Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nklofy/code-agent-skills --skill foundation-models-on-devicegit clone --depth 1 https://github.com/nklofy/code-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nklofy/code-agent-skills/foundation-models-on-device)<a href="https://agentmods.dev/skills/nklofy/code-agent-skills/foundation-models-on-device"><img src="https://agentmods.dev/badge/skills/nklofy/code-agent-skills/foundation-models-on-device.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.01731 |
| Opus 5 | $0.00019 | $0.00865 |
| Sonnet 5 | $0.00008 | $0.00346 |
| Haiku 4.5 | $0.00004 | $0.00173 |
Grade A, and why
foundation-models-on-device scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to foundation-models-on-device — 23 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 244 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
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
// 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?")
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 244 lines · 38 tokens per session scan A 8efee7e392b3
foundation-models-on-device is a skill published in the GitHub repository nklofy/code-agent-skills (19 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 1,731 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to foundation-models-on-device, differing in 23 lines, and is treated as a copy.
Other skills, from other repositories
model-authoring
Empirical rules for authoring PyTorch models for on-device execution on Apple platforms, covering energy-efficient inference, scalable compute, and correctness testing. Use this skill whenever the user is writing, debugging, or reviewing PyTorch model code intended for on-device execution — even if they don't…
working-with-coreai
Use this skill whenever the user mentions coreai-torch, TorchConverter, coreai-build, AIModel, AIProgram, .aimodel, or wants to export/compile/run a PyTorch model on Apple silicon (iPhone, iPad, Mac). Also triggers for "deploy on device", "optimize for on-device performance", onboarding new models to Core AI, or…
axiom-ai
Use when implementing, testing, or evaluating ANY Apple Intelligence, on-device AI, or speech-to-text feature. Covers Foundation Models, @Generable, LanguageModelSession, Tool protocol, eval suites, model-as-judge scoring, SpeechTranscriber, CoreML.
firebase-ai
Use when setting up firebaseai, generating text/chat with Gemini, streaming AI output, building multimodal prompts, or handling AI errors.
frontend-a11y
Accessibility patterns for React and Next.js — semantic HTML, ARIA attributes, form labeling, keyboard navigation, focus management, and screen reader support. Use when building any interactive UI component or form.
maui-essentials-ai
Adopt Microsoft.Maui.Essentials.AI for local/on-device MAUI AI. USE FOR: Apple Intelligence chat, IChatClient, iOS/macOS/Mac Catalyst 26+ checks, fallback UI, NLEmbeddingGenerator, local tool invocation, privacy/offline UX. DO NOT USE FOR: source-generated tools, cloud-only AI, UI debugging.