coreml
Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
Profile →npx skills add dpearson2699/swift-ios-skills --skill coreml --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Core ML Swift Integration Load, configure, and run Core ML models in iOS apps. This skill covers the Swift side: model loading, prediction, MLTensor, profiling, and deployment. > **Scope boundary:** Python-side model conversion, optimization (quantization, > palettization, pruning), and framework selection live in the `apple-on-device-ai` > skill. This skill owns Swift integration only. See [references/coreml-swift-integration.md](references/coreml-swift-integration.md) for complete code patterns including actor-based caching, batch inference, image preprocessing, and testing. ## Contents - [Loading Models](#loading-models) - [Model Configuration](#model-configuration) - [Making Predictions](#making-predictions) - [MLTensor (iOS 18+)](#mltensor-ios-18) - [Working with MLMultiArray](#working-with-mlmultiarray) - [Image Preprocessing](#image-preprocessing) - [Multi-Model Pipelines](#multi-model-pipelines) - [Vision Integration](#vision-integration) - [Performance Profiling](#performance-profiling) - [Model Deployment](#model-deployment) - [Memory Management](#memory-management) - [Common Mistakes](#common-mistakes) - [Review Checklist](#review-checklist) - [References](#references)
- Contents
- Loading Models
- Auto-Generated Classes
- Manual Loading
- Async Loading (iOS 15+)
- Compile at Runtime (iOS 16+)
- Model Configuration
- Compute Units Decision Table
- Configuration Properties
- Making Predictions
- With Auto-Generated Classes
- With MLDictionaryFeatureProvider
- Prediction Inside Async Workflows
- Batch Prediction
What does the coreml skill do?
Integrate Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodel, .mlpackage, .mlmodelc), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.
How do I install it?
Run `npx skills add dpearson2699/swift-ios-skills --skill coreml --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From dpearson2699/swift-ios-skills, a repository with 963 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.