affaan-m/everything-claude-code/foundation-models-on-device

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Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+. Use when adding on-device LLM features with Apple FoundationModels on iOS 26+.

Install

npx vskill@latest install affaan-m/everything-claude-code/foundation-models-on-device

SKILL.md

FoundationModels: On-Device LLM (iOS 26)

Patterns for integrating Apple's on-device language model into apps using the FoundationModels framework. Covers text generation, structured output with @Generable, custom tool calling, and snapshot streaming — all running on-device for privacy and offline support.

When to Activate

  • Building AI-powered features using Apple Intelligence on-device
  • Generating or summarizing text without cloud dependency
  • Extracting structured data from natural language input
  • Implementing custom tool calling for domain-specific AI actions
  • Streaming structured responses for real-time UI updates
  • Need privacy-preserving AI (no data leaves the device)

Core Pattern — Availability Check

Always check model availability before creating a session:

swift
struct GenerativeView: View {
private var model = SystemLanguageModel.default
var body: some View {
switch model.availability {
case .available:
ContentView()
case .unavailable(.deviceNotEligible):
Text("Device not eligible for Apple Intelligence")
case .unavailable(.appleIntelligenceNotEnabled):
Text("Please enable Apple Intelligence in Settings")
case .unavailable(.modelNotReady):
Text("Model is downloading or not ready")
case .unavailable(let other):
Text("Model unavailable: \(other)")
}
}
}

Core Pattern — Basic Session

swift
// Single-turn: create a new session each time
let session = LanguageModelSession()
let response = try await session.respond(to: "What's a good month to visit Paris?")
print(response.content)
// Multi-turn: reuse session for conversation context
let session = LanguageModelSession(instructions: """
You are a cooking assistant.
Provide recipe suggestions based on ingredients.
Keep suggestions brief and practical.
""")
let first = try await session.respond(to: "I have chicken and rice")
let followUp = try await session.respond(to: "What about a vegetarian option?")

Key points for instructions:

  • Define the model's role ("You are a mentor")
  • Specify what to do ("Help extract calendar events")
  • Set style preferences ("Respond as briefly as possible")
  • Add safety measures ("Respond with 'I can't help with that' for dangerous requests")

Core Pattern — Guided Generation with @Generable

Generate structured Swift types instead of raw strings:

1. Define a Generable Type
swift
@Generable(description: "Basic profile information about a cat")
struct CatProfile {
var name: String
@Guide(description: "The age of the cat", .range(0...20))
var age: Int
@Guide(description: "A one sentence profile about the cat's personality")
var profile: String
}
2. Request Structured Output
swift
let response = try await session.respond(
to: "Generate a cute rescue cat",
generating: CatProfile.self
)
// Access structured fields directly
print("Name: \(response.content.name)")
print("Age: \(response.content.age)")
print("Profile: \(response.content.profile)")
Supported @Guide Constraints
  • .range(0...20) — numeric range
  • .count(3) — array element count
  • description: — semantic guidance for generation

Core Pattern — Tool Calling

Let the model invoke custom code for domain-specific tasks:

1. Define a Tool
swift
struct RecipeSearchTool: Tool {
let name = "recipe_search"
let description = "Search for recipes matching a given term and return a list of results."
@Generable
struct Arguments {
var searchTerm: String
var numberOfResults: Int
}
func call(arguments: Arguments) async throws -> ToolOutput {
let recipes = await searchRecipes(
term: arguments.searchTerm,
limit: arguments.numberOfResults
)
return .string(recipes.map { "- \($0.name): \($0.description)" }.joined(separator: "\n"))
}
}
2. Create Session with Tools
swift
let session = LanguageModelSession(tools: [RecipeSearchTool()])
let response = try await session.respond(to: "Find me some pasta recipes")
3. Handle Tool Errors
swift
do {
let answer = try await session.respond(to: "Find a recipe for tomato soup.")
} catch let error as LanguageModelSession.ToolCallError {
print(error.tool.name)
if case .databaseIsEmpty = error.underlyingError as? RecipeSearchToolError {
// Handle specific tool error
}
}

Core Pattern — Snapshot Streaming

Stream structured responses for real-time UI with PartiallyGenerated types:

swift
@Generable
struct TripIdeas {
@Guide(description: "Ideas for upcoming trips")
var ideas: [String]
}
let stream = session.streamResponse(
to: "What are some exciting trip ideas?",
generating: TripIdeas.self
)
for try await partial in stream {
// partial: TripIdeas.PartiallyGenerated (all properties Optional)
print(partial)
}
SwiftUI Integration
swift
@State private var partialResult: TripIdeas.PartiallyGenerated?
@State private var errorMessage: String?
var body: some View {
List {
ForEach(partialResult?.ideas ?? [], id: \.self) { idea in
Text(idea)
}
}
.overlay {
if let errorMessage { Text(errorMessage).foregroundStyle(.red) }
}
.task {
do {
let stream = session.streamResponse(to: prompt, generating: TripIdeas.self)
for try await partial in stream {
partialResult = partial
}
} catch {
errorMessage = error.localizedDescription
}
}
}

Key Design Decisions

DecisionRationale
On-device executionPrivacy — no data leaves the device; works offline
4,096 token limitOn-device model constraint; chunk large data across sessions
Snapshot streaming (not deltas)Structured output friendly; each snapshot is a complete partial state
@Generable macroCompile-time safety for structured generation; auto-generates PartiallyGenerated type
Single request per sessionisResponding prevents concurrent requests; create multiple sessions if needed
response.content (not .output)Correct API — always access results via .content property

Best Practices

  • Always check `model.availability` before creating a session — handle all unavailability cases
  • Use `instructions` to guide model behavior — they take priority over prompts
  • Check `isResponding` before sending a new request — sessions handle one request at a time
  • Access `response.content` for results — not .output
  • Break large inputs into chunks — 4,096 token limit applies to instructions + prompt + output combined
  • Use `@Generable` for structured output — stronger guarantees than parsing raw strings
  • Use `GenerationOptions(temperature:)` to tune creativity (higher = more creative)
  • Monitor with Instruments — use Xcode Instruments to profile request performance

Anti-Patterns to Avoid

  • Creating sessions without checking model.availability first
  • Sending inputs exceeding the 4,096 token context window
  • Attempting concurrent requests on a single session
  • Using .output instead of .content to access response data
  • Parsing raw string responses when @Generable structured output would work
  • Building complex multi-step logic in a single prompt — break into multiple focused prompts
  • Assuming the model is always available — device eligibility and settings vary

When to Use

  • On-device text generation for privacy-sensitive apps
  • Structured data extraction from user input (forms, natural language commands)
  • AI-assisted features that must work offline
  • Streaming UI that progressively shows generated content
  • Domain-specific AI actions via tool calling (search, compute, lookup)

Security

  • Security scanPassed, 95 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
109,581
Repository forks
14,296
Repository npm downloads, 7 days
702
7-day trend score
75

Stars and forks are for the whole GitHub repository, not this skill alone. Downloads are for the repository's npm package. Refreshed 22 Mar 2026, 7 months ago.

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