AI Analysis
The package exhibits low risks in terms of network calls, shell execution, obfuscation, and credential harvesting. However, the incomplete author details and lack of an associated GitHub repository increase suspicion.
- Incomplete author details
- No associated GitHub repository
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires external services.
- Shell: No shell execution detected, indicating the package does not perform system-level operations.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package has no associated GitHub repository and the author details are incomplete, which raises some suspicion but does not conclusively indicate malicious intent.
Package Quality Overall: Low (4.8/10)
Test suite present — 3 test file(s) found
Test runner config found: pyproject.toml3 test file(s) detected (e.g. test_autoplot.py)
Some documentation present
Detailed PyPI description (4008 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
Partial type annotation coverage
5 type-annotated function signatures (partial)
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: nucos.de>
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a Python-based document generator application named 'DocCraft' that leverages the 'autobasedoc' package to simplify the creation of visually appealing reports and documents. The application should allow users to input various types of data such as text, tables, images, and charts, which will then be compiled into a professional-looking PDF document. Here are the key steps and features you should include in your project: 1. **User Interface**: Develop a simple GUI using a Python library like Tkinter or PyQt to make it user-friendly. 2. **Data Input**: Allow users to enter text, upload images, and import CSV files for generating tables and charts within the document. 3. **Report Generation**: Utilize 'autobasedoc' to format and structure the content into a PDF document. This includes setting margins, fonts, headers, footers, and page numbers. 4. **Customization Options**: Provide options to customize the layout, color schemes, and overall design of the document. 5. **Export Functionality**: Implement a feature to export the generated document as a PDF file to the user's specified location. 6. **Error Handling**: Ensure robust error handling to manage invalid inputs or file issues gracefully. 7. **Documentation**: Write comprehensive documentation explaining how to install the application, use its features, and troubleshoot common issues. Incorporate 'autobasedoc' by integrating its core functionalities to streamline the process of adding formatted text, images, and charts into the document. For instance, use 'autobasedoc' to automatically adjust the layout based on the type and amount of content added, ensuring a consistent and professional appearance throughout the document.
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