Core AI Models is a toolkit for exporting AI models, creating model components in Python, and running exported models in macOS and iOS apps through Swift utilities. It is for developers building on-device AI applications with Core AI. Its catalogue skills and plugin help coding agents work with the model export and runtime tooling.
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 apple/coreai-models --skill model-compression-explorationgit clone --depth 1 https://github.com/apple/coreai-modelsWrote 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/apple/coreai-models/model-compression-exploration)<a href="https://agentmods.dev/skills/apple/coreai-models/model-compression-exploration"><img src="https://agentmods.dev/badge/skills/apple/coreai-models/model-compression-exploration/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/apple/coreai-models/model-compression-exploration"><img src="https://agentmods.dev/badge/skills/apple/coreai-models/model-compression-exploration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00114 | $0.03500 |
| Opus 5.5 | $0.00046 | $0.01400 |
| Sonnet 5.5 | $0.00023 | $0.00700 |
| Haiku 4.5 | $0.00011 | $0.00350 |
Grade A, and why
model-compression-exploration 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.
How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Compression Exploration
Systematically explore weight-only compression configurations for a PyTorch model using coreai_opt. The goal is to present the user with a clear overview of accuracy-vs-size tradeoff options across quantization and palettization, organized into three experiment groups.
Supporting files
| File | Contents |
|---|---|
compression_patterns.md |
Empirical patterns: what works, what doesn't, and why |
size_estimation.md |
How to compute theoretical compressed model size |
experiment_runner.md |
Memory-safe experiment loop, helpers, average bitwidth |
output_report.md |
How to format and organize the output produced |
Bundled scripts
The deterministic helpers are unit-tested and importable. Prefer them over hand-rolled equivalents — they encode formulas and edge cases that have already been debugged.
| Script | Purpose |
|---|---|
scripts/compression_metrics.py |
Theoretical size, average bitwidth, divisibility, parametrize walk |
scripts/quality_metrics.py |
PSNR / SNR / IoU and a per-output dispatcher |
CoreAI Opt
CoreAI Opt (coreai-opt) is a package that helps with model compression and model optimization in a hardware-aware manner.
For the full coreai-opt documentation, fetch:
https://apple.github.io/coreai-optimization/llms-full.txt
Check to see that the package is installed in the current python scope (venv, conda env). The package is called coreai-opt and is imported as coreai_opt. If it is not installed, prompt the user to install it.
For API verification at runtime, use help(coreai_opt) or inspect to confirm current signatures.
What ships with it
6 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.
- 1mo ago First seen · 192 lines · 114 tokens per session scan A 0e820caf038a
model-compression-exploration is a skill published in the GitHub repository apple/coreai-models (2,194 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 114 tokens to every session and 3,500 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 2026-08-30.
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