🤖 AI Analysis

Final verdict: SAFE

The package appears safe with minimal risks across all categories. The metadata risk is slightly elevated due to signs of low effort and new maintainer activity, but it does not indicate a supply-chain attack.

No network calls detected
No shell execution patterns
Low effort and potential new maintainer activity noted in metadata

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 command injection or backdoor activities.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some signs of low effort and potential new maintainer activity, but there are no clear red flags.

📦 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

62 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-inference-report

Your task is to develop a mini-application that leverages the 'agi-page-inference-report' package to streamline the process of generating and managing reports based on inference results from machine learning models. This application will serve as a tool for data scientists and researchers to easily visualize and share their model performance insights.

### Project Overview:
- **Name:** Inference Report Manager
- **Goal:** To create a user-friendly interface that allows users to input inference results from various machine learning models and generate comprehensive reports with visualizations.
- **Target Audience:** Data Scientists, Researchers, Machine Learning Engineers

### Core Features:
1. **Inference Result Input:** Users should be able to upload inference results in CSV format. These results include predicted values, actual values, and any other relevant metrics such as confidence scores.
2. **Report Generation:** Based on the uploaded data, the application should automatically generate a report that includes statistical summaries, error analysis, and visual representations like confusion matrices and ROC curves.
3. **Customization Options:** Allow users to customize the report content and style. They should be able to choose which metrics to display and adjust the layout of the report.
4. **Export Functionality:** Provide options to export the generated report in different formats (PDF, HTML, etc.).
5. **Collaboration Tools:** Implement basic collaboration features allowing multiple users to view and comment on reports.

### Utilizing 'agi-page-inference-report':
- Use the 'agi-page-inference-report' package to handle the backend logic for report generation. Specifically, utilize its functions for parsing inference results, generating statistical summaries, and creating visualizations.
- Ensure that the package's capabilities are integrated seamlessly into the application's workflow, enhancing the efficiency and accuracy of report creation.

### Additional Considerations:
- Ensure the application is responsive and works well on both desktop and mobile devices.
- Include a tutorial or guide within the application to help new users understand how to use it effectively.
- Design the UI/UX to be intuitive and visually appealing, making the application easy and enjoyable to use.

### Deliverables:
- A fully functional web application deployed on a cloud platform (e.g., Heroku, AWS).
- Documentation detailing how to set up and run the application locally.
- A README file explaining the project structure, dependencies, and how to contribute.

This project aims to showcase the power of 'agi-page-inference-report' while providing a valuable tool for professionals working with machine learning.