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 agentmods add skills/apple/coreai-models/model-authoringnpx skills add apple/coreai-models --skill model-authoringgit clone --depth 1 https://github.com/apple/coreai-modelsWhat 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 | $0.00095 | $0.02084 |
| Opus 5 | $0.00048 | $0.01042 |
| Sonnet 5 | $0.00019 | $0.00417 |
| Haiku 4.5 | $0.00010 | $0.00208 |
Grade A, and why
model-authoring 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 3d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Authoring
This skill contains the hard-won empirical knowledge for making PyTorch models compile and run correctly on Apple hardware via Core AI. The rules here are stable across Core AI releases — they reflect hardware behavior, not API shapes.
Reference material
Use these resources on-demand — do not read all files upfront. Consult the relevant reference when the user's task requires specific patterns for a target platform, or when debugging.
| Resource | When to consult |
|---|---|
neural_engine_rules.md |
Neural Engine patterns: BC1S layout, Conv2d projections, per-head attention, KV cache readonly pattern, stride/dilation/pooling rules, causal mask, RoPE, chunked prefill |
gpu_rules.md |
GPU patterns: fused QKV, native SDPA, KV cache stateful pattern, MoE (GatherMM/SwitchLinear), memory-efficient loading, RMSNorm variants |
common_issues.md |
Debugging: PSNR issues, compilation errors, runtime problems, stale flags |
| coreai-models repo | Complete working reference implementations for LLMs, vision, audio, diffusion. Explore primitives/ and models/ directories. |
coreai-models: working reference implementations
For complex models (LLMs, MoE, multimodal, diffusion), explore the coreai-models repo before writing primitives from scratch. It has complete authoring primitives for both GPU and Neural Engine, including advanced patterns like iOS embedding quantization, MoE routing, and memory-efficient weight loading for large models. If the user has a local clone, explore it directly. If not, suggest cloning it.
Online docs: coreai-torch composite ops | externalization | composite ops API
What ships with it
3 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.
- 3d ago First seen · 154 lines · 95 tokens per session scan A 0786578f0fd3
model-authoring is a skill published in the GitHub repository apple/coreai-models (2,037 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 95 tokens to every session and 2,084 once invoked, about $0.0005 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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