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Use when implementing on-device AI with Apple's Foundation Models framework (iOS 26+), building summarization/extraction/classification features, or using @Generable for type-safe structured output.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use when building features with TCA (The Composable Architecture), structuring reducers, managing state, handling effects, navigation, or testing TCA features. Covers @Reducer, Store, Effect, TestStore, reducer composition, and TCA patterns.
Use when implementing iOS 26 features (Liquid Glass, new SwiftUI APIs, WebView, Chart3D), deploying iOS 26+ apps, or supporting backward compatibility with iOS 17/18.
Use when implementing iOS 17+ SwiftUI patterns: @Observable/@Bindable, MVVM architecture, NavigationStack, lazy loading, UIKit interop, accessibility (VoiceOver/Dynamic Type), async operations (.task/.refreshable), or migrating from ObservableObject/@StateObject.
Use when writing async/await code, enabling strict concurrency, fixing Sendable errors, migrating from completion handlers, managing shared state with actors, or using Task/TaskGroup for concurrency.
Use when writing raw SQL with GRDB, complex joins across 4+ tables, window functions, ValueObservation for reactive queries, or dropping down from SQLiteData for performance. Direct SQLite access for iOS/macOS with type-safe queries and migrations.
Use when adding haptic feedback for user confirmations (button presses, toggles, purchases), error notifications, or custom tactile patterns (Core Haptics). Covers UIFeedbackGenerator and CHHapticEngine patterns.
| name | foundation-models |
| description | Use when implementing on-device AI with Apple's Foundation Models framework (iOS 26+), building summarization/extraction/classification features, or using @Generable for type-safe structured output. |
Apple's on-device AI framework providing access to a 3B parameter language model for summarization, extraction, classification, and content generation. Runs entirely on-device with no network required.
Foundation Models enable intelligent text processing directly on device without server round-trips, user data sharing, or network dependencies. The core principle: leverage on-device AI for specific, contained tasks (not for general knowledge).
ALWAYS load reference files if there is even a small chance the content may be required. It's better to have the context than to miss a pattern or make a mistake.
| Reference | Load When |
|---|---|
| Getting Started | Setting up LanguageModelSession, checking availability, basic prompts |
| Structured Output | Using @Generable for type-safe responses, @Guide constraints |
| Tool Calling | Integrating external data (weather, contacts, MapKit) via Tool protocol |
| Streaming | AsyncSequence for progressive UI updates, PartiallyGenerated types |
| Troubleshooting | Context overflow, guardrails, errors, anti-patterns |
SystemLanguageModel.default.availabilityLanguageModelSession with optional instructions| Use Case | Foundation Models? | Alternative |
|---|---|---|
| Summarization | Yes | - |
| Extraction (key info) | Yes | - |
| Classification | Yes | - |
| Content tagging | Yes (built-in adapter) | - |
| World knowledge | No | ChatGPT, Claude, Gemini |
| Complex reasoning | No | Server LLMs |
Using Foundation Models for world knowledge — The 3B model is trained for on-device tasks only. It won't know current events, specific facts, or "who is X". Use ChatGPT/Claude for that. Keep prompts to: summarizing user's own content, extracting info, classifying text.
Blocking the main thread — LanguageModelSession calls must run on a background thread or async context. Blocking the main thread locks UI. Always use Task { } or background queue.
Ignoring context overflow — The model has finite context. If the user pastes a 50KB document, it will fail silently or truncate. Check input length and trim/truncate proactively.
Forgetting to check availability — Not all devices support Foundation Models. Check SystemLanguageModel.default.availability before using. Graceful degradation is required.
Ignoring guardrails — The model won't answer harmful queries. Instead of fighting it, design prompts that respect safety guidelines. Rephrasing requests usually works.