Monitors the health and operational status of LlamaFarm machine learning services, models, and data buffers.
The ml-status skill provides a centralized diagnostic tool for managing LlamaFarm ML environments directly within Claude Code. It allows developers to verify Universal Runtime health, inspect trained anomaly and classification models, and monitor real-time streaming detectors and Polars data buffers. By consolidating status reports from multiple ML endpoints into a single, formatted view with actionable troubleshooting tips, it streamlines the debugging and maintenance of data-intensive AI applications.
Características Principales
010 GitHub stars
02Real-time monitoring of active streaming detectors including window statistics
03Inspection of Polars data buffers for row counts and memory usage
04Comprehensive health checks for Universal Runtime, Celery, and database components
05Automated troubleshooting recommendations for service failures and missing dependencies
06Detailed inventory of trained anomaly detection and text classification models
Casos de Uso
01Debugging model loading errors or backend dependency issues in a LlamaFarm environment
02Verifying ML runtime readiness before executing complex training or inference workflows
03Monitoring memory consumption and data volume in active real-time streaming buffers
