🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has notable obfuscation and metadata risks, with no provided description or author details, raising concerns about its legitimacy and purpose.

Obfuscation risk of 4/10
Metadata risk of 5/10

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or unauthorized system access.
Obfuscation: The obfuscation patterns appear to be related to model evaluation and inference, possibly not malicious but could indicate an attempt to hide code logic.
Credentials: No credentials or secrets harvesting patterns detected.
Metadata: The package shows signs of low maintenance and could potentially be suspicious due to the lack of author details and a GitHub repository.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

ab_backbone) pt_model.eval() with torch.no_grad(): for i in range(0
meters()).device pt_model.eval() # GradientExplainer requires a model that takes a pla

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" 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 TabNado

Create a desktop application using Python that allows users to manage their bookmarks efficiently. The application will utilize the 'TabNado' package, which is designed to handle tab management in web browsers but we'll adapt its functionality for bookmark management. Here’s a step-by-step guide on how to develop this application:

1. **Application Setup**: Start by setting up your development environment. Ensure you have Python installed along with the necessary libraries such as PyQt5 or Tkinter for the GUI and TabNado for managing bookmarks.
2. **User Interface Design**: Design a simple yet effective user interface where users can add, delete, and organize their bookmarks. Include fields for URL, title, and tags.
3. **Integration of TabNado**: Use TabNado's core features to simulate tab management within the application. For example, use it to create a 'tabbed' interface where each tab represents a category of bookmarks (e.g., Work, Personal, Education).
4. **Bookmark Management Features**:
- **Add Bookmark**: Users should be able to add new bookmarks by entering the URL, title, and tags. These details should be stored in a structured format like JSON or SQLite.
- **Delete Bookmark**: Implement a feature to remove bookmarks based on user selection.
- **Search Functionality**: Allow users to search for bookmarks by keyword or tag.
5. **Advanced Features**:
- **Tag System**: Introduce a tagging system where users can categorize bookmarks by adding tags.
- **Import/Export**: Enable users to import bookmarks from a file or export them.
6. **Testing and Deployment**: Test the application thoroughly to ensure all features work as expected. Once ready, deploy the application so users can download and install it easily.

This project aims to showcase how TabNado's unique capabilities can be adapted for non-traditional uses, enhancing user experience through innovative application design.