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maminul007
maminul007COMMUNITY
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model-registry

Manage AI model versions across staging and production environments.

Updated Aug 23, 2026
Search Tags:#version control#deployment#mlops#ai#machine learning#model management
One-click install
npx skills add https://github.com/maminul007/trading-platform --skill model-registry
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-registry
Source: https://github.com/maminul007/trading-platform/tree/main/.claude/skills/model-registry
Command: npx skills add https://github.com/maminul007/trading-platform --skill model-registry

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the management of AI model versions, enabling efficient promotion through different deployment stages and facilitating rollbacks when necessary.

Core Features & Use Cases

  • Model Listing: View all registered models and their current deployment stages.
  • Version Management: List specific versions of a model and compare different versions.
  • Stage Promotion: Promote models to staging or production environments after validation.
  • Rollback: Revert to a previous stable model version in case of issues.
  • Archiving: Archive older model versions.
  • Export: Export models in formats like ONNX for inference optimization.
  • Use Case: A data science team can use this skill to safely deploy a new version of a trading strategy model to production after it has passed staging tests, or quickly roll back to a previous version if the new one underperforms.

Quick Start

Use the model-registry skill to promote the tsmom_v2 model to the staging environment.

Frequently Asked Questions about model-registry

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I promote machine learning models across staging and production environments?▼

To promote machine learning models across staging and production, you can use automated checks against promotion criteria to validate and trigger deployments. This skill manages the model lifecycle to safely move versions between environments.

What is the process to roll back a production AI model to a previous version?▼

Rolling back a production AI model to a previous version reverts the active deployment to the last stable state. This is triggered when a newly promoted model underperforms or causes issues in the production environment.

Can I export AI models to ONNX format for inference optimization?▼

Yes, you can export AI models to ONNX format for inference optimization. This functionality is supported through Python scripts that handle model export operations within the registry.

Do I need Python scripts to manage AI model versioning and deployment stages?▼

Yes, Python scripts are required to perform registry operations and model export functionalities. These scripts enable listing, comparing, and managing model versions across development, staging, and production.

How do I list and compare different versions of a registered AI model?▼

To list and compare different versions of a registered AI model, you use registry scripts to view all registered models and their current deployment stages. This allows detailed version management and tracking.

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