🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network calls, shell execution, and obfuscation. However, the metadata quality and maintenance level are concerning, suggesting potential issues that could indicate a supply-chain attack.

Low maintenance and poor metadata quality
Potential signs of a supply-chain attack

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require internet access.
Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
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 low maintenance and metadata quality indicators, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

No test files or test-runner configuration detected

◈ Medium Documentation 5.0

Some documentation present

Documentation URL: "Documentation" -> https://thalesgroup.github.io/agilab

○ 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

20 type-annotated function signatures detected in source

✦ High Multiple Contributors 10.0

Active multi-contributor project

5 unique contributor(s) across 69 commits in ThalesGroup/agilab
Active community — 5 or more distinct contributors

🔬 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

Repository ThalesGroup/agilab appears legitimate

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
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 agi-page-live-artifacts

Develop a real-time monitoring dashboard using the Python package 'agi-page-live-artifacts'. This dashboard will serve as a tool for tracking live artifacts and evidence in a laboratory setting, providing researchers with up-to-date information on various experiments and their outcomes. The application should allow users to visualize data from multiple sources in real-time, including temperature readings, chemical concentrations, and other relevant metrics. Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Install necessary Python packages including 'agi-page-live-artifacts' and any dependencies required for data visualization.
2. **Data Collection**: Integrate the application with existing lab equipment or sensors to collect real-time data. Ensure that data can be streamed into the dashboard seamlessly.
3. **Dashboard Creation**: Use 'agi-page-live-artifacts' to create a customizable dashboard where different types of data can be displayed. Each type of data should have its own widget or chart for easy visualization.
4. **Real-Time Updates**: Implement functionality that updates the dashboard in real-time as new data comes in. This ensures that researchers always have access to the most current information.
5. **Alert System**: Add an alert system that notifies researchers when certain thresholds are reached (e.g., if temperature exceeds a safe limit). Alerts should be configurable based on user preferences.
6. **User Interface**: Design an intuitive UI that allows researchers to navigate through different sections of the dashboard easily. Consider adding features like zooming, filtering, and exporting data for further analysis.
7. **Testing & Deployment**: Test the application thoroughly under various conditions to ensure reliability and accuracy. Deploy the application to a server or cloud service for wider accessibility.

Suggested Features:
- Customizable widgets for different types of data visualization.
- Real-time data streaming from connected devices.
- Configurable alerts for critical data points.
- User-friendly interface with options for customization.
- Export functionality for data analysis outside the dashboard.

Utilizing 'agi-page-live-artifacts', you can focus on building the frontend and backend integration for real-time data display while leveraging the package's capabilities for handling live artifacts and evidence monitoring efficiently.