Develop

AI-Assisted Development

Updated: Mar 12, 2026

Overview

The Wearables Device Access Toolkit provides two levels of AI assistance for developers:
  1. Project-level config — SDK knowledge (setup guides, streaming patterns, testing, debugging) delivered directly to your AI tool via config files in the GitHub repos. This is the primary integration path.
  2. API reference endpoint — The full API surface served via llms.txt⁠ as a supplementary reference for on-demand queries.

Project-level config

The SDK GitHub repos ship config files for four AI coding tools. Each tool gets the same SDK knowledge — setup guides, streaming patterns, MockDeviceKit testing, session lifecycle, permissions, debugging, and sample app guidance — in whatever format it expects.
ToolConfigHow it loads
Claude Code⁠
.claude/skills/*.md
Auto-discovered when you open the project
GitHub Copilot⁠
.github/copilot-instructions.md
Auto-loaded by Copilot in VS Code
Cursor⁠
.cursor/rules/*.mdc
Auto-loaded with glob-based triggers
AGENTS.md⁠
AGENTS.md
Universal — auto-discovered by Codex, Gemini CLI, Devin, Windsurf, Jules, and others
See the dedicated setup guides for each tool: Claude Code, GitHub Copilot, Cursor, AGENTS.md.

Setup

If you cloned the SDK repo, the config is already included — no extra setup needed. Otherwise, use the installer to add it to an existing project:
./install-skills.sh claude    # Claude Code only
./install-skills.sh copilot   # GitHub Copilot only
./install-skills.sh cursor    # Cursor only
./install-skills.sh agents    # AGENTS.md only
./install-skills.sh all       # All tools
Or install everything remotely with a single command:
iOS:
curl -sL https://raw.githubusercontent.com/facebook/meta-wearables-dat-ios/main/install-skills.sh | bash
Android:
curl -sL https://raw.githubusercontent.com/facebook/meta-wearables-dat-android/main/install-skills.sh | bash

What the config covers

All four tools receive the same SDK knowledge, covering:
  • Getting started — Dependencies, project setup, and first integration
  • Session lifecycle — Connecting, reconnecting, and managing device sessions
  • Camera streaming — Resolution selection, frame rates, and photo capture
  • Permissions — Requesting and handling device permissions
  • MockDeviceKit — Testing without physical hardware
  • Debugging — Common issues, Developer Mode, version compatibility
  • Sample app guide — Building a complete DAT app from scratch

API reference endpoint (llms.txt)

As a supplement to the project-level config, the SDK provides an llms.txt⁠ endpoint with the full API surface. The project config covers integration patterns and best practices; the endpoint covers the API reference (classes, methods, parameters, return types).

Endpoints

EndpointDescription
wearables.developer.meta.com/llms.txt⁠
Index of available documentation sections with links
wearables.developer.meta.com/llms.txt?full=true⁠
Full API reference content in a single document
The index endpoint follows the llms.txt specification⁠ and returns a lightweight listing of documentation sections. The ?full=true variant returns the complete API reference inline, which is what most AI tools need to write code.

What’s included

The endpoint serves API reference documentation for both iOS (Swift) and Android (Kotlin) platforms, covering:
  • MWDATCore — App registration, device discovery, session management, and telemetry
  • MWDATCamera — Camera access, resolution and frame rate selection, and photo capture
  • MWDATMockDevice — Simulated device for testing without physical hardware

Tips for effective use

  • Start with the project-level config — Clone the repo or run the installer. The project-level config gives your AI tool the integration patterns, best practices, and debugging guidance it needs for most tasks.
  • Add the API reference when you need specifics — If your AI tool can’t find a particular method signature or parameter type, point it at the llms.txt endpoint for the full API surface.
  • Be specific in your prompts — Mention the platform (iOS or Android) and the module you’re working with (MWDATCore, MWDATCamera, or MWDATMockDevice).
  • Combine with the guides — For deeper integration patterns and lifecycle management, point your AI tool at the integration overview and platform-specific integration guides (iOS, Android).