AI Analysis
The package shows minimal risks across all assessed categories, with no detected network calls, shell executions, obfuscations, or credential harvesting attempts. However, the metadata risk score is slightly elevated due to the maintainer's limited presence.
- No network calls detected
- Maintainer has only one package and lacks a GitHub repository
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 direct command execution from the package.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
- Metadata: The maintainer has only one package and no associated GitHub repository, which could indicate a less experienced or potentially suspicious actor.
Package Quality Overall: Low (2.0/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Detailed PyPI description (6059 chars)
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
No type annotations detected
No type annotations, py.typed marker, or stub files detected
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
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: orangefox.tech
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
1 maintainer concern(s) found
Author "Yacha" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a command-line utility called 'ArgumentSifter' that leverages the 'ass-tg' Python package to manage and manipulate command-line arguments efficiently. This utility will serve as a robust tool for developers who frequently work with complex command-line interfaces and need a streamlined way to parse, validate, and handle these arguments. The primary goal of 'ArgumentSifter' is to simplify the process of dealing with command-line arguments, making it easier to focus on the core functionality of their applications. **Core Features:** 1. **Argument Parsing:** Automatically parse command-line arguments provided by users, utilizing 'ass-tg' to ensure all necessary arguments are correctly identified and processed. 2. **Validation:** Implement a series of validation checks using 'ass-tg' to ensure that all parsed arguments meet specific criteria (e.g., file paths exist, numerical values fall within acceptable ranges). 3. **Help Documentation:** Generate detailed help documentation for the command-line interface based on the defined arguments, providing clear descriptions and examples for each argument type supported by 'ass-tg'. 4. **Customizable Argument Handling:** Allow users to define custom argument types and handling logic through configuration files or directly via the command line, leveraging 'ass-tg' to dynamically apply these configurations during runtime. 5. **Error Reporting:** Provide informative error messages when invalid arguments are detected, guiding users on how to correct their input. **How 'ass-tg' is Utilized:** - 'ass-tg' will be the backbone of 'ArgumentSifter', providing the framework and tools needed to handle command-line arguments effectively. It will be responsible for parsing arguments, validating them against predefined rules, and generating help documentation. Users will interact with 'ass-tg' through a simple, intuitive API exposed by 'ArgumentSifter', allowing for easy integration into existing projects without requiring deep knowledge of its underlying mechanisms.
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