Coreml

Handles the Swift side of Core ML integration: loading models, configuring compute units (CPU, GPU, Neural Engine), running predictions, and working with MLTensor and MLMultiArray. Covers both auto-generated model classes and manual MLFeatureProvider paths, plus batch inference, stateful predictions for sequence models, and Vision framework integration. The skill draws a clear line between Swift integration (what's here) and Python-side conversion and optimization (handled elsewhere). Good decision tables for compute unit selection and concrete examples of async loading, image preprocessing, and multi-model pipelines. Targets iOS 14+ with newer features flagged by version. Use this when you're wiring up Core ML models in an iOS app and need the boilerplate right.

skills.sh installs: 3.9K
GitHub stars: 1.3K
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add dpearson2699/swift-ios-skills --skill coreml --agent claude-code

Installs into .claude/skills of the current project.

Files

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 for complete code patterns including actor-based caching, batch inference, image preprocessing, and testing.

SKILL.md on GitHub

Sponsors

Promote your product hereReach 125k+ visitors monthly Promote your product hereReach 125k+ visitors monthly