| 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 |
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