🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks across all technical indicators such as network calls, shell execution, and obfuscation. However, the metadata risk score is elevated due to the novelty of the package and the limited history of its maintainer.

Metadata risk due to new package and limited maintainer history
No significant technical risks detected

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new and maintained by an author with limited history, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.6/10)

◈ 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://thalesgroup.github.io/agilab
Detailed PyPI description (5934 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

✦ 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 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
Package is very new: uploaded 3 day(s) ago
Author "Jean-Pierre Morard" 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 agi-app-tescia-diagnostic

Your task is to create a diagnostic tool application using Python, which leverages the 'agi-app-tescia-diagnostic' package to perform comprehensive health checks on various components of a system. This application will be particularly useful for IT professionals and system administrators who need to ensure their systems are running optimally. Here’s a detailed plan for your project:

1. **Project Setup**: Begin by setting up a new Python virtual environment. Install the 'agi-app-tescia-diagnostic' package from its source or any available repository.
2. **Core Functionality**: Implement a main function that initiates a diagnostic process. This process should include steps to gather information about the system's current state, analyze this data, and generate a report based on the findings.
3. **Evidence Scoring**: Utilize the 'agi-app-tescia-diagnostic' package to assign scores to different pieces of evidence collected during the diagnostic process. These scores will help in determining the severity of any issues found.
4. **Regression Plan Generation**: For each issue identified, the application should automatically generate a regression plan. This plan will outline steps to mitigate or resolve the identified problems.
5. **User Interface**: Develop a simple command-line interface (CLI) for the application. This interface should allow users to initiate diagnostics, view results, and access generated regression plans.
6. **Documentation**: Write clear documentation that explains how to install the application, use its CLI, and interpret the diagnostic reports and regression plans.
7. **Testing and Validation**: Conduct thorough testing to ensure the application works as expected across different scenarios. Validate the accuracy of the evidence scoring and the effectiveness of the generated regression plans.
8. **Deployment**: Prepare the application for deployment. Ensure it can be easily installed and run on different systems.

Suggested Features:
- Customizable diagnostic profiles allowing users to select specific components for diagnosis.
- Integration with logging frameworks to record diagnostic activities.
- Support for exporting diagnostic reports and regression plans in multiple formats (e.g., CSV, JSON).
- An option to schedule regular diagnostics automatically.

The 'agi-app-tescia-diagnostic' package plays a crucial role in this project by providing the framework and tools necessary for evidence scoring and generating actionable regression plans. Your goal is to create a robust, user-friendly tool that significantly simplifies the process of diagnosing and resolving system issues.