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name: axiom-ios-ml description: Use when deploying ANY machine learning model on-device, converting models to CoreML, compressing models, or implementing speech-to-text. Covers CoreML conversion, MLTensor, model compression (quantization/palettization/pruning), stateful models, KV-cache, multi-function models, async prediction, SpeechAnalyzer, SpeechTranscriber. user-invocable: false

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

CopyUser 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