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 johnrogers/claude-swift-engineering --skill foundation-modelsgit clone --depth 1 https://github.com/johnrogers/claude-swift-engineeringWrote 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/johnrogers/claude-swift-engineering/foundation-models)<a href="https://agentmods.dev/skills/johnrogers/claude-swift-engineering/foundation-models"><img src="https://agentmods.dev/badge/skills/johnrogers/claude-swift-engineering/foundation-models.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 | $0.00042 | $0.00749 |
| Opus 5 | $0.00021 | $0.00375 |
| Sonnet 5 | $0.00008 | $0.00150 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
foundation-models 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
| Reference | Load When |
|---|---|
| Getting Started | Setting up LanguageModelSession, checking availability, basic prompts |
| Structured Output | Using @Generable for type-safe responses, @Guide constraints |
| Tool Calling | Integrating external data (weather, contacts, MapKit) via Tool protocol |
| Streaming | AsyncSequence for progressive UI updates, PartiallyGenerated types |
| Troubleshooting | Context overflow, guardrails, errors, anti-patterns |
Core Workflow
- Check availability with
SystemLanguageModel.default.availability - Create
LanguageModelSessionwith optional instructions - Choose output type: plain String or @Generable struct
- Use streaming for long generations (>1 second)
- Handle errors: context overflow, guardrails, unsupported language
Model Capabilities
| Use Case | Foundation Models? | Alternative |
|---|---|---|
| Summarization | Yes | - |
| Extraction (key info) | Yes | - |
| Classification | Yes | - |
| Content tagging | Yes (built-in adapter) | - |
| World knowledge | No | ChatGPT, Claude, Gemini |
| Complex reasoning | No | Server LLMs |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 62 lines · 42 tokens per session scan A d099d3675a0d
foundation-models is a skill published in the GitHub repository johnrogers/claude-swift-engineering (228 stars, last pushed 7mo ago), licensed MIT. It adds 42 tokens to every session and 749 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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