🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network calls, shell execution, and code obfuscation. However, metadata analysis raises concerns due to missing maintainer history and a GitHub repository link, suggesting potential issues with transparency.

Low risk in network calls, shell execution, and obfuscation.
Metadata risk due to missing maintainer history and GitHub repository.

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet connectivity.
Shell: No shell execution detected, which is expected unless the package is designed to run system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has some red flags including lack of maintainer history and a GitHub repository link, but no clear evidence of malicious intent.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://docs.arthur.ai
Brief PyPI description (614 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

430 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

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

Email domain looks legitimate: arthur.ai>

✓ 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 name is missing or very short
Author "" 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 arthur-client

Create a mini-application called 'Arthur Insights' that leverages the 'arthur-client' Python package to fetch and visualize model performance metrics from Arthur AI's platform. This application will serve as a user-friendly dashboard where users can input their model ID and see various performance metrics over time. Here are the key steps and features for building this application:

1. **Setup**: Install the required packages including 'arthur-client'. Ensure you have access credentials to the Arthur AI platform.
2. **Model Input**: Design a simple UI where users can enter their model ID.
3. **Data Fetching**: Use the 'arthur-client' package to fetch performance metrics associated with the entered model ID from Arthur AI's API. Store these metrics in a structured format.
4. **Data Visualization**: Implement visualization features using a library like Matplotlib or Plotly to display the fetched metrics. Include options for different types of visual representations such as line graphs, bar charts, etc.
5. **Interactive Features**: Add interactive elements to the application such as date range selectors for filtering data based on specific periods.
6. **Alert System**: Integrate an alert system that notifies users via email if certain thresholds are crossed in the model performance metrics.
7. **Documentation**: Write comprehensive documentation explaining how to install the application, use it, and interpret the results.
8. **Testing**: Conduct thorough testing to ensure the application works correctly under various scenarios.

This project aims to provide developers and data scientists with an easy-to-use tool to monitor and understand the performance of their models over time.