model-registry-manager

Name: model-registry-manager - Agent Skill
Rating: 5.0 (1046 reviews)
Author: jeremylongshore

1,046

MIT

Manage model registry manager operations. Auto-activating skill for ML Deployment. Triggers on: model registry manager, model registry manager Part of the ML Deployment skill category. Use when working with model registry manager functionality. Trigger with phrases like "model registry manager", "model manager", "model".

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CLI Install

Recommended

Use the skills CLI to install this skill with one command. Auto-detects all installed AI assistants.

Method 1 - skills CLI

npx skills i jeremylongshore/claude-code-plugins-plus-skills/skills/08-ml-deployment/model-registry-manager

Method 2 - openskills (supports sync & update)

npx openskills install jeremylongshore/claude-code-plugins-plus-skills

Auto-detects Claude Code, Cursor, Codex CLI, Gemini CLI, and more. One install, works everywhere.

Installation Path

Download and extract to one of the following locations:

~/.claude/skills/model-registry-manager/

SKILL.md

Skill Instructions

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Claude Sonnet 4.5

$0.20/task

Best for coding tasks

No setup required

Model Registry Manager

Overview

This skill provides automated assistance for model registry manager tasks within the ML Deployment domain.

When to Use

This skill activates automatically when you:

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

Instructions

Provides step-by-step guidance for model registry manager
Follows industry best practices and patterns
Generates production-ready code and configurations
Validates outputs against common standards

Examples

Example: Basic Usage Request: "Help me with model registry manager" 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

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