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name: "model-drift-detector" description: | Detect model drift detector operations. Auto-activating skill for ML Deployment. Triggers on: model drift detector, model drift detector Part of the ML Deployment skill category. Use when working with model drift detector functionality. Trigger with phrases like "model drift detector", "model detector", "model". allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep" version: 1.0.0 license: MIT author: "Jeremy Longshore jeremy@intentsolutions.io"

Model Drift Detector

Overview

This skill provides automated assistance for model drift detector tasks within the ML Deployment domain.

When to Use

This skill activates automatically when you:

• Mention "model drift detector" in your request
• Ask about model drift detector patterns or best practices
• Need help with machine learning deployment skills covering model serving, mlops pipelines, monitoring, and production optimization.

Instructions

1. Provides step-by-step guidance for model drift detector
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

Examples

Example: Basic Usage Request: "Help me with model drift detector" Result: Provides step-by-step guidance and generates appropriate configurations

Prerequisites

• Relevant development environment configured
• Access to necessary tools and services
• Basic understanding of ml deployment concepts

Output

• Generated configurations and code
• Best practice recommendations
• Validation results

Error Handling

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

Resources

• Official documentation for related tools
• Best practices guides
• Community examples and tutorials

Related Skills

Part of the ML Deployment skill category. Tags: mlops, serving, inference, monitoring, production