AI Analysis
Final verdict: SUSPICIOUS
The package shows minimal risk in terms of network, shell, and obfuscation activities. However, the metadata risk score of 4 out of 10 due to low activity and missing author details raises concerns about its legitimacy and maintenance status.
- Low metadata activity and missing author details
- Potential abandoned package
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
- Shell: No shell execution detected, which is expected as most packages do not require system-level commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
- Metadata: Low activity and missing author details suggest potential low effort or abandoned package.
Package Quality Overall: Low (4.4/10)
✦ High
Test Suite
9.0
Test suite present — 3 test file(s) found
Test runner config found: pyproject.toml3 test file(s) detected (e.g. test_matchers.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (1894 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
45 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
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
No author email provided
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 annassert
Create a Python utility named 'AssertMaster' that leverages the 'annassert' package to enhance code quality and maintainability by automatically converting Python assertions into type annotations and vice versa. This tool will help developers ensure their functions are robust and type-safe without manually writing extensive type hints. Step 1: Define the core functionality of AssertMaster. It should take as input a Python function (including its assertions) and output either a version of the function with assertions converted into type annotations, or a version with type annotations converted into assertions. Step 2: Implement a user-friendly command-line interface for AssertMaster. Users should be able to run AssertMaster on a specific file or directory containing Python code. The utility should provide options to choose between assertion-to-annotation conversion or annotation-to-assertion conversion. Step 3: Add error handling and validation checks. AssertMaster must validate if the provided input is a valid Python function and handle cases where the conversion cannot be performed due to unsupported Python syntax or complex assertion structures. Step 4: Integrate unit tests using a testing framework like pytest to verify the correctness of both the conversion process and the overall utility functionality. Suggested Features: - Support for multiple assertion types including equality, inequality, truthiness, etc. - Ability to convert type annotations for basic data types and custom classes. - Option to preserve original comments and docstrings during the conversion process. - Provide visual feedback through the CLI for successful conversions and errors encountered. How 'annassert' is utilized: - Use 'annassert' to parse the input Python function and extract assertions. - Apply 'annassert' transformation logic to convert extracted assertions into appropriate type annotations. - Conversely, use 'annassert' to analyze type annotations and generate equivalent assertions. - Ensure 'annassert' is integrated seamlessly into AssertMaster's workflow, providing accurate and efficient conversions.
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