AI Analysis
The package exhibits low risks across all assessed categories with no clear evidence of malicious activity. However, its low maintenance status raises some concerns.
- Low risk scores in all major categories
- Lack of package description
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 direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintenance and could indicate a low-effort attempt, but lacks clear indicators of malicious intent.
Package Quality Overall: Low (1.2/10)
No test suite detected
No test files or test-runner configuration detected
No documentation detected
No documentation URL, doc files, or meaningful description found
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: 163.com>
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
3 maintainer concern(s) found
Only one version has ever been released β brand new packageAuthor "dujiahao666" 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 Python-based mini-application named 'ChuMenExplorer' which leverages the 'amxk-mcp-chumen' package to explore and visualize various cultural landmarks across different regions of China. This application will serve as an educational tool to help users understand the historical and cultural significance of these landmarks. Hereβs a detailed breakdown of the application's functionalities and steps to implement it using the 'amxk-mcp-chumen' package: 1. **Project Setup**: Begin by installing the 'amxk-mcp-chumen' package via pip. Ensure your development environment is set up correctly for Python. 2. **Data Collection**: Use 'amxk-mcp-chumen' to gather information about cultural landmarks from its database. The package should provide methods to fetch data based on region, type of landmark, and other filters. 3. **Feature Implementation**: - **Interactive Map Visualization**: Implement an interactive map where users can click on different regions to see landmarks highlighted. Utilize libraries like Folium or Plotly for map visualization. - **Detailed Information Panel**: When a user clicks on a landmark on the map, display detailed information about it in a sidebar or popup panel. Include images, historical facts, and cultural significance. - **Search Functionality**: Allow users to search for specific landmarks by name or type. Integrate a search bar that interacts with the 'amxk-mcp-chumen' API to fetch results dynamically. - **Filter Options**: Provide filter options such as 'By Region', 'By Era', and 'By Type' to refine the search results. Users should be able to apply multiple filters at once. 4. **User Interface**: Design a clean and intuitive user interface using HTML, CSS, and JavaScript (for frontend interactivity). For backend logic, use Flask or Django. 5. **Testing & Deployment**: Thoroughly test the application to ensure all features work as expected. Deploy the application using platforms like Heroku or AWS for public access. The 'amxk-mcp-chumen' package is central to fetching and filtering data about cultural landmarks, making it essential for the functionality and success of 'ChuMenExplorer'.
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