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.
You MUST use this skill for ANY on-device machine learning or speech-to-text work.
Use this router when:
Implementation patterns → /skill coreml
API reference → /skill coreml-ref
Diagnostics → /skill coreml-diag
Implementation patterns → /skill speech
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
coreml:
coreml-diag:
speech:
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