🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no immediate signs of malicious activity, but the unavailability of the repository and the maintainer having only one package raise concerns about its legitimacy and could indicate potential supply-chain risks.

Repository not found, raising questions about the maintainer's credibility.
Maintainer has only one package, which is unusual and may suggest a lack of community involvement or trust.

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 no direct system command execution observed.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting no immediate risk related to secret theft.
Metadata: The repository is not found and the maintainer has only one package, which raises suspicion but does not definitively indicate malice.

🔬 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 score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "The AgentForge Authors" 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 agentforge-evidently

Create a data monitoring dashboard application using Python and the 'agentforge-evidently' package. This application will serve as a tool for data scientists and analysts to monitor and detect changes in their datasets over time. Here are the steps and features you need to implement:

1. **Setup**: Install the necessary packages including 'agentforge-evidently'. Ensure your environment is set up for Python development.
2. **Data Ingestion**: Implement a feature that allows users to upload CSV files or connect to databases to ingest data. The application should support multiple file formats and database types.
3. **Drift Detection**: Utilize 'agentforge-evidently' to periodically calculate and display data drift metrics. These metrics should include but not be limited to numerical features, categorical features, and timestamps. Display these metrics in real-time or near real-time on the dashboard.
4. **Visualization**: Create interactive visualizations for the drift metrics. Use libraries like Plotly or Bokeh to make these visualizations dynamic and user-friendly.
5. **Alert System**: Set up an alert system that notifies users via email or SMS when significant changes or drifts are detected in the data.
6. **User Interface**: Develop a clean, intuitive user interface using a web framework such as Flask or Django. The UI should allow users to easily navigate through different sections of the application, configure settings, and view reports.
7. **Documentation**: Provide comprehensive documentation that explains how to use the application, its functionalities, and how to install it on local machines.

This project aims to provide a robust solution for monitoring data quality and detecting drifts in datasets, making it easier for professionals to maintain the integrity of their data.