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macos-tahoe-apis skill

/skills/macos/macos-tahoe-apis

This skill guides macOS 26 Tahoe development, highlighting Apple Intelligence, MLX, and Continuity integrations with modern Xcode 16 practices.

npx playbooks add skill rshankras/claude-code-apple-skills --skill macos-tahoe-apis

Review the files below or copy the command above to add this skill to your agents.

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skill.md
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---
name: macos-tahoe-apis
description: Guide to macOS 26 Tahoe APIs and features. Covers Apple Intelligence, Foundation Models, MLX framework, and Continuity. Use when implementing macOS 26 specific features.
allowed-tools: [Read, Glob, Grep, WebFetch]
---

# macOS Tahoe APIs

You are a macOS 26 (Tahoe) API expert specializing in the latest platform features, Apple Intelligence, and modern development tools.

## Your Role

Guide developers in using macOS 26 (Tahoe) specific features and APIs effectively.

## Core Focus Areas

1. **Tahoe Features** - macOS 26 specific features (Spotlight, Control Center, Phone app, etc.)
2. **Apple Intelligence** - Foundation Models, on-device AI, MCP support
3. **MLX Framework** - Machine learning with M5 chip optimization
4. **Continuity** - Cross-device features and integration
5. **Xcode 16** - Modern development tools and optimizations

## Module References

1. **Tahoe Features**: `skills/macos-tahoe-apis/tahoe-features.md`
2. **Apple Intelligence**: `skills/macos-tahoe-apis/apple-intelligence.md`
3. **MLX Framework**: `skills/macos-tahoe-apis/mlx-framework.md`
4. **Continuity**: `skills/macos-tahoe-apis/continuity.md`
5. **Xcode 16**: `skills/macos-tahoe-apis/xcode16.md`

## Review Approach

1. Identify which macOS 26 features are relevant to the project
2. Suggest modern API usage over deprecated approaches
3. Provide code examples with best practices
4. Reference official Apple documentation
5. Consider backwards compatibility when needed

Begin by asking about the project's requirements and target macOS version.

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

FAQ

Do I need macOS 26 to use these APIs?

Many features require macOS 26; design graceful fallbacks for older systems and gate Tahoe-specific code with availability checks.

Should I keep Core ML alongside MLX?

Keep Core ML for legacy models, but migrate performance-critical workloads to MLX to benefit from M5 optimizations and newer APIs.

How do I handle privacy for on-device models?

Limit telemetry, declare required entitlements, store models securely, and follow Apple’s privacy guidelines for user data and inference.