🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal risks in terms of network calls, shell execution, and obfuscation. However, the metadata risk score suggests low maintenance, raising suspicion about its integrity.

Low metadata maintenance
No direct risks identified but low maintenance is concerning

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 no immediate signs of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows low maintenance and effort signs which may indicate potential risk.

📦 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

6 type-annotated function signatures (partial)

✦ 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-queue-health

Create a web-based monitoring tool using Python and the 'agi-page-queue-health' package. This tool will allow users to monitor the health and resilience of their queue systems in real-time. The application should have the following features:

1. **Queue Status Monitoring**: Display the current status of multiple queues, including their processing speed, latency, and any errors encountered.
2. **Resilience Analysis**: Provide insights into how well the queue system handles unexpected spikes in traffic or failures.
3. **Health Reports**: Generate periodic reports summarizing the performance of each queue over time.
4. **User Management**: Allow administrators to add, remove, and manage different user roles within the application.
5. **Alert System**: Implement an alert system that notifies users via email or SMS when a queue's health falls below a certain threshold.

To achieve these functionalities, utilize the 'agi-page-queue-health' package to gather data about the queue's health and resilience. Integrate this data into your application's backend using Flask or Django, and display it through a React frontend. Ensure the application is user-friendly and provides actionable insights to improve queue management.