🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risks across network, shell, and obfuscation fronts, but the metadata suggests it might come from an inexperienced or inactive developer, raising concerns about its reliability and potential maintenance.

Low metadata quality indicative of an inexperienced or inactive author.
No significant technical risks identified.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
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 from an inexperienced or inactive author, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Low (3.8/10)

○ 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

○ 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 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-simplex-map

Create a Python-based interactive visualization tool that leverages the 'agi-page-simplex-map' package to perform simplex projection and barycentric analysis on multi-dimensional data sets. This tool will enable users to upload their own datasets and visualize them using simplex maps, which are particularly useful for understanding complex relationships within data points in a simplified geometric space.

Step 1: Set up the project environment by installing necessary packages including 'agi-page-simplex-map'.
Step 2: Design a user-friendly interface where users can input or upload their datasets. Ensure that the dataset format is flexible enough to accept CSV or Excel files.
Step 3: Implement functionality within the application to process these inputs into a format suitable for simplex projection and barycentric analysis using 'agi-page-simplex-map'.
Step 4: Develop algorithms to automatically generate simplex projections based on the uploaded data. These projections should be customizable, allowing users to adjust parameters such as the number of dimensions displayed.
Step 5: Integrate interactive features into the visualization, enabling users to manipulate the simplex map (e.g., zoom, pan, highlight specific regions).
Step 6: Provide visual feedback to users about the significance of different areas within the simplex map through color coding or other graphical indicators.
Step 7: Offer export options for the generated simplex maps, allowing users to save their visualizations in various formats (PNG, PDF).
Step 8: Include documentation and tutorials within the application to guide users through the process of uploading data and interpreting the results.

Suggested Features:
- Support for real-time data updates and dynamic re-projection.
- Integration with machine learning models for predictive analysis based on simplex maps.
- Option to compare multiple datasets simultaneously on the same simplex map.
- Detailed tooltips providing additional information about each data point when hovered over in the simplex map.
- Compatibility with various Python visualization libraries to enhance the presentation quality of the simplex maps.

This project aims to democratize access to advanced data analysis techniques like simplex projection and barycentric analysis by making them accessible via an intuitive, user-friendly tool.