axiom-ios-ai by charleswiltgen/axiom
npx skills add https://github.com/charleswiltgen/axiom --skill axiom-ios-ai对于任何 Apple Intelligence 或 Foundation Models 相关的工作,您必须使用此技能。
在以下情况时使用此路由器:
首先,确定开发者需要哪种类型的 AI:
| 开发者意图 | 路由至 |
|---|---|
| 设备端文本生成 (Apple Intelligence) | 留在此处 → Foundation Models 技能 |
| 自定义 ML 模型部署 (PyTorch, TensorFlow) | 路由至 ios-ml → CoreML 转换、压缩 |
| 计算机视觉 (图像分析、OCR、分割) | 路由至 ios-vision → Vision 框架 |
| 云 API 集成 (OpenAI 等) | 路由至 ios-networking → URLSession 模式 |
| 系统 AI 功能 (写作工具、Genmoji) | 无需自定义代码 —— 这些是系统提供的功能 |
关键边界:ios-ai 与 ios-ml
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触达数万 AI 开发者,精准高效
Foundation Models + 并发 (会话阻塞主线程,UI 冻结):
await 或在 @MainActor 上运行Foundation Models + 数据 (@Generable 解码错误,结构化输出问题):
实现模式 → /skill axiom-foundation-models
API 参考 → /skill axiom-foundation-models-ref
诊断 → /skill axiom-foundation-models-diag
| 想法 | 现实 |
|---|---|
| "Foundation Models 只是 LanguageModelSession" | Foundation Models 包含 @Generable、工具协议、流式传输和护栏。foundation-models 涵盖了所有内容。 |
| "我会边做边想 AI 模式" | AI API 有特定的错误处理和回退要求。foundation-models 可防止运行时故障。 |
| "我以前用过 LLM,这很相似" | Apple 的设备端模型有独特的约束 (护栏、上下文限制)。foundation-models 是 Apple 特有的。 |
foundation-models :
foundation-models-diag :
用户:"如何使用 Apple Intelligence 生成结构化数据?" → 调用:/skill axiom-foundation-models
用户:"我的 AI 生成被阻止了" → 调用:/skill axiom-foundation-models-diag
用户:"给我看 @Generable 示例" → 调用:/skill axiom-foundation-models-ref
用户:"实现流式 AI 生成" → 调用:/skill axiom-foundation-models
用户:"我想给我的应用添加 AI" → 首先询问:Apple Intelligence (Foundation Models) 还是自定义 ML 模型?据此路由。
用户:"我的 Foundation Models 会话正在阻塞 UI" → 调用:/skill axiom-foundation-models (异步模式) + 如果需要则同时调用 ios-concurrency
用户:"我想在设备上运行我的 PyTorch 模型" → 路由至:ios-ml 路由器 (CoreML 转换,非 Foundation Models)
每周安装数
96
仓库
GitHub 星标数
601
首次出现
2026年1月21日
安全审计
安装于
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You MUST use this skill for ANY Apple Intelligence or Foundation Models work.
Use this router when:
First, determine which kind of AI the developer needs:
| Developer Intent | Route To |
|---|---|
| On-device text generation (Apple Intelligence) | Stay here → Foundation Models skills |
| Custom ML model deployment (PyTorch, TensorFlow) | Route to ios-ml → CoreML conversion, compression |
| Computer vision (image analysis, OCR, segmentation) | Route to ios-vision → Vision framework |
| Cloud API integration (OpenAI, etc.) | Route to ios-networking → URLSession patterns |
| System AI features (Writing Tools, Genmoji) | No custom code needed — these are system-provided |
Key boundary: ios-ai vs ios-ml
Foundation Models + concurrency (session blocking main thread, UI freezes):
await or running on @MainActorFoundation Models + data (@Generable decoding errors, structured output issues):
Implementation patterns → /skill axiom-foundation-models
API reference → /skill axiom-foundation-models-ref
Diagnostics → /skill axiom-foundation-models-diag
| Thought | Reality |
|---|---|
| "Foundation Models is just LanguageModelSession" | Foundation Models has @Generable, Tool protocol, streaming, and guardrails. foundation-models covers all. |
| "I'll figure out the AI patterns as I go" | AI APIs have specific error handling and fallback requirements. foundation-models prevents runtime failures. |
| "I've used LLMs before, this is similar" | Apple's on-device models have unique constraints (guardrails, context limits). foundation-models is Apple-specific. |
foundation-models :
foundation-models-diag :
User: "How do I use Apple Intelligence to generate structured data?" → Invoke: /skill axiom-foundation-models
User: "My AI generation is being blocked" → Invoke: /skill axiom-foundation-models-diag
User: "Show me @Generable examples" → Invoke: /skill axiom-foundation-models-ref
User: "Implement streaming AI generation" → Invoke: /skill axiom-foundation-models
User: "I want to add AI to my app" → First ask: Apple Intelligence (Foundation Models) or custom ML model? Route accordingly.
User: "My Foundation Models session is blocking the UI" → Invoke: /skill axiom-foundation-models (async patterns) + also invoke ios-concurrency if needed
User: "I want to run my PyTorch model on device" → Route to: ios-ml router (CoreML conversion, not Foundation Models)
Weekly Installs
96
Repository
GitHub Stars
601
First Seen
Jan 21, 2026
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Installed on
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