Can I use this for MLOps workflows?

Yes, it is specifically designed to facilitate MLOps best practices like automation of model workflows and performance optimization.

What plugin does this skill require?

This skill requires the model-versioning-tracker plugin to be active within your Claude Code environment.

How do I trigger this skill in Claude Code?

You can trigger it by asking Claude to 'track a version', 'log performance', or manage a 'model registry'.

What does the AI Model Versioning Tracker skill do?

It allows Claude to interact with a versioning plugin to track AI/ML model versions, log performance data, and manage model lineage.

Does it support metrics retrieval?

Yes, Claude can generate code to query the plugin for specific performance metrics associated with any registered model version.

AI Model Versioning Tracker

Name: AI Model Versioning Tracker
Author: BbgnsurfTech

byBbgnsurfTech

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数据科学与机器学习

Manages and tracks AI/ML model versions, performance metrics, and lineage directly within the Claude Code environment.

关于

The AI Model Versioning Tracker skill empowers Claude to interface with the model-versioning-tracker plugin, streamlining the management of machine learning lifecycles. It enables developers to automate the registration of new model versions, document metadata, and monitor performance metrics through a centralized registry. By implementing MLOps best practices such as version control and lineage tracking, this skill ensures that AI development remains organized, observable, and reproducible during both training and deployment phases.

主要功能

Comprehensive model lineage and history tracking
Validation of input data and robust error handling for model logs
Automated registration of new AI/ML model versions
Real-time retrieval and logging of model performance metrics
1 GitHub stars
Seamless integration with model registries and MLflow workflows

使用场景

Comparing performance metrics between different versions of a production model
Establishing a verifiable audit trail for model deployment and lineage
Registering a new iteration of a training model with custom metadata

关于

主要功能

Comprehensive model lineage and history tracking
Validation of input data and robust error handling for model logs
Automated registration of new AI/ML model versions
Real-time retrieval and logging of model performance metrics
1 GitHub stars
Seamless integration with model registries and MLflow workflows

使用场景

Comparing performance metrics between different versions of a production model
Establishing a verifiable audit trail for model deployment and lineage
Registering a new iteration of a training model with custom metadata