AI Analysis
The package exhibits low risks across multiple categories but has a moderate metadata risk due to potential low maintainer effort, which could indicate a lack of proper maintenance or updates. This warrants further investigation.
- Low network, shell, obfuscation, and credential risks
- Moderate metadata risk due to low maintainer effort
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintainer effort and could be suspicious, but lacks clear indicators of malicious intent.
Package Quality Overall: Low (3.8/10)
Partial test coverage signals detected
Test runner config found: pyproject.toml
Some documentation present
Detailed PyPI description (1527 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
10 type-annotated function signatures detected in source
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
No author email provided
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 named 'LangGraphToolExplorer' that leverages the 'athena-ptc-middleware' package to explore and manage nested tool cards surfaced from Athena Programmatic Tool Calling (PTC) events. This application should allow users to interactively browse through different layers of nested tools, execute specific tools, and view the results or outputs directly within the app interface. Step 1: Set up your development environment with Python installed, along with the 'athena-ptc-middleware' package. Ensure you have the necessary permissions and configurations set for accessing Athena PTC services. Step 2: Design the main interface of 'LangGraphToolExplorer', which includes a sidebar for navigation and a main content area for displaying tool cards. Implement a clean and user-friendly design. Step 3: Integrate the 'athena-ptc-middleware' package into your application. Use its functionalities to fetch and display nested tool cards from Athena PTC events. Each tool card should contain relevant information such as the tool name, description, and any parameters required for execution. Step 4: Develop interactive features allowing users to expand or collapse nested tool cards, revealing more detailed information about each tool. Users should also be able to select and execute individual tools, with their outputs displayed within the application. Step 5: Enhance the application with additional features like search functionality to find specific tools, bookmarks to save frequently used tools, and a history tab to track previously executed tools and their results. Ensure your application is well-documented, with clear instructions on setup and usage. Include comments in your code to explain how 'athena-ptc-middleware' is utilized throughout the application.
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