charleswiltgen/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)

Boundary with ios-ai

ios-ml vs ios-ai — know the difference:

| Developer Intent | Router | |-----------------|--------| | "Use Apple Intelligence / Foundation Models" | ios-ai — Apple's on-device LLM | | "Run my own ML model on device" | ios-ml — CoreML conversion + deployment | | "Add text generation with @Generable" | ios-ai — Foundation Models structured output | | "Deploy a custom LLM with KV-cache" | ios-ml — Custom model optimization | | "Use Vision framework for image analysis" | ios-vision — Not ML deployment | | "Use pre-trained Apple NLP models" | ios-ai — Apple's models, not custom |

Rule of thumb: If the developer is converting/compressing/deploying their own model → ios-ml. If they're using Apple's built-in AI → ios-ai. If they're doing computer vision → ios-vision.

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