🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network usage, shell execution, and obfuscation. However, due to its recent creation and the maintainer's limited history with PyPI, there is a moderate suspicion of potential supply-chain attack.

Limited package and maintainer history
New package on PyPI

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, indicating the package does not perform system-level operations.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
Metadata: The package is new with limited history and the maintainer has few PyPI packages, which may indicate potential risk.

📦 Package Quality Overall: Medium (6.4/10)

◈ 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 (2210 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

50 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 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-pages

Create a web-based data analysis dashboard using the Python package 'agi-pages'. This dashboard will allow users to upload datasets, perform basic statistical analyses, and visualize the results. The application should have the following functionalities:

1. User Authentication: Implement user registration and login functionality to ensure that only authenticated users can access and save their analysis sessions.
2. Data Upload: Allow users to upload CSV files containing datasets for analysis.
3. Basic Statistical Analysis: Provide tools for performing common statistical operations such as mean, median, mode, standard deviation, correlation coefficients, etc.
4. Data Visualization: Enable users to generate visual representations of their data, including bar charts, line graphs, scatter plots, and histograms.
5. Session Management: Users should be able to save their analysis sessions and return to them later, allowing them to continue where they left off.
6. Documentation: Include comprehensive documentation explaining how to use the dashboard effectively.

The 'agi-pages' package will be used to create the web pages and handle routing between different sections of the application, such as the login page, dataset upload page, and analysis result page. It will also facilitate the integration of other necessary Python libraries for data processing and visualization, ensuring that the application is both functional and user-friendly.