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Foundation Models

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johnrogers
foundation-models

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.

Overview

Publisherjohnrogers
Repositoryclaude-swift-engineering
Skill namefoundation-models
Stars
232
Forks
21
Bundled files
5
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by johnrogers on GitHub. Read the source before you install it.

Installation

Install the Foundation Models AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/johnrogers/claude-swift-engineering/tree/main/plugins/swift-engineering/skills/foundation-models
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/johnrogers/claude-swift-engineering.git /tmp/claude-swift-engineering
mkdir -p .claude/skills
cp -r /tmp/claude-swift-engineering/plugins/swift-engineering/skills/foundation-models .claude/skills/foundation-models
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Foundation Models in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Foundation Models on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Foundation Models is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Foundation Models

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.

Overview

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).

Reference Loading Guide

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.

ReferenceLoad When
Getting StartedSetting up LanguageModelSession, checking availability, basic prompts
Structured OutputUsing @Generable for type-safe responses, @Guide constraints
Tool CallingIntegrating external data (weather, contacts, MapKit) via Tool protocol
StreamingAsyncSequence for progressive UI updates, PartiallyGenerated types
TroubleshootingContext overflow, guardrails, errors, anti-patterns

Core Workflow

  1. Check availability with SystemLanguageModel.default.availability
  2. Create LanguageModelSession with optional instructions
  3. Choose output type: plain String or @Generable struct
  4. Use streaming for long generations (>1 second)
  5. Handle errors: context overflow, guardrails, unsupported language

Model Capabilities

Use CaseFoundation Models?Alternative
SummarizationYes-
Extraction (key info)Yes-
ClassificationYes-
Content taggingYes (built-in adapter)-
World knowledgeNoChatGPT, Claude, Gemini
Complex reasoningNoServer LLMs

Platform Requirements

  • iOS 26+, macOS 26+, iPadOS 26+, visionOS 26+
  • Apple Intelligence-enabled device (iPhone 15 Pro+, M1+ iPad/Mac)
  • User opted into Apple Intelligence

Common Mistakes

  1. 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.

  2. 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.

  3. 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.

  4. Forgetting to check availability — Not all devices support Foundation Models. Check SystemLanguageModel.default.availability before using. Graceful degradation is required.

  5. Ignoring guardrails — The model won't answer harmful queries. Instead of fighting it, design prompts that respect safety guidelines. Rephrasing requests usually works.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Foundation Models AI skill do?

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.

Why use Foundation Models on TypingMind?

Because you install it once and use it with any model. Foundation Models is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Foundation Models in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/johnrogers/claude-swift-engineering/tree/main/plugins/swift-engineering/skills/foundation-models. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Foundation Models?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Foundation Models?

As many as you like. As long as a model supports skills, you can use Foundation Models with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Foundation Models AI skill free?

Yes. It is published on GitHub by johnrogers under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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