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 The-Artificer-of-Ciphers-LLC/skills-from-the-artificer --skill apple-on-device-aigit clone --depth 1 https://github.com/The-Artificer-of-Ciphers-LLC/skills-from-the-artificerWrote 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/the-artificer-of-ciphers-llc/skills-from-the-artificer/apple-on-device-ai)<a href="https://agentmods.dev/skills/the-artificer-of-ciphers-llc/skills-from-the-artificer/apple-on-device-ai"><img src="https://agentmods.dev/badge/skills/the-artificer-of-ciphers-llc/skills-from-the-artificer/apple-on-device-ai/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/the-artificer-of-ciphers-llc/skills-from-the-artificer/apple-on-device-ai"><img src="https://agentmods.dev/badge/skills/the-artificer-of-ciphers-llc/skills-from-the-artificer/apple-on-device-ai.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00127 | $0.04196 |
| Opus 5.5 | $0.00051 | $0.01678 |
| Sonnet 5.5 | $0.00025 | $0.00839 |
| Haiku 4.5 | $0.00013 | $0.00420 |
Grade A, and why
apple-on-device-ai 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 today.
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 — 492 lines — stays where its author put it; the contents beside it link to each section on GitHub.
On-Device AI for Apple Platforms
Guide for selecting, deploying, and optimizing on-device ML models. Covers Apple Foundation Models, Core ML, MLX Swift, and llama.cpp.
Contents
- Framework Selection Router
- Apple Foundation Models Overview
- Core ML Overview
- MLX Swift Overview
- Multi-Backend Architecture
- Performance Best Practices
- Common Mistakes
- Review Checklist
- References
Framework Selection Router
Use this decision tree to pick the right framework for your use case.
Apple Foundation Models
When to use: Text generation, summarization, entity extraction, structured output, and short dialog on iOS 26+ / macOS 26+ devices with Apple Intelligence enabled. No app-managed API key, network round trip, or model hosting; still handle system model asset readiness.
Best for:
- Generating text or structured data with
@Generabletypes - Summarization, classification, content tagging
- Tool-augmented generation with the
Toolprotocol - Apps that need guaranteed on-device privacy
Not suited for: Complex math, code generation, factual accuracy tasks, or apps targeting pre-iOS 26 devices.
Core ML
When to use: Deploying custom trained models (vision, NLP, audio) across all Apple platforms. Converting models from PyTorch, TensorFlow, or scikit-learn with coremltools.
Best for:
- Image classification, object detection, segmentation
- Custom NLP classifiers, sentiment analysis models
- Audio/speech models via SoundAnalysis integration
- Any scenario needing Neural Engine optimization
- Models requiring quantization, palettization, or pruning
MLX Swift
When to use: Running specific open-source LLMs (Llama, Mistral, Qwen, Gemma) on Apple Silicon with maximum throughput. Research and prototyping.
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.
- today First seen · 492 lines · 127 tokens per session scan A 95974275e65c
apple-on-device-ai is a skill published in the GitHub repository The-Artificer-of-Ciphers-LLC/skills-from-the-artificer (6 stars, last pushed yesterday), licensed MIT. It adds 127 tokens to every session and 4,196 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-10-15.
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