🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all assessed categories, with no indications of malicious activities such as network calls, shell executions, or credential harvesting. However, the metadata risk slightly increases due to the maintainer's limited presence on PyPI.

No network calls
No shell execution
No obfuscation
No credential harvesting
Single package from author

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external communication for its functionality.
Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The author has only one package on PyPI, which may indicate a new or less active maintainer, raising some suspicion.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

Test runner config found: pyproject.toml
1 test file(s) detected (e.g. test_audit_entry_export.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/microsoft/agent-governance-toolkit/issues
Detailed PyPI description (3315 chars)

○ 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

14 unique contributor(s) across 100 commits in microsoft/agent-governance-toolkit
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 microsoft/agent-governance-toolkit appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Microsoft Corporation" 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 agentmesh_audit_export

Your task is to develop a mini-application named 'AuditLogAnalyzer' using the Python package 'agentmesh_audit_export'. This application will serve as a powerful tool for transforming and exporting audit logs into a structured format suitable for external accountability systems. Here's a detailed breakdown of what your application should achieve:

1. **Initialization**: Start by setting up a Python virtual environment and installing the necessary packages, including 'agentmesh_audit_export'. Ensure you have a clean and isolated environment for development.
2. **Configuration**: Create a configuration file where users can define their preferred export formats and destinations (e.g., CSV, JSON files, or databases). Allow flexibility in specifying these parameters.
3. **Data Ingestion**: Develop a feature that allows the application to ingest audit log data directly from AgentMesh's AuditEntry output. Ensure this process is efficient and scalable.
4. **Transformation**: Utilize the 'agentmesh_audit_export' package to transform the ingested data into a structured format that aligns with the chosen export destination. This transformation should be customizable based on user-defined rules and preferences.
5. **Export Functionality**: Implement functionality to export the transformed data into the specified format and destination. For example, if the user chooses CSV as the format, ensure the application can write the data into a CSV file at a given path.
6. **User Interface**: Design a simple command-line interface (CLI) that guides users through the setup and execution process. Provide clear instructions and error messages.
7. **Testing & Documentation**: Write comprehensive tests to validate each component of your application. Ensure the documentation is thorough, covering installation, configuration, usage examples, and troubleshooting tips.
8. **Enhancements**: Consider adding advanced features such as real-time data processing, support for multiple concurrent export tasks, and integration with popular cloud storage solutions.

By completing this project, you'll gain valuable experience in handling complex data transformations, working with third-party packages, and building robust CLI applications.