AI Analysis
The package exhibits low risk across all assessed categories with no indications of malicious behavior or network/shell activity. However, slight concerns about metadata quality and maintainer activity suggest cautious monitoring.
- Low risk in network, shell, obfuscation, and credential aspects.
- Metadata quality and maintainer activity could be improved.
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 signs of malicious shell command execution.
- Obfuscation: No obfuscation patterns detected, suggesting low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows some signs of low maintainer activity and metadata quality, but lacks clear indicators of malicious intent.
Package Quality Overall: Low (2.0/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Brief PyPI description (362 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked β 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: gmail.com>
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a mini-application called 'ETF Designer' that allows users to design electronic test fixture (ETF) layouts using the 'alg-etf' Python package. This application should serve as a user-friendly interface for generating ETF layouts, which can then be exported for further use or manufacturing. Hereβs a detailed breakdown of the steps and features for building this application: 1. **Setup Environment**: Ensure you have Python installed on your machine. Install the 'alg-etf' package via pip. 2. **User Interface Design**: Develop a simple GUI using a Python library such as Tkinter or PyQt. The UI should allow users to input basic parameters necessary for ETF layout generation. 3. **Parameter Input**: Allow users to specify key parameters like size, type of components, connections, and any other relevant details required for the ETF layout. 4. **Generate Layout**: Integrate the 'alg-etf' package to generate the ETF layout based on user inputs. Display the generated layout visually within the application. 5. **Export Functionality**: Implement a feature to export the generated layout into common file formats such as PDF or SVG for easy sharing or printing. 6. **Error Handling**: Include robust error handling to manage invalid inputs and provide meaningful feedback to users. 7. **Documentation and Help**: Provide clear documentation and tooltips within the application to guide users through the process. Suggested Features: - Support for multiple ETF types (e.g., different connectors) - Preview mode before finalizing the layout - Option to save layouts for future editing - Integration with popular CAD software for advanced editing By following these steps and incorporating the suggested features, you will create a valuable tool for engineers and designers working with ETFs.