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-compression-explorationnpx skills add apple/coreai-models --skill model-compression-explorationgit 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 | $0.00114 | $0.03500 |
| Opus 5 | $0.00057 | $0.01750 |
| Sonnet 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 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 — 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.
- 3d 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 (2,037 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 114 tokens to every session and 3,500 once invoked, about $0.0006 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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