coreml

Route and execute Core ML workflows across multiple models.

57|18|Updated Jul 9, 2025

Search Tags:#routing #workflow optimization #model management #coreml #neural engine

Ready to run

npx skills add https://github.com/mattmireles/kokoro-coreml --skill coreml-mattmireles

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: coreml
Source: https://github.com/mattmireles/kokoro-coreml/tree/main/.claude/skills/coreml
Command: npx skills add https://github.com/mattmireles/kokoro-coreml --skill coreml-mattmireles

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines and optimizes Core ML workflows, ensuring efficient routing and execution of tasks across various models and procedures.

Core Features & Use Cases

Core ML Routing: Directs and delegates tasks to specific Core ML child skills based on context.
Model Optimization: Facilitates efficient use of Apple Neural Engine for Core ML models.
Use Case: If you have a collection of Core ML models and need a unified way to validate, profile, and debug them, this skill can manage the entire workflow, optimizing performance and ensuring accuracy.

Quick Start

To begin, navigate to your Core ML workspace and run the command 'coreml route my-model' to start the workflow.

Frequently Asked Questions about coreml

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

FAQPage Schema

How do I optimize Core ML models for the Apple Neural Engine?▼

To optimize Core ML models for the Apple Neural Engine, this skill facilitates efficient workflow routing and delegates procedures to manage execution and maximize hardware performance across multiple models.

What is the best way to manage routing and execution for multiple Core ML workflows?▼

Managing Core ML workflows is handled by directing and delegating tasks to specific child skills based on context, providing a unified system to validate, profile, and debug your entire model collection.

How do I validate and profile Core ML models in a unified workflow?▼

You can validate and profile Core ML models by running the workflow routing command, which manages execution across your models and delegates tasks like debugging to ensure accuracy and performance.

Do I need a specific execution environment to route Core ML workflows?▼

Yes, routing Core ML workflows requires access to your Core ML models and an appropriate execution environment to manage tasks like validation, profiling, and Neural Engine optimization successfully.

Can I debug multiple Core ML models simultaneously through workflow routing?▼

Debugging multiple Core ML models is supported through the workflow routing system, which handles task execution and delegates debugging procedures to child skills for comprehensive model management.

Why use a dedicated routing skill for Core ML workflow optimization?▼

A dedicated routing skill streamlines Core ML workflows by ensuring efficient task execution and validation across various models, maximizing Apple Neural Engine performance without manual intervention.