🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk in terms of network, shell, obfuscation, and credential risks. However, the metadata risk score is elevated due to the package's recent creation and lack of maintainer information, raising suspicion about its legitimacy.

Low metadata quality
New package with unknown maintainer

Per-check LLM notes

Network: No network calls suggest the package is not attempting to communicate externally, which is normal for most packages.
Shell: No shell executions indicate that the package does not execute external commands, reducing the risk of unauthorized system access.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low effort and could be suspicious due to its newness and lack of maintainer information.

📦 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

26 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-training-report

Create a fully functional mini-application that leverages the 'agi-page-training-report' package to generate comprehensive reports for machine learning training runs. This application will serve as a tool for data scientists and machine learning engineers to monitor and analyze the performance of their models over time. Here’s a step-by-step guide on how to develop this application:

1. **Setup Project Environment**: Begin by setting up a new Python virtual environment and installing necessary packages including 'agi-page-training-report'. Ensure that you have a clean and isolated development environment.
2. **Design User Interface**: Design a simple yet effective user interface using a web framework like Flask or Django. The UI should allow users to input details about their training runs such as model name, dataset used, hyperparameters, etc.
3. **Integrate 'agi-page-training-report' Package**: Utilize the 'agi-page-training-report' package to process and format the data collected from the user. The package should be able to generate structured reports based on the inputs provided by the user.
4. **Generate Training Reports**: Implement functionality within your application that generates detailed training reports. These reports should include visualizations such as loss curves, accuracy graphs, and other relevant metrics. Use the capabilities of 'agi-page-training-report' to enhance these visualizations and make them more informative.
5. **Database Integration**: Integrate a database system (such as SQLite or PostgreSQL) to store information about past training runs. This allows users to retrieve and compare historical data easily.
6. **User Authentication**: Add basic user authentication to the application to ensure that only authorized users can access and modify their training run data.
7. **Deployment**: Prepare the application for deployment on a cloud service provider such as AWS, Heroku, or DigitalOcean. Ensure that all dependencies are correctly managed and that the application runs smoothly in a production environment.

Suggested Features:
- Support for multiple types of machine learning models (e.g., classification, regression).
- Ability to upload and attach files (like Jupyter notebooks) related to each training run.
- Detailed logging and error handling to assist with debugging and maintenance.
- Optional real-time updates during training run monitoring.

This project aims to streamline the process of tracking and analyzing machine learning training runs, making it easier for professionals to manage and improve their models.