docs(skills): add library consumer skills

README.md

@@ -27,6 +27,8 @@
 
 The library ships with a curated set of pre-exported models covering computer vision, language models, text-to-speech, transcription, and more — all available in our [Hugging Face collection](https://huggingface.co/software-mansion/collections) and ready to use out of the box. You can also bring your own `.pte` models and plug them into existing pipelines or build entirely custom ones from scratch.
 
+To export your own PyTorch models to `.pte`, use [ExecuTorch Export Recipes](https://github.com/software-mansion-labs/executorch-export-recipes), our export harness with model recipes and agent skills.
+
 To explore all on-device capabilities in an interactive showcase app, check out the [React Native ExecuTorch Gallery](https://github.com/software-mansion-labs/react-native-executorch-gallery).
 
 [![npm version](https://img.shields.io/npm/v/react-native-executorch?color=00008B)](https://www.npmjs.com/package/react-native-executorch)
@@ -54,6 +56,19 @@ To explore all on-device capabilities in an interactive showcase app, check out
 
 ## Quickstart
 
+> [!TIP]
+> **Using an AI coding agent?** The consumer skill helps your agent choose ready-made models and task
+> hooks, integrate a custom `.pte`, or build a custom TypeScript pipeline.
+>
+> Install the consumer skill from the Software Mansion skills repository:
+>
+> ```bash
+> npx skills add software-mansion-labs/skills -s react-native-executorch
+> ```
+>
+> See [AI Tools](https://docs.swmansion.com/react-native-executorch/docs/next/fundamentals/ai-tools)
+> for model-selection guidance and an example prompt.
+
 ### 1. Installation
 
 Install `react-native-executorch` alongside its required peer dependencies:
@@ -70,8 +85,8 @@ pnpm add react-native-executorch react-native-worklets react-native-blob-util
 > React Native ExecuTorch requires:
 > - **New Architecture** enabled
 > - **React Native 0.83+** or **Expo SDK 55+** with [Development Builds](https://docs.expo.dev/develop/development-builds/introduction/) (**Expo Go is not supported** due to custom C++ native libraries)
-> - **`react-native-worklets` 0.10 or newer** (`>=0.10.0 <0.13.0`)
-> - **iOS 17.0+** / **Android 13+** (`minSdkVersion` >= 26)
+> - **`react-native-worklets` 0.10 or newer** (`>=0.10.0 <0.14.0`)
+> - **iOS 17.0+** / **Android 8.0+** (`minSdkVersion` >= 26)
 > - **`react-native-worklets/plugin`** as the last entry in your Babel `plugins`
 >
 > Downloads of our pre-exported models send an anonymous download event; call `setTelemetryEnabled(false)` to opt out.
@@ -112,6 +127,10 @@ export function App() {
 
 ## Documentation
 
+**Building with an AI coding agent?** See [AI Tools](https://docs.swmansion.com/react-native-executorch/docs/next/fundamentals/ai-tools)
+for model-selection guidance and an example prompt.
+Choose a pre-exported model with a task hook, or bring your own `.pte` and build a custom TypeScript pipeline.
+
 Full documentation, guides, architecture deep dives, and API references are available at:
 **[docs.swmansion.com/react-native-executorch](https://docs.swmansion.com/react-native-executorch/)**
 

docs/docs/01-fundamentals/01-getting-started.md

@@ -16,6 +16,8 @@ keywords:
 description: 'Get started with React Native ExecuTorch — high-performance, privacy-first on-device AI inference for React Native.'
 ---
 
+<!-- cspell:ignore Podfile -->
+
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
@@ -33,6 +35,19 @@ computer vision, language models, text-to-speech, transcription, and more — al
 and ready to use out of the box. You can also bring your own models and
 plug them into existing pipelines or build entirely custom ones from scratch.
 
+:::tip Using an AI coding agent?
+The consumer skill helps your agent choose ready-made models and task hooks, integrate a custom
+`.pte`, or build a custom TypeScript pipeline.
+
+Install the consumer skill from the Software Mansion skills repository:
+
+```bash
+npx skills add software-mansion-labs/skills -s react-native-executorch
+```
+
+See [AI Tools](./05-ai-tools.md) for model-selection guidance and an example prompt.
+:::
+
 ## What is ExecuTorch?
 
 [ExecuTorch](https://executorch.ai) is a PyTorch Core project — Meta's on-device
@@ -86,7 +101,7 @@ React Native ExecuTorch requires:
 - **New Architecture** enabled
 - **React Native 0.83+** or **Expo SDK 55+** with [Development Builds](https://docs.expo.dev/develop/development-builds/introduction/) (**Expo Go is not supported** due to custom C++ native libraries)
 - **`react-native-worklets` 0.10 or newer** (`>=0.10.0 <0.14.0`)
-- **iOS 17.0+** / **Android 13+** (`minSdkVersion` >= 26); a bare app's
+- **iOS 17.0+** / **Android 8.0+** (`minSdkVersion` >= 26); a bare app's
   `Podfile` needs `platform :ios, '17.0'`
 - **`tar` on `PATH`** — the install-time download extracts with it; macOS, Linux
   and Windows 10 1803+ all ship one. Windows and Linux hosts can build for

docs/docs/01-fundamentals/05-ai-tools.md

@@ -0,0 +1,51 @@
+---
+title: AI Tools
+slug: /fundamentals/ai-tools
+description: 'Use React Native ExecuTorch with a coding agent: choose ready-to-use models and hooks, bring your own .pte, and retrieve documentation for the installed version.'
+---
+
+# AI Tools
+
+React Native ExecuTorch gives your coding agent two starting points:
+
+- **Use a ready-made task.** Choose a [pre-exported model](./04-supported-models.md) and a task hook
+  for chat, vision, speech or embeddings. The task handles model loading, preprocessing, inference
+  and postprocessing; the hook manages downloads and React lifecycle.
+- **Bring your own model.** [Export a compatible `.pte`](../03-core-and-advanced/07-exporting-custom-models.md),
+  inspect its schema, and reuse a built-in task if its contract fits. Otherwise build a custom
+  TypeScript pipeline using [models and tensors](../03-core-and-advanced/02-models-and-tensors.md),
+  [schema validation](../03-core-and-advanced/03-schema-validation.md) and
+  [worklets](../03-core-and-advanced/06-worklets-and-threading.md).
+
+These paths use the same runtime. You can start with a shipped model and replace it with your own
+without rewriting the application when the input/output contract stays compatible.
+
+## Consumer skill
+
+The consumer skill teaches an agent how to choose between these paths, prepare a React Native app,
+handle native resources, and verify model behavior. Install it from the
+[Software Mansion skills repository](https://github.com/software-mansion-labs/skills).
+
+```bash
+npx skills add software-mansion-labs/skills -s react-native-executorch
+```
+
+The skill is optional; the library and its documentation work independently of skill installation.
+
+For existing apps, use documentation matching the installed react-native-executorch version.
+
+## A prompt to start with
+
+> Create a new React Native app using the latest stable react-native-executorch release and its
+> matching documentation. Help me build [describe the feature]. Choose a pre-exported model and task
+> hook if one fits; otherwise use
+> [ExecuTorch Export Recipes](https://github.com/software-mansion-labs/executorch-export-recipes)
+> to export my custom model and build the mobile pipeline. Configure the required native dependencies
+> and verify inference on the target device.
+
+For a custom model, provide its PyTorch definition or exported `.pte`, sample inputs and expected
+outputs, preprocessing/tokenizer assets, and target devices. Use
+[ExecuTorch Export Recipes](https://github.com/software-mansion-labs/executorch-export-recipes) and its
+agent skills for the export step, then follow
+[Exporting Custom Models](../03-core-and-advanced/07-exporting-custom-models.md) to integrate and verify
+the result in your app.

docs/docs/03-core-and-advanced/07-exporting-custom-models.md

@@ -16,30 +16,60 @@ keywords:
 
 # Exporting Custom Models
 
-React Native ExecuTorch runs any ExecuTorch `.pte` file, not just the models in
+React Native ExecuTorch runs compatible ExecuTorch `.pte` files, including models outside
 our [HuggingFace collection](https://huggingface.co/software-mansion/collections).
 Getting your own model running takes two things:
 
-1. **Export it to `.pte`** with a hardware backend — the standard ExecuTorch
-   workflow.
+1. **Export it to `.pte`** for your target backend using
+   [ExecuTorch Export Recipes](https://github.com/software-mansion-labs/executorch-export-recipes).
 2. **Integrate it** — either feed it into a built-in pipeline by matching that
    pipeline's contract, or build your own pipeline around it, where you define the
    contract. Both come down to the model's schema.
 
-The export step is plain ExecuTorch and is documented thoroughly upstream, so this
-page links out for it and focuses on the second part — integrating a custom model,
-into a built-in pipeline or one you build yourself — which is where the
-library-specific work lives.
+The exporter provides model recipes, pinned environments and agent skills for the
+PyTorch → ExecuTorch step. This guide connects that workflow to integrating the
+result in a built-in pipeline or one you build yourself.
+
+## Choose your integration level
+
+| Starting point                                                             | Integration                                                                         |
+| -------------------------------------------------------------------------- | ----------------------------------------------------------------------------------- |
+| A supported task and a model from the catalog fit your goal                | Use a [pre-exported model and task hook](../01-fundamentals/04-supported-models.md) |
+| Your own model matches a built-in task's schema and preprocessing contract | Supply a custom configuration to that task's hook or factory                        |
+| Your model needs different inputs, outputs or orchestration                | Build a custom TypeScript pipeline on the model, tensor and worklet primitives      |
+
+You can mix these approaches in one application. A custom model does not have to be contributed to the
+library registry, and a custom pipeline generally does not require app-specific C++.
+
+## Keep export and mobile integration in agreement
+
+Carry these artifacts from PyTorch through export to the app:
+
+- The `.pte` and every required tokenizer, vocabulary, label map or other auxiliary asset.
+- Exported method names, ordered inputs/outputs, data types, layouts, shapes and dynamic bounds.
+- Preprocessing and output-decoding rules, such as normalization, image resize/padding or sample rate.
+- The export/runtime version, backend requirements, and representative inputs with reference outputs.
+
+Use an ExecuTorch export version and operators compatible with the native runtime shipped by the
+installed React Native ExecuTorch package. Match its enabled backends and target hardware; a `.pte`
+extension or successful schema inspection alone does not establish execution compatibility. See
+[Native Libraries](./08-native-libraries.md) for app backend selection.
+
+Preserve that contract when exporting. Compare exported outputs against the PyTorch reference before
+moving to mobile, with tolerances appropriate to the selected precision/backend.
 
 ## Exporting to `.pte`
 
-Producing the `.pte` is the standard PyTorch → ExecuTorch path:
-[`torch.export`](https://docs.pytorch.org/docs/2.14/user_guide/torch_compiler/export.html),
-then lowering to a backend (XNNPACK, Core ML, Vulkan, …) with
-[`to_edge_transform_and_lower`](https://docs.pytorch.org/executorch/stable/export-to-executorch-api-reference.html#executorch.exir.to_edge_transform_and_lower).
-It is not specific to this library, so follow the official
-[ExecuTorch documentation](https://docs.pytorch.org/executorch/stable/index.html)
-for the details, including which backend to target for your model and platform.
+Use [ExecuTorch Export Recipes](https://github.com/software-mansion-labs/executorch-export-recipes),
+our export harness, model recipes and agent skills. Its
+[README](https://github.com/software-mansion-labs/executorch-export-recipes/blob/docs/public-repo-foundations/README.md)
+covers setup and exporting; its repository instructions and skills guide agents through adding recipes,
+choosing a backend and validating outputs. Follow those instructions for your model and target device,
+and check compatibility with the native runtime shipped by your installed React Native ExecuTorch package.
+
+For the underlying PyTorch → ExecuTorch APIs, see the official
+[ExecuTorch documentation](https://docs.pytorch.org/executorch/stable/index.html).
+Once validated, bring the `.pte` and the assets listed above into your React Native app.
 
 What matters here is the **contract** the resulting `.pte` exposes: the input and
 output shapes, data types, and any dynamic dimensions or dimension relationships.
@@ -359,6 +389,10 @@ Before wiring a custom model into a screen, confirm two things on the device:
 
 Once both check out, load the model into the target pipeline and run it.
 
+Compare a known inference result with the PyTorch/exported reference on the actual target backend.
+Also verify preprocessing, repeated calls, cancellation where supported, and resource cleanup. Export,
+inspection, a TypeScript build and device inference establish different parts of the workflow.
+
 ## Where to go next
 
 - [Schema Validation](./03-schema-validation.md) — the contract a custom model must satisfy, in depth.

docs/docs/05-other/01-compatibility.mdx

@@ -138,7 +138,7 @@ it.
 On SDK 55 and 56, install both:
 
 ```bash
-npm install react-native-worklets@^0.10.0 react-native-reanimated@^4.5.0
+npm install react-native-worklets@^0.10.0 react-native-reanimated@~4.5.0
 ```
 
 SDK 54 is React Native 0.81, below what worklets 0.10 accepts, so no