🤖 AI Analysis

Final verdict: SAFE

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)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: 163.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "dujiahao666" 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 amxk-mcp-chumen

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. Here’s 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'.