AI Analysis
The package has minimal risks associated with it, showing no signs of malicious activity or poor coding practices. It appears to be a straightforward tool for integrating with Modal's AutoDiscovery feature.
- No network calls detected
- No shell executions detected
- Low metadata quality but no clear malicious indicators
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell executions detected, indicating the package does not attempt to execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows some signs of low maintenance and metadata quality but does not exhibit clear malicious indicators.
Package Quality Overall: Low (2.0/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (923 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 — 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
2 maintainer concern(s) found
Author "Allen Institute for Artificial Intelligence" 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 web-based inventory management system using Python's Flask framework and the 'asta-autodiscovery-modal' package. This system should allow users to easily manage their inventory items through a user-friendly interface. Here are the key functionalities you need to implement: 1. **Inventory Item Management**: Users should be able to add, edit, delete, and view details of inventory items. Each item will have attributes like name, category, quantity, price, and description. 2. **Category Management**: Allow users to create, edit, delete, and view categories of inventory items. Categories help organize the items into meaningful groups. 3. **Search Functionality**: Implement a search feature that allows users to find specific items or categories based on keywords. 4. **User Authentication**: Ensure that only authenticated users can access the inventory management features. Use basic authentication for simplicity. 5. **Auto-Discovery Modal Integration**: Utilize the 'asta-autodiscovery-modal' package to enhance the user experience. Specifically, use it to automatically display modals when users perform certain actions such as adding a new item or editing an existing one. These modals should provide quick feedback or additional options relevant to the action being performed. 6. **Responsive Design**: Make sure the application is responsive and works well on both desktop and mobile devices. The 'asta-autodiscovery-modal' package will be used to dynamically show modals based on user interactions without requiring manual intervention. For example, when a user clicks on the 'Add New Item' button, a modal should appear automatically to guide them through the process of adding a new item. Similarly, after an item is added or edited, a confirmation modal should pop up to notify the user about the successful operation. Your task is to integrate this package seamlessly into your Flask application to ensure a smooth and intuitive user experience.
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