AI Analysis
The package exhibits signs of low maintenance and development effort, raising concerns about its reliability and security. Despite no direct malicious activities detected, the potential misuse of shell commands warrants caution.
- Signs of low maintainer effort and early-stage development.
- Potential risks associated with the execution of shell commands.
Per-check LLM notes
- Network: No network calls detected, which is low risk.
- Shell: Execution of shell commands can be risky if not properly sanitized or intended for legitimate use. Further investigation into the context and necessity of shell command execution is recommended.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintainer effort and could be suspicious, but there's no clear evidence of malice or typosquatting.
Package Quality Overall: Low (3.8/10)
Partial test coverage signals detected
Test runner config found: pyproject.toml
Some documentation present
Brief PyPI description (336 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
Partial type annotation coverage
249 type-annotated function signatures detected in source
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
Found 1 shell execution pattern(s)
) res = sp.Popen(cmd, shell=True, stdout=sp.PIPE, stderr=sp.PIPE, text=True) if res.
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
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)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a simple yet engaging application using Python and the 'autestoy' package that automates the testing of basic arithmetic operations such as addition, subtraction, multiplication, and division. This application will serve as both a demonstration of 'autestoy' capabilities and a practical tool for verifying the accuracy of arithmetic functions. Step 1: Set up your Python environment and install 'autestoy'. Ensure you have Python installed on your machine and use pip to install 'autestoy'. Step 2: Design the structure of your application. Create a main module that imports 'autestoy' and defines the arithmetic functions (addition, subtraction, multiplication, division). Each function should take two numbers as input and return the result of the corresponding operation. Step 3: Utilize 'autestoy' to define test cases for each arithmetic function. Write test cases that cover a range of scenarios including positive numbers, negative numbers, zero, and edge cases (e.g., division by zero). Step 4: Implement error handling within the arithmetic functions to manage exceptions gracefully. For example, ensure the division function handles division by zero without crashing the application. Step 5: Integrate 'autestoy' into your application to run the defined test cases automatically. Use 'autestoy' to execute these tests and display the results in a user-friendly format, indicating which tests passed and which failed. Suggested Features: - A command-line interface (CLI) that allows users to select which arithmetic operation to test. - A logging mechanism to record test outcomes and errors. - An option to save test results to a file for future reference. - A feature to visualize test results using simple ASCII charts or tables. By following these steps, you'll create a robust, automated testing utility that showcases the simplicity and effectiveness of 'autestoy' for managing test cases in Python applications.
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