AI Analysis
The package shows no signs of direct malicious activity such as network calls or shell executions. However, due to the maintainer's limited history and the recent creation of the package, there is a moderate level of suspicion.
- Limited maintainer history
- Recently created package
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: The package is new and the maintainer has limited history with PyPI, raising some suspicion but not conclusive evidence of malice.
Package Quality Overall: Medium (5.6/10)
Partial test coverage signals detected
Test runner config found: pyproject.toml
Some documentation present
Documentation URL: "Documentation" -> https://thalesgroup.github.io/agilabDetailed PyPI description (2482 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
No type annotations detected
No type annotations, py.typed marker, or stub files detected
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
3 maintainer concern(s) found
Only one version has ever been released — brand new packagePackage is very new: uploaded 3 day(s) agoAuthor "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 decision support system for a fictional space exploration agency called 'AGILAB'. This system will help AGILAB prioritize its missions based on various factors such as scientific value, resource availability, public interest, and potential risks. The project should utilize the 'agi-app-mission-decision' package to manage the deterministic workflow of mission decisions, including scenario scoring and evidence artifact handling. Here are the steps and features you need to include in your project: 1. **Mission Data Entry**: Allow users to input details about each mission, including mission goals, estimated cost, required resources, and potential benefits. 2. **Scenario Scoring**: Implement a scoring mechanism to evaluate each mission based on predefined criteria such as scientific significance, economic impact, and public engagement. 3. **Evidence Artifact Management**: Utilize the 'agi-app-mission-decision' package to handle evidence artifacts related to each mission, such as research papers, feasibility studies, and expert opinions. 4. **Decision Workflow**: Use the deterministic workflow provided by the package to guide the decision-making process from initial proposal to final approval. 5. **Visualization Tools**: Develop tools to visualize the scoring results and decision paths, helping stakeholders understand the rationale behind mission prioritization. 6. **User Interface**: Design a user-friendly interface for administrators and decision-makers to interact with the system. 7. **Documentation and Testing**: Ensure thorough documentation and testing of all components to maintain reliability and ease of use. This project aims to showcase the capabilities of the 'agi-app-mission-decision' package while providing a practical solution for managing complex decision processes in a space exploration context.