---
name: Tracking Model Versions
description: |
  This skill enables Claude to track and manage AI/ML model versions using the model-versioning-tracker plugin. It should be used when the user asks to manage model versions, track model lineage, log model performance, or implement version control for AI/ML models. Use this skill when the user mentions "track versions", "model registry", "MLflow", or requests assistance with AI/ML model deployment and management. This skill facilitates the implementation of best practices for model versioning, automation of model workflows, and performance optimization.
---

## Overview

This skill empowers Claude to interact with the model-versioning-tracker plugin, providing a streamlined approach to managing and tracking AI/ML model versions. It ensures that model development and deployment are conducted with proper version control, logging, and performance monitoring.

## How It Works

1. **Analyze Request**: Claude analyzes the user's request to determine the specific model versioning task.
2. **Generate Code**: Claude generates the necessary code to interact with the model-versioning-tracker plugin.
3. **Execute Task**: The plugin executes the code, performing the requested model versioning operation, such as tracking a new version or retrieving performance metrics.

## When to Use This Skill

This skill activates when you need to:
- Track new versions of AI/ML models.
- Retrieve performance metrics for specific model versions.
- Implement automated workflows for model versioning.

## Examples

### Example 1: Tracking a New Model Version

User request: "Track a new version of my image classification model."

The skill will:
1. Generate code to log the new model version and its associated metadata using the model-versioning-tracker plugin.
2. Execute the code, creating a new entry in the model registry.

### Example 2: Retrieving Performance Metrics

User request: "Get the performance metrics for version 3 of my sentiment analysis model."

The skill will:
1. Generate code to query the model-versioning-tracker plugin for the performance metrics associated with the specified model version.
2. Execute the code and return the metrics to the user.

## Best Practices

- **Data Validation**: Ensure input data is validated before logging model versions.
- **Error Handling**: Implement robust error handling to manage unexpected issues during version tracking.
- **Performance Monitoring**: Continuously monitor model performance to identify opportunities for optimization.

## Integration

This skill integrates with other Claude Code plugins by providing a centralized location for managing AI/ML model versions. It can be used in conjunction with plugins that handle data processing, model training, and deployment to ensure a seamless AI/ML workflow.