AI Analysis
Final verdict: SAFE
The package shows low risks across multiple categories and has a moderate metadata risk due to the maintainer's limited history. There is no strong evidence to suggest a supply-chain attack.
- Network calls present but not unusual for the functionality described.
- No signs of obfuscation, shell execution, or credential harvesting.
Per-check LLM notes
- Network: The presence of network calls suggests the package communicates with external services, which could be normal if it's designed to interact with web APIs.
- Shell: No shell execution patterns were detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, indicating a potentially new or less active account.
Package Quality Overall: Low (2.0/10)
β Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
β Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
β Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
β Medium
Type Annotations
5.0
Partial type annotation coverage
7 type-annotated function signatures (partial)
β Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked β contributor count unavailable
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
# θ·εresponse res = requests.post(self.wsdl, json=concated_params, headers=self.headers)} } response = requests.post(url=url, headers=headers, json=data) print(response.tex
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 2.0
1 maintainer concern(s) found
Author "ailuoge" 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 ailuoge
Create a mini-application named 'AiluogeHelper' that leverages the Python package 'ailuoge' to provide users with a tool for managing and generating unique identifiers for various data types. This application should serve as both a utility and a learning tool for understanding identifier generation processes. Here are the steps and features to include: 1. **Setup Environment**: Ensure you have Python installed on your system. Install the 'ailuoge' package using pip. Set up a virtual environment for your project to keep dependencies organized. 2. **Application Structure**: Create a clean directory structure for your project including subdirectories for 'src', 'tests', and 'docs'. Within 'src', create modules for handling different aspects of identifier management such as creation, validation, and storage. 3. **Core Functionality**: Implement functions within the 'ailuoge' module that use the package to generate unique identifiers based on user input parameters like length, type (e.g., alphanumeric, numeric only), and purpose (e.g., database keys, session IDs). 4. **User Interface**: Develop a simple command-line interface (CLI) where users can interact with the application. Users should be able to specify the type and length of the identifier they want to generate, and the application should output the generated identifier. 5. **Advanced Features**: Extend the application to allow for batch processing of identifier generation requests. Users should be able to submit multiple parameters at once and receive a list of generated identifiers. 6. **Documentation**: Write comprehensive documentation for your application, explaining how each function works, how to install and run the application, and examples of usage scenarios. 7. **Testing**: Implement unit tests for each function in your application to ensure reliability and correctness. Use the 'unittest' framework provided by Python. 8. **Deployment**: Package your application as a distributable Python package. Include instructions on how others can contribute to the project through GitHub. The goal is to create a versatile tool that not only demonstrates the capabilities of the 'ailuoge' package but also provides practical value for developers needing to manage unique identifiers efficiently.
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