🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal direct security risks but raises concerns due to low maintainer effort and potential new creation, suggesting it might warrant further investigation.

Low maintainer effort
Potential new package creation

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or unauthorized system access.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low maintainer effort and may be newly created, raising suspicion but not conclusive evidence of malice.

📦 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

8 type-annotated function signatures (partial)

✦ 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-decision-evidence

Create a mini-application called 'Decision Review Tool' using the Python package 'agi-page-decision-evidence'. This tool will help users analyze and review decision-making processes by providing visual and analytical support. Here’s a detailed step-by-step guide on how to build it:

1. **Setup**: Begin by setting up your Python environment. Ensure you have 'agi-page-decision-evidence' installed. Use pip to install it if necessary.
2. **User Interface**: Develop a simple yet intuitive user interface where users can input data related to their decisions. This includes factors like criteria weights, alternatives, and outcomes.
3. **Data Processing**: Utilize 'agi-page-decision-evidence' to process the input data. Implement functions to calculate decision scores based on the input criteria and weights. Ensure the package's core functionalities are leveraged effectively for this purpose.
4. **Visualization**: Integrate visualization tools within your application to display the decision-making process visually. This could include charts showing the impact of different criteria on the final decision.
5. **Review & Feedback**: Allow users to review the decision-making process through the application. Provide options for them to adjust inputs and see how changes affect the outcome.
6. **Report Generation**: Finally, enable the generation of comprehensive reports summarizing the decision-making process. These reports should include key insights, visualizations, and recommendations based on the analysis.

Suggested Features:
- Interactive sliders for adjusting criteria weights.
- Real-time updates on decision scores as inputs change.
- Comparison charts between different decision scenarios.
- Detailed breakdowns of how each alternative performed against the criteria.
- Exportable PDF reports summarizing the entire decision-making process.

Ensure the application is user-friendly and provides clear, actionable insights into the decision-making process.