AI Analysis
Final verdict: SAFE
The package exhibits minimal risk indicators and no direct evidence of malicious activities or supply-chain attacks.
- No network calls detected
- No shell execution patterns found
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access to function properly.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands which could be malicious.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: The package shows some low-effort signs but no clear malicious indicators.
Package Quality Overall: Low (2.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ 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
8 type-annotated function signatures (partial)
○ 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 4.0
2 maintainer concern(s) found
Author "Gisaïa" 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 airsmodel
Create a mini-application that leverages the 'airsmodel' package to manage and analyze item registration data in a retail environment. This application will serve as a simplified version of an inventory management system, focusing on the registration, tracking, and analysis of product items using the ARLAS Item Registration Service Model provided by 'airsmodel'. ### Step-by-Step Guide: 1. **Setup**: Begin by installing the 'airsmodel' package and setting up a basic Flask or Django web framework for your application. 2. **Item Registration**: Implement functionality to register new items into the system. Each item should have attributes like SKU, name, category, price, and stock level. 3. **Data Analysis**: Utilize 'airsmodel' to perform basic data analysis such as calculating total inventory value, identifying low-stock items, and generating sales reports. 4. **User Interface**: Design a simple user interface where users can input new items, view existing inventory, and access analytical reports. 5. **Security Measures**: Ensure that the application has basic security measures in place, such as user authentication and authorization. 6. **Testing**: Thoroughly test the application to ensure all functionalities work as expected and are secure. 7. **Deployment**: Deploy the application on a cloud platform like Heroku or AWS. ### Suggested Features: - **Real-time Stock Alerts**: Notify users when stock levels fall below a certain threshold. - **Search Functionality**: Allow users to search for specific items by SKU or name. - **Export Reports**: Provide the ability to export analytical reports in CSV or PDF format. - **Admin Dashboard**: Create an admin dashboard for managing user roles and permissions. ### Utilization of 'airsmodel': - Use 'airsmodel' to handle the backend logic for item registration, ensuring data integrity and consistency. - Leverage 'airsmodel' for its advanced analytics capabilities to generate meaningful insights from the inventory data. - Integrate 'airsmodel' with the frontend to provide real-time updates and interactive visualizations of inventory status and trends.