🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network and shell execution, but the metadata quality and maintainer engagement are poor, which raises suspicion about its origin and intent.

Low maintainer engagement
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution detected, indicating no immediate signs of executing system commands.
Metadata: The package shows signs of low maintainer engagement and poor metadata quality, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

1 test file(s) detected (e.g. test_search.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (1226 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 project
5 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 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" 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 aidu-support

Create a Python-based mini-application called 'AIDu Assistant' which serves as a versatile tool for managing and monitoring AI models in real-time. This application will utilize the 'aidu-support' package to streamline various functionalities essential for efficient AI model management. Here’s a detailed step-by-step guide on how to develop this application:

1. **Project Setup**: Initialize a new Python project and install the 'aidu-support' package along with any other necessary dependencies such as Flask for web framework, and SQLAlchemy for database operations.
2. **Database Integration**: Use 'aidu-support' to integrate with a SQLite database to store information about different AI models including their names, versions, deployment status, and performance metrics.
3. **Model Management**: Implement functionalities within the application to add, update, delete, and query information related to AI models. Leverage 'aidu-support' to handle common tasks such as logging, error handling, and configuration management efficiently.
4. **Real-Time Monitoring**: Develop a feature that allows users to monitor the performance of deployed AI models in real-time. This could include metrics like accuracy, response time, and error rates. Utilize 'aidu-support' for its utility functions that help in collecting and processing these metrics effectively.
5. **User Interface**: Create a simple yet effective user interface using Flask that displays all the above-mentioned functionalities in an organized manner. Ensure that the UI is interactive and provides easy access to all features.
6. **Security Measures**: Incorporate basic security measures such as user authentication and authorization using 'aidu-support' to ensure only authorized personnel can manage the AI models.
7. **Documentation**: Finally, document all steps involved in setting up and running the application, along with any configurations required from the 'aidu-support' package. Provide examples and best practices for integrating 'aidu-support' into similar projects.

The goal of 'AIDu Assistant' is to provide a robust solution for managing AI models, making it easier for developers and data scientists to focus more on innovation rather than the mundane aspects of model management.