home / skills / jeremylongshore / claude-code-plugins-plus-skills / tracking-model-versions

This skill helps you track, log, and manage AI/ML model versions using a plugin, ensuring version control and performance visibility.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill tracking-model-versions

Review the files below or copy the command above to add this skill to your agents.

Files (10)
SKILL.md
3.4 KB
---
name: tracking-model-versions
description: |
  Build this skill enables AI assistant 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 f... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version: 1.0.0
author: Jeremy Longshore <[email protected]>
license: MIT
---
# Model Versioning Tracker

This skill provides automated assistance for model versioning tracker tasks.

## Overview


This skill provides automated assistance for model versioning tracker tasks.
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.

## Prerequisites

- Appropriate file access permissions
- Required dependencies installed

## Instructions

1. Invoke this skill when the trigger conditions are met
2. Provide necessary context and parameters
3. Review the generated output
4. Apply modifications as needed

## Output

The skill produces structured output relevant to the task.

## Error Handling

- Invalid input: Prompts for correction
- Missing dependencies: Lists required components
- Permission errors: Suggests remediation steps

## Resources

- Project documentation
- Related skills and commands

Overview

This skill enables the assistant to track and manage AI/ML model versions using the model-versioning-tracker plugin. It provides an automated, code-driven workflow to log new versions, retrieve version history, and surface performance metrics. The skill is designed to enforce version control, lineage tracking, and reproducible model management across development and deployment stages.

How this skill works

When invoked, the assistant analyzes the user's intent and required parameters (model name, version identifier, metadata, performance metrics, etc.). It generates and executes code that calls the model-versioning-tracker plugin to perform operations like create, update, query, and list model versions. The plugin returns structured results which the assistant formats into human-readable summaries, alerts about failures, or next-step recommendations.

When to use it

  • Logging a new model version after training or fine-tuning
  • Querying performance metrics for a specific model version
  • Auditing model lineage and provenance across experiments
  • Automating version control in CI/CD for model deployments
  • Recovering or reverting to a prior model release

Best practices

  • Validate and sanitize metadata (dataset, hyperparameters, commit hash) before logging
  • Include performance snapshots (metrics, evaluation dataset) with each version entry
  • Automate error handling and retries for remote plugin calls
  • Tag versions with environment and deployment context (staging, prod)
  • Periodically audit the registry to remove or archive deprecated versions

Example use cases

  • Track a new image-classification model version with training metrics and dataset checksum
  • Retrieve latency and accuracy metrics for version 3 of a sentiment-analysis model
  • Create an automated pipeline step that logs model versions after each successful training job
  • List all versions associated with a particular experiment or Git commit
  • Compare performance across multiple model versions to decide rollback or promote to production

FAQ

What information should I include when logging a model version?

Include model name, semantic version or identifier, training dataset reference, hyperparameters, code commit hash, evaluation metrics, and any deployment notes.

How does the skill handle failures when contacting the plugin?

It surfaces error details, suggests immediate remediation (permission fixes, dependency checks), and can retry the operation if configured to do so.