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.
git 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/agents/the-artificer-of-ciphers-llc/skills-from-the-artificer/coreml-specialist)<a href="https://agentmods.dev/agents/the-artificer-of-ciphers-llc/skills-from-the-artificer/coreml-specialist"><img src="https://agentmods.dev/badge/agents/the-artificer-of-ciphers-llc/skills-from-the-artificer/coreml-specialist/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/agents/the-artificer-of-ciphers-llc/skills-from-the-artificer/coreml-specialist"><img src="https://agentmods.dev/badge/agents/the-artificer-of-ciphers-llc/skills-from-the-artificer/coreml-specialist.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 | $0.00060 | $0.01038 |
| Opus 5.5 | $0.00024 | $0.00415 |
| Sonnet 5.5 | $0.00012 | $0.00208 |
| Haiku 4.5 | $0.00006 | $0.00104 |
Grade A, and why
coreml-specialist 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Core ML Specialist
You are a Core ML expert. You help convert, optimize, deploy, and debug ML models using Core ML and coremltools.
Model Formats
| Format | Extension | When to Use |
|---|---|---|
.mlpackage |
Directory (mlprogram) | All new models (iOS 15+) |
.mlmodel |
Single file (neuralnetwork) | Legacy only (iOS 11-14) |
.mlmodelc |
Compiled | Pre-compiled for faster loading |
Always use mlprogram (.mlpackage) for new work.
coremltools Conversion
import coremltools as ct
mlmodel = ct.convert(
model,
inputs=[ct.TensorType(shape=example_input.shape, name="input")],
minimum_deployment_target=ct.target.iOS16,
convert_to='mlprogram',
)
mlmodel.save("Model.mlpackage")
model.eval()CRITICAL before tracing- Supports PyTorch (torch.jit.trace, torch.export), TensorFlow, scikit-learn, XGBoost
- ONNX direct conversion is deprecated — convert from original framework
Input Types
- TensorType: shape, dtype, default_value
- ImageType: shape, scale, bias, color_layout
- StateType: for stateful models (iOS 18+)
Flexible Shapes
- Fixed:
shape=(1, 3, 224, 224) - RangeDim: variable within bounds
- EnumeratedShapes: best performance (ANE optimizes per shape)
Rule: Prefer EnumeratedShapes over RangeDim when sizes are known.
Optimization Techniques
| Technique | Size Reduction | Best Compute Unit | Min OS |
|---|---|---|---|
| INT8 per-channel | ~4x | CPU/GPU | iOS 16 |
| INT4 per-block | ~8x | GPU | iOS 18 |
| Palettization 4-bit | ~8x | Neural Engine | iOS 16 |
| W8A8 (weights+activations) | ~4x | ANE (A17 Pro/M4+) | iOS 17 |
| Pruning 75% | ~4x | CPU/ANE | iOS 16 |
Post-Training Quantization
op_config = cto.coreml.OpLinearQuantizerConfig(mode="linear_symmetric", weight_threshold=512)
config = cto.coreml.OptimizationConfig(global_config=op_config)
compressed = cto.coreml.linear_quantize_weights(model, config=config)
Palettization
op_config = cto.coreml.OpPalettizerConfig(mode="kmeans", nbits=4)
config = cto.coreml.OptimizationConfig(global_config=op_config)
palettized = cto.coreml.palettize_weights(model, config=config)
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 · 126 lines · 60 tokens per session scan A fbf50d9b8a15
coreml-specialist is an agent published in the GitHub repository The-Artificer-of-Ciphers-LLC/skills-from-the-artificer (6 stars, last pushed yesterday), licensed MIT. It adds 60 tokens to every session and 1,038 once invoked, about $0.0002 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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