foundation-models-on-device

foundation-models-on-device is a skill for Claude Code, Codex from Jamkris/everything-gemini-code. It costs 38 tokens per session (1,730 once invoked), scanned A, a copy of foundation-models-on-device, MIT.

An Apple framework for running language models directly on supported iPhones and iPads. It can generate text, return structured data, call app-defined tools, and stream results as they arrive.

In plain words
What is it for?
Use it to add private, on-device text generation, summarization, natural-language data extraction, structured responses, and real-time AI updates to iOS apps.
Why use it?
It lets apps use AI without sending user data to a cloud service, and can continue working without an internet connection. It also provides patterns for handling devices where Apple Intelligence is unavailable.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it to add private, on-device text generation, summarization, natural-language data extraction, structured responses, and real-time AI updates to iOS apps.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jamkris/everything-gemini-code/foundation-models-on-device
Install

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.

Any agent
npx skills add Jamkris/everything-gemini-code --skill foundation-models-on-device
Clone the repo
git clone --depth 1 https://github.com/Jamkris/everything-gemini-code

Made for: Claude Code, Codex.

Wrote 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.

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README.md
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Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,730 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 98% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00038 $0.01730
Opus 5.5 $0.00015 $0.00692
Sonnet 5.5 $0.00008 $0.00346
Haiku 4.5 $0.00004 $0.00173

Measured 1mo ago against content hash 1151b265f4bb, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 1mo 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.

Origin

This is a copy

98% identical to foundation-models-on-device — 4 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.

skills/foundation-models-on-device/SKILL.md · 244 lines

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 Use

  • 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?")

Read the full file on GitHub · 244 lines

Changes

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.

  1. 1mo ago First seen · 244 lines · 38 tokens per session scan A 1151b265f4bb

Subscribe to this mod's changes

foundation-models-on-device is a skill published in the GitHub repository Jamkris/everything-gemini-code (91 stars, last pushed 5mo ago), licensed MIT. It adds 38 tokens to every session and 1,730 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to foundation-models-on-device, differing in 4 lines, and is treated as a copy.

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