rshankras/claude-code-apple-skills/foundation-models

Claude Code and agent skill by rshankras/claude-code-apple-skills

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On-device LLM integration using Apple's Foundation Models framework. Use when implementing AI text generation, structured output, or tool calling.

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npx vskill@latest install rshankras/claude-code-apple-skills/foundation-models

SKILL.md

Foundation Models

Integrate Apple's on-device LLM into your apps for privacy-preserving AI features. Companion references: safety-and-guardrails.md (model limits, prompt design, the four-layer safety stack), models-and-agents.md (Private Cloud Compute, LanguageModel protocol, vision input, DynamicProfile agentic sessions — the iOS 27 wave), and utilities-package.md (Apple's open-source utilities package: OpenAI-compatible endpoints, just-in-time Skills, history compression).

When This Skill Activates

  • User wants AI text generation features
  • User needs structured data from natural language
  • User asks about prompting or LLM integration
  • User wants to implement AI assistants or agentic features (tool loops, multi-profile sessions)
  • User needs content summarization or extraction
  • User asks about Private Cloud Compute, guardrails, or model safety

Model Fit — Check Before Building

The on-device model is ~3B parameters (2-bit quantized): built for summarization, extraction, classification, tagging, revision, short chat — not math, code generation, facts, or world knowledge (WWDC25 248). For capability boundaries, prompt-design rules, and the safety stack, read safety-and-guardrails.md first. For anything bigger, PrivateCloudComputeLanguageModel (32k context, reasoning) and third-party backends are in models-and-agents.md.

Quick Start

1. Check Availability
swift
import FoundationModels
struct IntelligentView: View {
private var model = SystemLanguageModel.default
var body: some View {
switch model.availability {
case .available:
ContentView()
case .unavailable(.deviceNotEligible):
UnsupportedDeviceView()
case .unavailable(.appleIntelligenceNotEnabled):
EnableIntelligenceView()
case .unavailable(.modelNotReady):
ModelDownloadingView()
case .unavailable(let reason):
ErrorView(reason: reason)
}
}
}
2. Create a Session
swift
// Simple session
let session = LanguageModelSession()
// Session with instructions
let session = LanguageModelSession(instructions: """
You are a helpful cooking assistant.
Provide concise, practical advice for home cooks.
""")
3. Generate Response
swift
let response = try await session.respond(to: "What's a quick dinner idea?")
print(response.content)

Prompt Engineering Best Practices

The Instruction Formula

Instructions set the model's persona and constraints. They're prioritized over prompts.

[Role] + [Task] + [Style] + [Safety]

Example:

swift
let instructions = """
You are a fitness coach specializing in home workouts.
Help users create exercise routines based on their equipment and goals.
Keep responses under 100 words and use bullet points for exercises.
Decline requests for medical advice and suggest consulting a doctor.
"""
Instruction Components
ComponentPurposeExample
RoleDefine persona"You are a travel expert"
TaskWhat to do"Help plan itineraries"
StyleOutput format"Use bullet points, be concise"
SafetyBoundaries"Don't provide medical advice"
Effective Prompts

Prompts are user inputs. Make them:

PrincipleBadGood
Specific"Help with cooking""Suggest a 30-minute vegetarian dinner"
Constrained"Tell me about dogs""Describe Golden Retrievers in 3 sentences"
Focused"I need help with many things""What ingredients substitute for eggs in baking?"
Prompt Patterns

Question Pattern:

swift
let prompt = "What are three ways to reduce food waste at home?"

Command Pattern:

swift
let prompt = "Create a weekly meal plan for a family of four, budget-friendly."

Extraction Pattern:

swift
let prompt = """
Extract the following from this email:
- Sender name
- Meeting date
- Action items
Email: \(emailContent)
"""

Transformation Pattern:

swift
let prompt = "Rewrite this text to be more formal: \(casualText)"

Structured Output with @Generable

Get typed Swift data instead of raw strings.

Define Generable Types
swift
@Generable(description: "A recipe suggestion")
struct Recipe {
var name: String
@Guide(description: "Cooking time in minutes", .range(5...180))
var cookingTime: Int
@Guide(description: "Difficulty level", .options(["Easy", "Medium", "Hard"]))
var difficulty: String
@Guide(description: "List of ingredients", .count(3...15))
var ingredients: [String]
@Guide(description: "Step-by-step instructions")
var instructions: [String]
}
@Guide Constraints
ConstraintUse CaseExample
.range(min...max)Numeric bounds.range(1...100)
.options([...])Enum-like choices.options(["Low", "Medium", "High"])
.count(n)Exact array length.count(5)
.count(min...max)Array length range.count(3...10)
Two Rules the Macro Hides (WWDC25 301)
  • Don't re-describe your schema in the prompt. The framework injects your @Generable type's details "in a specific format that the model has been trained on" — hand-written "respond in JSON with fields…" text duplicates it and wastes tokens. Constrained decoding masks invalid tokens per-step, so structural correctness is guaranteed, not prompted for.
  • Property order is generation order. "Properties are generated in the order they are declared on your Swift struct… you may find that the model produces the best summaries when they're the last property" (WWDC25 286). Put conditioning fields (context, inputs, reasoning) before the properties that should depend on them; put summaries last. This affects output quality and streaming animations.

For schemas only known at runtime, build a DynamicGenerationSchema (supports arrayOf: and referenceTo: cross-references), validate with GenerationSchema(root:dependencies:) (throws on unresolved references), respond via session.respond(to:schema:), and read untyped values with response.content.value(String.self, forProperty: "question").

Generate Structured Data
swift
let session = LanguageModelSession(instructions: """
You are a recipe assistant. Generate practical, home-cook friendly recipes.
""")
let recipe = try await session.respond(
to: "Suggest a quick pasta dish",
generating: Recipe.self
)
print("Recipe: \(recipe.content.name)")
print("Time: \(recipe.content.cookingTime) minutes")
print("Ingredients: \(recipe.content.ingredients.joined(separator: ", "))")
Complex Nested Structures
swift
@Generable(description: "A travel itinerary")
struct Itinerary {
var destination: String
@Guide(description: "Daily activities for the trip")
var days: [DayPlan]
}
@Generable(description: "Activities for one day")
struct DayPlan {
var dayNumber: Int
@Guide(description: "Morning activity")
var morning: String
@Guide(description: "Afternoon activity")
var afternoon: String
@Guide(description: "Evening activity")
var evening: String
}

Tool Calling

Let the model call your code to access data or perform actions.

Define a Tool
swift
struct WeatherTool: Tool {
let name = "getWeather" // verb, short, no abbreviations
let description = "Get current weather for a location" // ~one sentence
@Generable
struct Arguments {
@Guide(description: "City name")
var location: String
}
func call(arguments: Arguments) async throws -> ToolOutput {
let weather = await WeatherService.shared.fetch(for: arguments.location)
return ToolOutput("Temperature: \(weather.temp)°F, Conditions: \(weather.conditions)")
}
}

Rules from the deep dive (WWDC25 301):

  • Name = verb, description = one sentence. "These strings are put verbatim in your prompt. So longer strings means more tokens, which can increase the latency." No abbreviations, no implementation details.
  • Arguments are `@Generable` — guided generation guarantees valid arguments; nest @Generable enums to give the model a closed set of options.
  • The session holds one instance for its whole lifetime — tools may be stateful (e.g. track already-returned results to avoid repeats).
  • Tools can be called in parallel within a single request — tool state must be concurrency-safe.
  • Tool output lands in the transcript like model output — it consumes context window.
Use Tools in Session
swift
let weatherTool = WeatherTool()
let session = LanguageModelSession(
instructions: "You help users plan outdoor activities based on weather.",
tools: [weatherTool]
)
// Model automatically calls tool when needed
let response = try await session.respond(
to: "Should I go hiking in San Francisco today?"
)
Tool Error Handling
swift
do {
let response = try await session.respond(to: prompt)
} catch let error as LanguageModelSession.ToolCallError {
print("Tool '\(error.tool.name)' failed: \(error.underlyingError)")
} catch {
print("Generation error: \(error)")
}

Snapshot Streaming

Show responses as they generate for better UX.

Stream to SwiftUI
swift
@Generable
struct StoryIdea {
var title: String
@Guide(description: "A brief plot summary")
var plot: String
@Guide(description: "Main characters", .count(2...4))
var characters: [String]
}
struct StreamingView: View {
@State private var partial: StoryIdea.PartiallyGenerated?
@State private var isGenerating = false
var body: some View {
VStack(alignment: .leading) {
if let partial {
if let title = partial.title {
Text(title).font(.headline)
}
if let plot = partial.plot {
Text(plot)
}
if let characters = partial.characters {
ForEach(characters, id: \.self) { char in
Text("• \(char)")
}
}
}
Button("Generate Story Idea") {
Task { await generateStory() }
}
.disabled(isGenerating)
}
}
func generateStory() async {
isGenerating = true
defer { isGenerating = false }
let session = LanguageModelSession()
let stream = session.streamResponse(
to: "Create a sci-fi story idea",
generating: StoryIdea.self
)
for try await snapshot in stream {
partial = snapshot
}
}
}

Multi-Turn Conversations

Reuse sessions to maintain context.

swift
@Observable
final class ChatViewModel {
private var session: LanguageModelSession?
var messages: [ChatMessage] = []
func startConversation() {
session = LanguageModelSession(instructions: """
You are a helpful assistant. Remember context from earlier in our conversation.
""")
}
func send(_ message: String) async throws {
guard let session else { return }
messages.append(ChatMessage(role: .user, content: message))
let response = try await session.respond(to: message)
messages.append(ChatMessage(role: .assistant, content: response.content))
}
}

Error Handling

This is the start of the file. Read the full SKILL.md on GitHub.

Security

  • Security scanPassedPattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

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

Source

Popularity

Repository stars
770
Repository forks
74
7-day trend score
33

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 18 Mar 2026, 7 months ago.

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