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 offline support. speech skill covers the modern API.

Critical Patterns

coreml:

• Model conversion (PyTorch → CoreML)
• Compression (palettization, quantization, pruning)
• Stateful KV-cache for LLMs
• Multi-function models for adapters
• MLTensor for pipeline stitching
• Async concurrent prediction

coreml-diag:

• Load failures and caching
• Inference performance issues
• Memory pressure from models
• Accuracy degradation from compression

speech:

• SpeechAnalyzer + SpeechTranscriber setup
• AssetInventory model management
• Live transcription with volatile results
• Audio format conversion

Example Invocations

User: "How do I convert a PyTorch model to CoreML?"
→ Invoke: /skill coreml

User: "Compress my model to fit on iPhone"
→ Invoke: /skill coreml

User: "Implement KV-cache for my language model"
→ Invoke: /skill coreml

User: "Model loads slowly on first launch"
→ Invoke: /skill coreml-diag

User: "My compressed model has bad accuracy"
→ Invoke: /skill coreml-diag

User: "Add live transcription to my app"
→ Invoke: /skill speech

User: "Transcribe audio files with SpeechAnalyzer"
→ Invoke: /skill speech

User: "What's MLTensor and how do I use it?"
→ Invoke: /skill coreml-ref