AI Analysis
Final verdict: SAFE
The package has minimal risks associated with network calls, shell execution, and obfuscation. However, it exhibits low maintenance effort, which could indicate potential issues in future updates.
- Low metadata quality
- Minimal functional risks
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network functionality.
- Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows low effort in maintenance and lacks a proper author description, indicating potential neglect or misuse.
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: gmail.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
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)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with ab-pydantic-patch
Create a RESTful API mini-application using FastAPI and SQLAlchemy that manages a simple database of books. This application will utilize the 'ab-pydantic-patch' package to handle PATCH requests efficiently by allowing partial updates to book records without requiring all fields to be specified every time. Here's a detailed breakdown of the project requirements: 1. **Setup**: Install FastAPI, SQLAlchemy, and 'ab-pydantic-patch'. Set up a basic FastAPI application and configure a SQLite database using SQLAlchemy. 2. **Models**: Define a Book model using SQLAlchemy ORM, which includes fields such as title, author, publication_date, and ISBN. 3. **Pydantic Models**: Create corresponding Pydantic models for the Book entity. Use 'ab-pydantic-patch' to define utility types for these models, such as PartialBook (for partial updates), RequiredBook (for full updates), and PickBook (to select specific fields). 4. **Database Operations**: Implement CRUD operations for the Book model using FastAPI endpoints. Focus on the PATCH endpoint, utilizing the PartialBook model to demonstrate how only necessary fields can be updated. 5. **Testing**: Write tests using pytest to ensure that the PATCH operation works as expected with different combinations of fields being updated. 6. **Documentation**: Provide comprehensive documentation on how each endpoint functions, especially focusing on the PATCH endpoint and its usage of 'ab-pydantic-patch'. 7. **Deployment**: Optionally, deploy the application using Docker for demonstration purposes. This project aims to showcase the flexibility and power of 'ab-pydantic-patch' in handling complex data structures and operations within a web application context.