AI Analysis
The package shows minimal risk indicators with no network calls, shell executions, or credential mishandling. The primary concern is the inactivity and newness of the maintainer, but this alone does not conclusively indicate malicious activity.
- No network calls detected.
- Maintainer is new and inactive.
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 direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
- Metadata: The maintainer seems new and inactive, but no other suspicious flags were raised.
Package Quality Overall: Medium (6.4/10)
Partial test coverage signals detected
Test runner config found: pyproject.toml
Some documentation present
Documentation URL: "Documentation" -> https://thalesgroup.github.io/agilabDetailed PyPI description (2468 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
Partial type annotation coverage
3 type-annotated function signatures (partial)
Active multi-contributor project
5 unique contributor(s) across 69 commits in ThalesGroup/agilabActive community — 5 or more distinct contributors
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
Repository ThalesGroup/agilab appears legitimate
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Jean-Pierre Morard" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a mini-application called 'AGI Artifact Explorer' that visualizes the workflow of artifact handoffs across different demo projects using the 'agi-app-global-dag' package. This application will serve as a tool for developers and project managers to better understand the flow of artifacts (such as data, models, and reports) between various stages of their projects. The application should have the following functionalities: 1. **Project Setup**: Allow users to set up multiple demo projects within the application, each representing a different phase or team involved in the project lifecycle. 2. **Artifact Definition**: Users should be able to define artifacts that move between these projects. Each artifact should have a name, type, and a brief description. 3. **DAG Visualization**: Utilize the 'agi-app-global-dag' package to create a directed acyclic graph (DAG) that visually represents how artifacts move from one project to another. The DAG should clearly show the direction of artifact flow and any dependencies between artifacts. 4. **Interactive Exploration**: Implement an interactive feature where users can click on nodes in the DAG to get more information about the specific artifact or project. Additionally, allow users to simulate the movement of artifacts through the DAG. 5. **Customization Options**: Provide options for users to customize the appearance of the DAG, such as changing colors or adding labels, to make it easier to differentiate between different types of artifacts or projects. 6. **Export Functionality**: Include a feature that allows users to export the DAG visualization as an image file or a shareable link, making it easy to share insights with stakeholders. 7. **User Interface**: Design a user-friendly interface that simplifies the process of setting up projects, defining artifacts, and interacting with the DAG. Consider incorporating tooltips and help sections to guide new users. To utilize the 'agi-app-global-dag' package effectively, you will need to integrate its core functions for creating and manipulating DAGs. Focus on leveraging the package's capabilities to enhance the visual representation of the workflow, ensuring that the DAG accurately reflects the complexity and interdependencies of the project's artifact handoffs.