axiom-ios-ml

# iOS Machine Learning Router

**You MUST use this skill for ANY on-device machine learning or speech-to-text work.**

## When to Use

Use this router when:
- Converting PyTorch/TensorFlow models to CoreML
- Deploying ML models on-device
- Compressing models (quantization, palettization, pruning)
- Working with large language models (LLMs)
- Implementing KV-cache for transformers
- Using MLTensor for model stitching
- Building speech-to-text features
- Transcribing audio (live or recorded)

## Routing Logic

### CoreML Work

**Implementation patterns** → `/skill coreml`
- Model conversion workflow
- MLTensor for model stitching
- Stateful models with KV-cache
- Multi-function models (adapters/LoRA)
- Async prediction patterns
- Compute unit selection

**API reference** → `/skill coreml-ref`
- CoreML Tools Python API
- MLModel lifecycle
- MLTensor operations
- MLComputeDevice availability
- State management APIs
- Performance reports

**Diagnostics** → `/skill coreml-diag`
- Model won't load
- Slow inference
- Memory issues
- Compression accuracy loss
- Compute unit problems

### Speech Work

**Implementation patterns** → `/skill speech`
- SpeechAnalyzer setup (iOS 26+)
- SpeechTranscriber configuration
- Live transcription
- File transcription
- Volatile vs finalized results
- Model asset management

## Decision Tree

1. Implementing / converting ML models? → coreml
2. CoreML API reference? → coreml-ref
3. Debugging ML issues (load, inference, compression)? → coreml-diag
4. Speech-to-text / transcription? → speech

## Anti-Rationalization

| Thought | Reality |
|---------|---------|
| "CoreML is just load and predict" | CoreML has compression, stateful models, compute unit selection, and async prediction. coreml covers all. |
| "My model is small, no optimization needed" | Even small models benefit from compute unit selection and async prediction. coreml has the patterns. |
| "I'll just use SFSpeechRecognizer" | iOS 26 has SpeechAnalyzer with better accuracy and off