annotate

v2.4.3 suspicious
6.0
Medium Risk

Decorator to set a function's __annotations__ like Py3.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation and suspicious metadata, raising concerns about its integrity and purpose.

  • High obfuscation risk due to dynamic imports and obfuscated strings
  • Suspicious metadata with a new or inactive maintainer account
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or unauthorized system access.
  • Obfuscation: The use of dynamic imports and obfuscated strings suggests potential for hiding malicious code.
  • Credentials: No clear evidence of credential harvesting, but the presence of obfuscation raises suspicion.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (6.8/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • 3 test file(s) detected (e.g. __init__.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://karpierz.github.io/annotate/
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (6613 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 12 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 43 commits in karpierz/annotate
  • Single author but highly active (43 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • x import application about = __import__("pkg_about").about_from_setup(1) def setup(app: application.Sphinx) ->
  • PDX-License-Identifier: Zlib __import__("pkg_about").about() # Copyright (c) 2012 Adam Karpierz # SPDX-License-
✓ 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: karpierz.net>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository karpierz/annotate appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 annotate 
Your task is to develop a simple yet powerful Python utility called 'TypeSafe' that leverages the 'annotate' package to enhance type checking and documentation within functions. This tool will serve as a runtime type checker for Python scripts, ensuring that function arguments adhere to specified types as defined in their annotations. The goal is to create a robust system that not only enforces type safety but also provides clear error messages and suggestions for corrections when type mismatches occur.

Step-by-Step Development:
1. **Setup**: Begin by installing the 'annotate' package. Ensure your development environment is set up correctly with Python 3.x installed.
2. **Core Functionality**: Implement the core functionality of 'TypeSafe'. This involves writing a decorator that uses the 'annotate' package to inspect and enforce type annotations on functions. When a function decorated with TypeSafe is called, it should check if the provided arguments match the annotated types. If they do not, an informative error message should be raised.
3. **Error Handling**: Design the error messages to be user-friendly. They should clearly indicate which argument(s) failed the type check, the expected type, and the actual type received. Additionally, suggest possible corrections based on the context.
4. **Advanced Features**: Expand the utility to support more advanced use cases such as optional arguments, default values, and return type validation. For instance, if a function has an optional argument with a default value, the decorator should handle this gracefully.
5. **Integration Testing**: Write a series of test cases to validate the functionality of 'TypeSafe'. These tests should cover a wide range of scenarios including basic type checks, nested structures, and edge cases.
6. **Documentation**: Finally, document the project thoroughly. Include examples of how to use the 'TypeSafe' decorator effectively, best practices for annotating functions, and tips for debugging common issues.

Utilizing the 'annotate' Package: Throughout the development process, the 'annotate' package will be crucial. It allows you to easily add type annotations to functions even if they were originally written without them. By leveraging 'annotate', you can dynamically modify functions at runtime to include these annotations, making them compatible with the 'TypeSafe' decorator and enhancing the overall type safety and maintainability of Python codebases.

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