axiom-ios-ml

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iOS Machine Learning Router

iOS机器学习路由

You MUST use this skill for ANY on-device machine learning or speech-to-text work.

任何设备端机器学习或语音转文本工作都必须使用此技能。

When to Use

使用场景

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)

在以下场景中使用此路由：

将PyTorch/TensorFlow模型转换为CoreML
在设备端部署机器学习模型
模型压缩（量化、调色板化、剪枝）
处理大语言模型（LLMs）
为Transformer模型实现KV缓存
使用MLTensor进行模型拼接
构建语音转文本功能
音频转录（实时或录制）

Routing Logic

路由逻辑

CoreML Work

CoreML相关工作

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

实现模式 →

/skill coreml

模型转换工作流
用于模型拼接的MLTensor
带KV缓存的有状态模型
多功能模型（适配器/LoRA）
异步预测模式
计算单元选择

API参考 →

/skill coreml-ref

CoreML Tools Python API
MLModel生命周期
MLTensor操作
MLComputeDevice可用性
状态管理API
性能报告

诊断 →

/skill coreml-diag

模型无法加载
推理速度慢
内存问题
压缩导致的精度损失
计算单元问题

Speech Work

语音相关工作

Implementation patterns →

/skill speech

SpeechAnalyzer setup (iOS 26+)
SpeechTranscriber configuration
Live transcription
File transcription
Volatile vs finalized results
Model asset management

实现模式 →

/skill speech

SpeechAnalyzer设置（iOS 26+）
SpeechTranscriber配置
实时转录
文件转录
临时结果与最终结果
模型资源管理

Decision Tree

决策树

Implementing / converting ML models? → coreml
CoreML API reference? → coreml-ref
Debugging ML issues (load, inference, compression)? → coreml-diag
Speech-to-text / transcription? → speech

实现/转换机器学习模型？→ coreml
CoreML API参考？→ coreml-ref
调试机器学习问题（加载、推理、压缩）？→ coreml-diag
语音转文本/转录？→ speech

Anti-Rationalization

常见误区纠正

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.

想法	实际情况
"CoreML只需要加载和预测就行"	CoreML包含压缩、有状态模型、计算单元选择和异步预测等功能，coreml技能涵盖所有这些内容。
"我的模型很小，不需要优化"	即使是小模型也能从计算单元选择和异步预测中受益，coreml技能提供了相关实现模式。
"我直接用SFSpeechRecognizer就行"	iOS 26中的SpeechAnalyzer具有更高的准确率和离线支持，speech技能涵盖了这个现代API。

Critical Patterns

关键模式

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

coreml:

模型转换（PyTorch → CoreML）
模型压缩（调色板化、量化、剪枝）
大语言模型的有状态KV缓存
适配器用多功能模型
用于流水线拼接的MLTensor
异步并发预测

coreml-diag:

加载失败与缓存问题
推理性能问题
模型导致的内存压力
压缩导致的精度下降

speech:

SpeechAnalyzer + SpeechTranscriber设置
AssetInventory模型管理
带临时结果的实时转录
音频格式转换

Example Invocations

调用示例

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

用户："如何将PyTorch模型转换为CoreML？" → 调用：

/skill coreml

用户："压缩我的模型以适配iPhone" → 调用：

/skill coreml

用户："为我的语言模型实现KV缓存" → 调用：

/skill coreml

用户："模型首次启动时加载缓慢" → 调用：

/skill coreml-diag

用户："我的压缩模型精度很差" → 调用：

/skill coreml-diag

用户："为我的应用添加实时转录功能" → 调用：

/skill speech

用户："使用SpeechAnalyzer转录音频文件" → 调用：

/skill speech

用户："MLTensor是什么，我该如何使用它？" → 调用：

/skill coreml-ref