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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 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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.