macos-tahoe-apis_skill

Overview

This skill is a practical guide to macOS 26 (Tahoe) APIs, focused on Apple Intelligence, Foundation Models, the MLX framework, Continuity, and platform-specific features. It helps developers choose the right Tahoe APIs, modernize code for Xcode 16, and plan for on-device ML and cross-device integration. Begin by describing your project requirements and target macOS support so I can tailor recommendations.

How this skill works

I inspect which macOS 26 features apply to your app, highlight modern API alternatives, and show concise Swift examples and integration patterns. The skill maps Apple Intelligence capabilities (foundation models, on-device inference) to the MLX framework and advises on MCP and M5 optimizations. It also reviews Continuity surface areas and suggests migration steps from deprecated APIs.

When to use it

• Building or upgrading an app targeting macOS 26 (Tahoe) features
• Implementing on-device AI or integrating Foundation Models and Apple Intelligence
• Optimizing ML workloads for M5 and MLX framework
• Adding or improving Continuity features across Apple devices
• Preparing an app for Xcode 16 and Tahoe-specific entitlements and capabilities

Best practices

• Start by specifying minimum macOS target and fallback behavior to preserve compatibility
• Prefer modern Tahoe APIs and check deprecation notes before using legacy APIs
• Use MLX with hardware acceleration and memory-aware batching for M5 devices
• Keep model inference on-device when possible; use secure MCP for sensitive requests
• Test Continuity flows across real devices and consider network variability and privacy
• Document entitlements and user-facing permissions required by Apple Intelligence features

Example use cases

• Add Spotlight-aware search and rich previews using Tahoe Spotlight extensions
• Integrate a Foundation Model for local summarization with MLX-optimized inference on M5
• Implement Continuity Hand-off and Handoff-like workflows between iPhone and macOS 26
• Replace legacy Core ML paths with MLX for better performance and memory usage
• Use Xcode 16 build settings and new Swift concurrency patterns to modernize async ML pipelines