From jeremylongshore-claude-code-plugins-plus-skills
Assists with model drift detection in ML deployments by providing step-by-step guidance, best practices, production-ready code, and configurations for MLOps monitoring.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin langchain-py-packThis skill is limited to using the following tools:
This skill provides automated assistance for model drift detector tasks within the ML Deployment domain.
Implements data and concept drift monitoring for production ML models using Evidently AI, PSI/KS tests, with alerting workflows. Use for performance degradation, data shifts, or regulatory needs.
Provides guidance for monitoring DataRobot models: tracks performance metrics, detects data/feature/target drift, and identifies prediction anomalies using Python SDK. For production ML health checks.
Guides prediction monitoring in ML deployments with step-by-step instructions, best practices, code generation, and configs for MLOps pipelines, model serving, inference, and production optimization.
Share bugs, ideas, or general feedback.
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