterns were detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has a new or inactive account and the repository lacks community engagement, raising some suspicion.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

ry: response_status = urllib.request.urlopen(URL).getcode() assert response_status == 200

✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "foysalpranto121" 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 NotebookFusion

Create a mini-application called 'NotebookEnhancer' using the Python package 'NotebookFusion'. This application aims to enhance the user experience of Jupyter notebooks by adding several useful features. Here’s a detailed plan on how to implement this application:

1. **Setup Environment**: Start by setting up your development environment. Ensure you have Python installed, along with Jupyter Notebooks and the 'NotebookFusion' package.

2. **Feature Implementation**:
- **Dynamic Code Snippets**: Implement a feature where users can insert predefined code snippets into their cells with just a click. These snippets should cover common tasks such as data loading, plotting, and basic machine learning model training.
- **Automated Documentation**: Enable automatic generation of documentation for each cell. When a user runs a cell, the application should automatically generate a markdown cell below it that summarizes the executed code and its output.
- **Code Quality Checks**: Integrate a feature that performs real-time code quality checks. Highlight potential issues like unused imports, deprecated functions, and style violations.
- **Interactive Widgets**: Allow users to add interactive widgets directly from a menu. These widgets should enable users to interactively change parameters and see the results update in real-time.
- **Version Control Integration**: Add functionality that allows users to save different versions of their notebooks easily. Each version should be timestamped and stored locally or remotely.

3. **Utilizing 'NotebookFusion'**: Use the 'NotebookFusion' package to extend Jupyter notebook functionalities. For example, leverage its APIs to dynamically modify notebook content, manage cell execution order, and handle notebook metadata efficiently.

4. **Testing and Validation**: After implementing these features, thoroughly test the application to ensure all functionalities work as expected. Pay special attention to edge cases and error handling.

5. **Documentation and Deployment**: Write comprehensive documentation explaining how to use 'NotebookEnhancer', including installation instructions, usage examples, and best practices. Consider deploying the application as a Jupyter extension or a standalone tool that can be integrated into existing Jupyter environments.