AI Analysis
Final verdict: SUSPICIOUS
The package exhibits some level of obfuscation that may be used to evade detection, and the maintainer's metadata raises concerns about their experience and activity level.
- obfuscation risk of 6/10
- maintainer has only one package and no visible git repo
Per-check LLM notes
- Obfuscation: The code shows signs of obfuscation through unconventional import methods which could be used to evade simple static analysis.
- Credentials: No clear patterns indicating credential harvesting were detected.
- Metadata: The maintainer has only one package and the git repository is not found, which could indicate a less experienced or inactive developer.
Package Quality Overall: Low (2.8/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (28635 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
93 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 8.0
Found 4 obfuscation pattern(s)
cancelled_error = getattr(__import__("asyncio"), "CancelledError") invalid_state_error = getattr(__impoinvalid_state_error = getattr(__import__("asyncio"), "InvalidStateError") incomplete_read_error = getattr(_complete_read_error = getattr(__import__("asyncio"), "IncompleteReadError") limit_overrun_error = getattr(_limit_overrun_error = getattr(__import__("asyncio"), "LimitOverrunError") if isinstance(exc, cancelled_err
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Aniket Modi" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with aniket-tools
Your task is to develop a mini-application named 'FastErrorManager' using Python, which will leverage the 'aniket-tools' package to manage API responses and handle exceptions efficiently. This application will serve as a simple RESTful service using FastAPI, allowing users to perform basic CRUD operations on a collection of books. Additionally, it will include robust error handling and logging mechanisms provided by 'aniket-tools'. Here’s a step-by-step guide on how to implement this project: 1. **Setup Your Environment**: Start by setting up your Python environment and installing necessary packages including FastAPI, Uvicorn, and 'aniket-tools'. 2. **Define Book Model**: Create a Pydantic model to represent a book entity, including fields such as title, author, publication date, etc. 3. **Create FastAPI Application**: Initialize your FastAPI application and set up routes for CRUD operations (Create, Read, Update, Delete). 4. **Implement Data Storage**: For simplicity, you can use an in-memory dictionary to store book data. However, consider integrating with a database like SQLite for persistent storage. 5. **Integrate 'aniket-tools'**: Use 'aniket-tools' to wrap your API endpoints to automatically handle common HTTP errors and exceptions. Ensure that all errors are logged appropriately using the logging utilities from 'aniket-tools'. 6. **Enhance User Experience**: Implement custom error messages for specific exceptions, ensuring that the user receives clear and concise feedback when something goes wrong. 7. **Testing**: Write tests to ensure that each endpoint works correctly under normal conditions and when exceptions occur. Use 'aniket-tools' to validate that errors are being handled as expected. 8. **Documentation**: Provide comprehensive documentation for your API, detailing how to interact with each endpoint and what kind of responses/errors to expect. Suggested Features: - Detailed logging for every request and response. - Customizable error messages based on the type of exception. - Support for pagination and filtering when listing books. - Validation of input data before processing requests. By following these steps and utilizing 'aniket-tools', you will create a robust, efficient, and user-friendly mini-application that demonstrates the power of integrating third-party utilities into a FastAPI project.
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