Core ML, Create ML, Vision framework, Natural Language framework, on-device ML integration. Use when user wants image classification, text analysis, object detection, sound classification, model optimization, or custom model integration. Covers Core ML vs Foundation Models decision.

Core ML Skills Combined advisory, generator, and workflow skill for integrating machine learning into Apple platform apps. Covers Core ML model integration, Vision framework image analysis, NaturalLanguage framework text processing, Create ML training, and on device model optimization. When This Skill Activates Use this skill when the user: Wants to add ML capabilities to their app Needs to integrate a Core ML model (.mlmodel) into an Xcode project Wants to use the Vision framework for image analysis (faces, text recognition, body pose, object detection) Wants to use the NaturalLanguage framework for text processing (sentiment, entities, language detection) Needs to train a custom model with Create ML Wants to optimize a model for on device use (quantization, pruning, palettization) Needs to choose between Core ML and Foundation Models (Apple Intelligence) Asks about image classification, object detection, sound classification, or tabular data prediction Wants real time camera + ML processing Decision Guide: Core ML vs Foundation Models Before generating code, determine which framework is appropriate. Use Foundation Models (Apple Intelligence) When: You need general purpose text generation, summarization, or conversational AI Target is iOS 26+ / macOS 26+ (Foundation Models requires Apple Silicon + latest OS) The task is open ended language understanding or generation You want @Generable structured output from natural language See apple intelligence/foundation models/ skill for implementation Use Core ML When: You need specialized ML: image classification, object detection, sound classification, custom regression/classification You have a trained model (.mlmodel, .mlpackage) or plan to train one You need broad device support (iOS 14+ / macOS 11+) The task requires domain specific predictions (medical imaging, product recognition, custom NLP) Performance critical inference on Neural Engine or GPU Use Vision Framework When (No Custom Model Needed): Image classification using Apple's built in models Face detection and facial landmark analysis Text recognition (OCR) with VNRecognizeTextRequest Body and hand pose detection Barcode and QR code scanning Image similarity and saliency detection Horizon detection, rectangle detection Use NaturalLanguage Framework When (No Custom Model Needed): Sentiment analysis on text Language identification Tokenization (word, sentence, paragraph boundaries) Named entity recognition (people, places, organizations) Word and sentence embeddings for similarity comparison Lemmatization and part of speech tagging Pre Generation Checks 1. Project Context Detection [ ] Check deployment target (Core ML requires iOS 11+ / macOS 10.13+; Vision requires iOS 11+; NaturalLanguage requires iOS 12+) [ ] Check for existing ML code or models [ ] Identify project structure and source file locations [ ] Determine if SwiftUI or UIKit/AppKit 2. Conflict Detection Search for existing ML integration: If found, ask user: Extend existing ML setup? Replace with new implementation? Add additional model/capability? Configuration Questions Ask user via AskUserQuestion: 1. What ML capability do you need? Image classification (identify objects in photos) Object detection (locate objects with bounding boxes) Text analysis (sentiment, entities, language) Custom Core ML model integration Vision framework (OCR, faces, poses) Sound classification Tabular data prediction 2. Do you have a trained model, or need to train one? I have a .mlmodel / .mlpackage file I want to train with Create ML I want to use Apple's built in models (Vision / NaturalLanguage) 3. Performance requirements? Real time (camera feed, < 33ms per prediction) Interactive (user initiated, < 500ms acceptable) Background processing (batch, latency not critical) Core ML Model Integration Adding a Model to Xcode 1. Drag .mlmodel or .mlpackage into Xcode project navigator 2. Xcode auto generates a Swift class with the model name 3. The generated class provides type safe input/output interfaces 4. Xcode compiles to .mlmodelc at build time (optimized for device) Loading Models Making Predictions Create ML Training Overview Image Classification Minimum : 10 images per category; Recommended : 40+ per category Organize images in folders named by category Supports JPEG, PNG, HEIC formats Data augmentation applied automatically (rotation, flip, crop) Transfer learning from Apple's base models Text Classification Training data: text samples with labels (CSV or JSON) Use cases: sentiment analysis, spam detection, topic classification, intent recognition Minimum 10 samples per class; 100+ recommended for accuracy Tabular Classification / Regression Structured data in CSV or JSON Automatic feature engineering Supports: Boosted Tree, Random Forest, Linear Regression, Decision Tree Sound Classification Audio files organized by category Environmental sounds, speech detection, music genre Minimum 10 samples per category at 15+ seconds each Object Detection Images with bounding box annotations (JSON format) Outputs bounding boxes + class labels + confidence Minimum 30 annotated images per class; 300+ recommended Training Approach Xcode Create ML App : Visual interface, drag and drop, no code required CreateML Framework : Programmatic training in Swift Playgrounds or macOS apps coremltools (Python) : Convert models from TensorFlow, PyTorch, ONNX to Core ML format Vision Framework Capabilities Capability Request Class Custom Model Needed? Image classification VNClassifyImageRequest No (built in) Object detection VNDetectObjectsRequest (custom model) Yes Face detection VNDetectFaceRectanglesRequest No Face landmarks VNDetectFaceLandmarksRequest No Text recognition (OCR) VNRecognizeTextRequest No Body pose VNDetectHumanBodyPoseRequest No Hand pose VNDetectHumanHandPoseRequest No Barcode detection VNDetectBarcodesRequest No Image saliency VNGenerateAttentionBasedSaliencyImageRequest No Horizon detection VNDetectHorizonRequest No Rectangle detection VNDetectRectanglesRequest No Image similarity VNGenerateImageFeaturePrintRequest No Vision Request Pipeline NaturalLanguage Framework Sentiment Analysis Language Detection Tokenization Named Entity Recognition Model Optimization Quantization (coremltools Python) Reduces model size by lowering numerical precision: Float32 to Float16 : ~50% size reduction, minimal accuracy loss Float16 to Int8 : ~50% further reduction, test accuracy carefully Palettization Reduces unique weight values using k means clustering: Pruning Removes near zero weights (sparse model): Optimization Guidelines Always benchmark accuracy after optimization Start with Float16 (safest, best effort to reward ratio) Test on target device (Neural Engine behavior differs from GPU) Profile with Xcode Instruments Core ML Performance Performance Patterns Compute Unit Selection Async Prediction for UI Responsiveness Batch Processing Compile Model at Install Time Generation Process Step 1: Determine Capability Based on user's answer to configuration questions, select the appropriate template(s) from templates.md . Step 2: Generate Core Files Capability Files Generated Any Core ML MLModelManager.swift Image classification ImageClassifier.swift Text analysis TextAnalyzer.swift Vision requests VisionService.swift Custom model ModelConfig.swift + model specific predictor Camera + ML CameraMLPipeline.swift Step 3: Determine File Location Check project structure: If Sources/ exists Sources/ML/ If App/Services/ exists App/Services/ML/ If App/ exists App/ML/ Otherwise ML/ Output Format After generation, provide: Files Created Integration Steps 1. Add .mlmodel file to Xcode project (if using custom model) 2. Import the generated ML service files 3. Initialize the service in your app's dependency injection 4. Call prediction methods from your views/view models 5. Handle errors and display results Testing Use known test inputs with expected outputs Verify confidence thresholds Profile prediction latency on target device Test graceful degradation when model unavailable References patterns.md — Architecture patterns, model manager, Vision pipeline, camera + ML, testing templates.md — Production ready Swift code templates for all ML capabilities Apple Docs: [Core ML Documentation](https://developer.apple.com/documentation/coreml) Apple Docs: [Vision Documentation](https://developer.apple.com/documentation/vision) Apple Docs: [NaturalLanguage Documentation](https://developer.apple.com/documentation/naturallanguage) Apple Docs: [Create ML Documentation](https://developer.apple.com/documentation/createml)

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