Add scan flow MVP and local Axiom skill workspace

This snapshot establishes the camera-to-result recognition flow and related tests while checking in the project skill/docs assets required for the configured local tooling.

.claude/skills/axiom-ios-ml/SKILL.md

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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.
+license: MIT
+---
+
+# 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`