AI Analysis
Final verdict: SUSPICIOUS
The package shows no immediate signs of malicious activity, but the incomplete author information and single package maintainer raise some concerns about its legitimacy.
- Incomplete author information
- Single package maintainer
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 attempt to execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author information is incomplete and the maintainer has only one package, which could indicate a less established or potentially suspicious account.
Package Quality Overall: Medium (6.8/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/microsoft/agent-governance-toolkit#readmeDetailed PyPI description (1137 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Classifier: Typing :: Typed84 type-annotated function signatures detected in source
✦ 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
No credential harvesting patterns detected
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_drift
Create a mini-application named 'DriftGuard' that leverages the 'agentmesh_drift' library to monitor and detect changes or 'drift' in the outputs of various machine learning models over time. This application will be particularly useful for data scientists and ML engineers who need to ensure that their models remain accurate and reliable as new data comes in. Here are the key steps and features for building 'DriftGuard': 1. **Setup Environment**: Begin by setting up your development environment with Python and installing the necessary packages including 'agentmesh_drift'. Ensure you have a virtual environment set up for dependency management. 2. **Data Collection Module**: Develop a module within 'DriftGuard' that allows users to input historical and current model output datasets. These datasets could come from CSV files, databases, or even APIs. 3. **Drift Detection Engine**: Utilize the core functionalities of 'agentmesh_drift' to calculate drift scores between the historical and current datasets. Implement functions that can handle different types of data formats and model outputs (e.g., numerical, categorical). 4. **Visualization Dashboard**: Create a simple yet effective visualization dashboard using libraries like Plotly or Matplotlib to display the drift scores over time. Users should be able to see trends and identify periods where significant drift occurred. 5. **Alert System**: Integrate an alert system that notifies users via email or SMS when drift scores exceed a certain threshold. This ensures timely intervention when model performance starts to degrade. 6. **Customization Options**: Allow users to customize the parameters used in the drift detection process, such as the type of drift score calculation, sensitivity thresholds, and alert settings. 7. **Documentation and User Guide**: Provide comprehensive documentation on how to install, configure, and use 'DriftGuard'. Include examples and best practices for integrating it into existing workflows. By following these steps and incorporating these features, 'DriftGuard' will become a valuable tool for maintaining the integrity and reliability of machine learning models in real-world applications.