AI Analysis
Final verdict: SAFE
The package presents minimal risks as it lacks network, shell, obfuscation, and credential manipulation concerns. It appears to serve its stated purpose without introducing significant security issues.
- Low risk scores across all categories.
- No indication of malicious or suspicious activity.
Per-check LLM notes
- Network: The use of asynchronous HTTP requests is common and not inherently suspicious; however, it should be verified if the package documentation explains this behavior.
- Shell: No shell execution patterns were detected, which is normal and expected.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
Package Quality Overall: Low (4.2/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (3591 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
7.0
Partial type annotation coverage
Type checker (mypy / pyright / pytype) referenced in project109 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
score 1.5
Found 1 network call pattern(s)
y: async with httpx.AsyncClient() as client: await client.post(sub_url,
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 akeneo-mock-server
Your task is to develop a small, fully-functional e-commerce product management tool using Python. This tool will interact with a mock Akeneo server implemented via the 'akeneo-mock-server' package. Your application should allow users to perform basic CRUD (Create, Read, Update, Delete) operations on products. Additionally, it should support filtering products based on specific criteria such as category, attribute values, etc.
### Project Overview:
- **Name**: E-commerce Product Manager
- **Language**: Python
- **Dependencies**: 'akeneo-mock-server', 'requests'
- **Features**:
- Create new products with attributes like name, description, price, and categories.
- List all products or filter them based on certain attributes.
- Update existing product details.
- Delete products from the system.
- Implement a simple command-line interface (CLI) for user interaction.
### Steps to Build the Application:
1. **Setup the Environment**: Install Python and necessary packages ('akeneo-mock-server', 'requests').
2. **Initialize the Mock Server**: Start the 'akeneo-mock-server' locally to simulate an Akeneo instance.
3. **Design the CLI**: Create commands for each of the CRUD operations and filtering.
4. **Implement CRUD Operations**: Write functions to handle creating, reading, updating, and deleting products.
5. **Filtering Products**: Allow users to filter products based on specific attributes.
6. **Testing**: Test each functionality thoroughly to ensure they work as expected.
7. **Documentation**: Provide clear documentation on how to use the CLI and any additional setup required.
### Detailed Instructions:
- Use the 'akeneo-mock-server' package to set up the mock server at the start of your application. Ensure it runs in a separate process or thread if needed.
- For each operation (create, read, update, delete), design RESTful APIs that interact with the mock server using the 'requests' library.
- The CLI should be intuitive, allowing users to easily input commands and arguments.
- When listing products, consider implementing pagination if the number of products is large.
- Filtering should allow for multiple conditions (e.g., products under $50 and in 'Electronics' category).
- Ensure error handling is robust, providing meaningful messages to users when something goes wrong.
This project aims to showcase your ability to work with external APIs, manage data through a CLI, and effectively utilize third-party packages in Python.