From jeremylongshore-claude-code-plugins-plus-skills
Guides model drift detection for ML deployment, covering MLOps pipelines, monitoring, and production optimization.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:model-drift-detectorThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides automated assistance for model drift detector tasks within the ML Deployment domain.
This skill provides automated assistance for model drift detector tasks within the ML Deployment domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with model drift detector" Result: Provides step-by-step guidance and generates appropriate configurations
| Error | Cause | Solution |
|---|---|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
Part of the ML Deployment skill category. Tags: mlops, serving, inference, monitoring, production
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin j-rigMonitors deployed model performance, detects data drift, and manages model health using the DataRobot Python SDK. Use for tracking prediction accuracy, feature drift, and prediction anomalies.
Detects data drift and concept drift in production ML models using Evidently AI, PSI, KS tests, and custom metrics. Sets up automated alerts and reports to catch model degradation before it impacts business metrics.
Guides prediction monitoring tasks for ML deployments, including model serving, MLOps pipelines, and production optimization.