skills.rest
femitz
femitzCOMMUNITY
Explore all skills by this author →
GitHub

coreml

Load and run Core ML models on-device with Swift APIs.

Updated Apr 19, 2026
Search Tags:#swift#vision#on-device#machine-learning#coreml#mlmodel#compute-plan
One-click install
npx skills add https://github.com/femitz/flyby --skill coreml-femitz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: coreml
Source: https://github.com/femitz/flyby/tree/main/.agents/skills/coreml
Command: npx skills add https://github.com/femitz/flyby --skill coreml-femitz

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Load and run Core ML models on-device through Swift APIs and configuration.

Core Features & Use Cases

  • Auto-generated Class Usage: Use Xcode-generated model classes for typed inputs and outputs during predictions.
  • Manual MLFeatureProvider: Build custom feature providers for dynamic input schemas.
  • Vision/NLP Integration: Leverage Vision or Natural Language for preprocessing and results when needed.

Quick Start

Load a model, configure compute units, and run a prediction to start on-device ML.

Frequently Asked Questions about coreml

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

FAQPage Schema
How do I load and run a Core ML model on-device using Swift?▼

To run a Core ML model on-device, load the compiled .mlmodelc or .mlpackage file using Swift APIs, configure the compute units via MLModelConfiguration, and execute predictions either through Xcode auto-generated classes or manual MLFeatureProvider inputs.

What is the difference between using auto-generated classes and MLFeatureProvider for predictions?▼

Auto-generated classes provide typed inputs and outputs for static model schemas, while manual MLFeatureProvider construction allows you to build custom feature providers for dynamic or flexible input schemas during on-device predictions.

How do I configure compute units for an on-device machine learning model?▼

Compute units are configured using MLModelConfiguration in Swift, allowing you to assign model execution to the CPU, GPU, or Neural Engine before loading the model and running predictions on-device.

Can I use Vision or Natural Language frameworks for preprocessing Core ML inputs?▼

Yes, you can leverage Vision or Natural Language frameworks to handle image or text preprocessing and parse prediction results before or after running the Core ML model on-device.

Do I need to convert my model to .mlpackage or .mlmodelc to run predictions?▼

Core ML requires models in the compiled .mlmodelc format to execute predictions on-device, though .mlpackage formats are also supported for loading and running workflows through Swift APIs.

How do I handle MLMultiArray data for Core ML model predictions?▼

MLMultiArray handling is required for managing multi-dimensional data structures when passing inputs to and extracting outputs from Core ML models via MLFeatureProvider or auto-generated classes.

Related Skills

Explore More Software Engineering Skills→
BorisserzBorisserz
1

coreml

Load and run Core ML models on-device in Swift iOS apps.

Community
Advanced
FelixRauchFelixRauch

coreml

Integrates Core ML models into iOS apps with Swift APIs and MLModelConfiguration.

Community
Advanced
harshav167harshav167

coreml

Load Core ML models and run predictions in Swift iOS apps.

Community
Advanced
pradeepmoulipradeepmouli

coreml-ref

Document Core ML APIs for model lifecycles, device management, and tensor operations.

Community
Intermediate