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

User asks about on-device ML or speech ├─ Machine learning? │ ├─ Implementing/converting? → coreml │ ├─ Need API reference? → coreml-ref │ └─ Debugging issues? → coreml-diag └─ Speech-to-text? └─ Any speech work → speech

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