AI Analysis
Final verdict: SAFE
The package shows minimal risks across various checks, indicating a low likelihood of malicious intent. However, the maintainer's limited activity and lack of associated repositories slightly increase the uncertainty.
- Low risk scores in network, shell, obfuscation, and credential areas.
- Maintainer has only one package and no associated GitHub repository.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
- Shell: No shell execution patterns detected, indicating no immediate risk of command injection or unauthorized system access.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package and no associated GitHub repository, which may indicate a new or less active developer.
Package Quality Overall: Low (2.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (4797 chars)
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low
Type Annotations
1.0
No type annotations detected
No type annotations, py.typed marker, or stub files detected
○ 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 2.0
1 maintainer concern(s) found
Author "AIMMS B.V." 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 aimmspy
Create a Python-based utility that integrates with the AIMMS optimization platform using the 'aimmspy' package. This utility will serve as a bridge between Python and AIMMS, enabling users to perform complex optimization tasks more efficiently. Your task is to develop a simple yet powerful application that demonstrates the following functionalities: 1. **Data Import/Export**: Allow users to import data from various sources (CSV, Excel, databases) into AIMMS using pandas, polars, or pyarrow. Conversely, allow the export of results back to these formats. 2. **Model Execution**: Provide a mechanism to execute pre-defined AIMMS models with user-supplied parameters. The application should be able to pass these parameters seamlessly between Python and AIMMS. 3. **Result Analysis**: Implement basic analysis tools in Python to interpret and visualize the results returned from AIMMS. This could include plotting graphs, generating summary statistics, etc. 4. **Interactive Interface**: Develop a simple GUI using a library like Tkinter or PyQt that allows users to interactively input data, select models, and view results. 5. **Error Handling & Logging**: Ensure robust error handling and logging mechanisms to help diagnose issues when interacting with AIMMS. The application should leverage 'aimmspy' to handle the low-level communication between Python and AIMMS, allowing you to focus on higher-level logic and functionality. Remember to document your code thoroughly and provide clear instructions on how to install and run the application.
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