AI Analysis
Final verdict: SUSPICIOUS
The package shows low risks in network and shell interactions but has incomplete metadata, suggesting potential unreliability or suspicion regarding the maintainer's experience.
- Incomplete author information
- Single package maintained by the author
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
- Shell: No shell execution patterns detected, indicating no immediate risk of command execution from the package.
- Metadata: The author information is incomplete and the maintainer has only one package, which may indicate a less experienced or potentially suspicious actor.
Package Quality Overall: Medium (5.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (1699 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
Classifier: Typing :: Typed
✦ High
Multiple Contributors
10.0
Active multi-contributor project
14 unique contributor(s) across 100 commits in microsoft/agent-governance-toolkitActive 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
score 2.5
Found 1 credential access pattern(s)
: "delete_file", "resource": "/etc/passwd"} } } ) [build-system] requir
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: microsoft.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository microsoft/agent-governance-toolkit appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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_primitives
Create a mini-application named 'AgentWatch' that monitors and reports on system failures using the 'agentmesh_primitives' package. This application will serve as a simple yet powerful tool for understanding how different failure types and their severities are managed within a system. Here’s a detailed breakdown of what your application should include and accomplish: 1. **Setup and Configuration**: Start by setting up a basic Python environment and installing the 'agentmesh_primitives' package. Ensure you have a clean, virtual environment for development. 2. **Data Models Initialization**: Utilize the 'agentmesh_primitives' package to define and initialize data models representing failure types and severity levels. These models should encapsulate the essence of each failure type and its corresponding severity, allowing for a structured representation of system issues. 3. **Failure Reporting System**: Develop a feature within 'AgentWatch' that simulates various system failures. Each simulated failure should be categorized according to the predefined failure types and severity levels from the 'agentmesh_primitives'. This system should be capable of logging these failures into a structured format, such as JSON files or a database, for future analysis. 4. **Severity-Based Alerts**: Implement an alerting mechanism that triggers based on the severity level of the reported failures. For instance, critical failures might require immediate attention, whereas minor issues could be logged without real-time notification. 5. **User Interface**: Create a simple user interface (UI) using a framework like Tkinter or Streamlit, which allows users to view recent failures, their types, severities, and timestamps. The UI should also provide options to filter failures by type or severity, enhancing usability. 6. **Analysis Tools**: Integrate basic analysis tools into 'AgentWatch', such as graphs and charts, to visualize trends over time. This could help in identifying patterns or common causes of failures, aiding in proactive system management. 7. **Testing and Documentation**: Finally, ensure thorough testing of all functionalities and document the setup process, usage instructions, and any limitations of the application. Your documentation should guide others through setting up 'AgentWatch' and understanding how it leverages 'agentmesh_primitives' for managing system failures effectively. By completing this project, you'll gain valuable experience in handling complex data models, implementing structured error reporting, and developing practical applications that enhance system reliability and maintainability.