AI-Assisted Development

Updated: Jun 22, 2026

Overview

The Wearables Device Access Toolkit provides three levels of AI assistance for developers:

Git-native tool integrations — SDK knowledge delivered directly from the DAT GitHub repos through Claude marketplace/plugin installs, Codex plugins, and repo-native tool files.

Live docs lookup — The shared Wearables MCP server exposes search_dat_docs so your AI tool can search current DAT guides while it works. When your MCP client supports response formats, ask for Markdown output so headings, links, and code blocks stay intact.

API reference endpoint — The full API surface served via llms.txt⁠ as a supplementary reference for on-demand queries.

For implementation work, use the GitHub-hosted DAT plugin or repo-native skills for coding patterns, then use MCP for current docs lookup. The plugin teaches the assistant how DAT projects are structured; MCP keeps the assistant grounded in the latest published guides.

Start by copying one of the prompts below into an AI coding tool. Each prompt includes the GitHub repo, install options, MCP endpoint, MCP tool, and docs links so a fresh AI session knows where to start.

Copy a starter prompt

Use these prompts to get started quickly. They are written to make the assistant install or use DAT’s GitHub-hosted plugin or skills when available, add MCP for live docs lookup, inspect your project first, use search_dat_docs for current docs, request Markdown-formatted results when available, and keep the first code change small.

iOS DAT integration

You are helping me build a Wearables Device Access Toolkit iOS integration. Assume you know nothing about this project yet.

Use these public sources before choosing APIs or editing code:
- DAT iOS GitHub repo: https://github.com/facebook/meta-wearables-dat-ios
- DAT AI overview: https://wearables.developer.meta.com/docs/develop/dat/ai-assisted/
- iOS integration guide: https://wearables.developer.meta.com/docs/develop/dat/build-integration-ios/
- DAT MCP setup: https://wearables.developer.meta.com/docs/develop/dat/ai-assisted-mcp/
- Wearables MCP endpoint: https://mcp.developer.meta.com/wearables
- MCP tool to call: search_dat_docs
- Preferred MCP result format: Markdown

If DAT guidance or live docs lookup is not installed in this AI tool yet, tell me to install the matching GitHub integration and add the matching MCP config before continuing:
- Claude Code plugin:
  claude plugin marketplace add facebook/meta-wearables-dat-ios
  claude plugin install mwdat-ios@mwdat-ios-marketplace
- Codex plugin:
  codex plugin marketplace add facebook/meta-wearables-dat-ios
  codex plugin add mwdat-ios@mwdat-ios-marketplace
- Repo-native fallback:
  curl -sL https://raw.githubusercontent.com/facebook/meta-wearables-dat-ios/main/install-skills.sh | bash -s -- agents
- MCP for Claude Code:
  claude mcp add --transport http wearables https://mcp.developer.meta.com/wearables
- MCP for Codex:
  codex mcp add wearables --url https://mcp.developer.meta.com/wearables

Using the Wearables MCP endpoint https://mcp.developer.meta.com/wearables, first call search_dat_docs for current iOS setup, registration, permissions, session lifecycle, MockDeviceKit, and camera guidance. Request Markdown-formatted results if the MCP client exposes an output format option.

Inspect this repository and identify the smallest working iOS integration slice for Wearables Device Access Toolkit. Add dependency setup, app registration, `Wearables.configure()`, and connection lifecycle code first, using the existing app structure. Call out any required Info.plist keys, bundle identifier assumptions, and local verification steps. Implement the smallest safe change and run the relevant local checks.

Android DAT integration

You are helping me build a Wearables Device Access Toolkit Android integration. Assume you know nothing about this project yet.

Use these public sources before choosing APIs or editing code:
- DAT Android GitHub repo: https://github.com/facebook/meta-wearables-dat-android
- DAT AI overview: https://wearables.developer.meta.com/docs/develop/dat/ai-assisted/
- Android integration guide: https://wearables.developer.meta.com/docs/develop/dat/build-integration-android/
- DAT MCP setup: https://wearables.developer.meta.com/docs/develop/dat/ai-assisted-mcp/
- Wearables MCP endpoint: https://mcp.developer.meta.com/wearables
- MCP tool to call: search_dat_docs
- Preferred MCP result format: Markdown

If DAT guidance or live docs lookup is not installed in this AI tool yet, tell me to install the matching GitHub integration and add the matching MCP config before continuing:
- Claude Code plugin:
  claude plugin marketplace add facebook/meta-wearables-dat-android
  claude plugin install mwdat-android@mwdat-android-marketplace
- Codex plugin:
  codex plugin marketplace add facebook/meta-wearables-dat-android
  codex plugin add mwdat-android@mwdat-android-marketplace
- Repo-native fallback:
  curl -sL https://raw.githubusercontent.com/facebook/meta-wearables-dat-android/main/install-skills.sh | bash -s -- agents
- MCP for Claude Code:
  claude mcp add --transport http wearables https://mcp.developer.meta.com/wearables
- MCP for Codex:
  codex mcp add wearables --url https://mcp.developer.meta.com/wearables

Using the Wearables MCP endpoint https://mcp.developer.meta.com/wearables, first call search_dat_docs for current Android setup, registration, permissions, session lifecycle, MockDeviceKit, and camera guidance. Request Markdown-formatted results if the MCP client exposes an output format option.

Inspect this repository and identify the smallest working Android integration slice for Wearables Device Access Toolkit. Add Gradle dependency setup, manifest setup, app registration, `Wearables.initialize(...)`, and connection lifecycle code first, using the existing app structure. Call out any required manifest metadata, permission assumptions, and local verification steps. Implement the smallest safe change and run the relevant local checks.

Mock Device Kit or camera validation

You are helping me validate Wearables Device Access Toolkit camera or MockDeviceKit behavior. Assume you know nothing about this project yet.

Use these public sources before choosing APIs or editing code:
- DAT iOS GitHub repo: https://github.com/facebook/meta-wearables-dat-ios
- DAT Android GitHub repo: https://github.com/facebook/meta-wearables-dat-android
- DAT AI overview: https://wearables.developer.meta.com/docs/develop/dat/ai-assisted/
- iOS integration guide: https://wearables.developer.meta.com/docs/develop/dat/build-integration-ios/
- Android integration guide: https://wearables.developer.meta.com/docs/develop/dat/build-integration-android/
- DAT MCP setup: https://wearables.developer.meta.com/docs/develop/dat/ai-assisted-mcp/
- Wearables MCP endpoint: https://mcp.developer.meta.com/wearables
- MCP tool to call: search_dat_docs
- Preferred MCP result format: Markdown

If DAT guidance or live docs lookup is not installed in this AI tool yet, identify whether this project is iOS or Android, tell me the matching install commands from the DAT GitHub repo, and tell me to add the Wearables MCP server with the command that matches my AI tool:
  claude mcp add --transport http wearables https://mcp.developer.meta.com/wearables
  codex mcp add wearables --url https://mcp.developer.meta.com/wearables

Using the Wearables MCP endpoint https://mcp.developer.meta.com/wearables, first call search_dat_docs for current MockDeviceKit, camera streaming, permissions, and session lifecycle guidance. Request Markdown-formatted results if the MCP client exposes an output format option.

Inspect this repository and add the smallest validation path for DAT camera or Mock Device Kit behavior. Prefer a local test or manual check that does not require physical glasses when possible. Keep the implementation scoped to one platform first, document what is not covered, and run the relevant local checks.

Git-native tool integrations

The SDK GitHub repos ship the same public DAT guidance in several formats. Each tool gets the same core knowledge — setup guides, streaming patterns, MockDeviceKit testing, session lifecycle, permissions, debugging, and sample app guidance — in whatever format it expects.

Tool	Public artifact	Recommended setup
Claude Code⁠	`.claude-plugin/marketplace.json` + plugin manifests	Add the DAT GitHub repo as a marketplace, then install the DAT plugin
Codex	`.codex-plugin/plugin.json` inside `plugins/`	Add the DAT GitHub repo as a marketplace, then add the DAT plugin
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`	Portable fallback for tools that read `AGENTS.md`

See the dedicated setup guides for each tool: Claude Code, Codex, GitHub Copilot, Cursor, AGENTS.md.

Setup

For Claude Code and Codex, start from the DAT GitHub repo for your platform:

claude plugin marketplace add facebook/meta-wearables-dat-ios
claude plugin install mwdat-ios@mwdat-ios-marketplace

codex plugin marketplace add facebook/meta-wearables-dat-ios
codex plugin add mwdat-ios@mwdat-ios-marketplace

Android uses the same flow with facebook/meta-wearables-dat-android and mwdat-android@mwdat-android-marketplace.

For tools that use repo-native checked-in files instead of plugin manifests, use:

./install-skills.sh copilot
./install-skills.sh cursor
./install-skills.sh agents

What the config covers

All surfaces 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 GitHub-hosted integrations, the SDK provides an llms.txt⁠ endpoint with the full API surface. The GitHub integrations cover patterns and best practices; the endpoint covers the API reference.

Endpoints

Endpoint	Description
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 GitHub-hosted integration — Use the Claude marketplace or Codex plugin when your tool supports it. Use the repo-native file installs for Copilot, Cursor, or AGENTS.md readers. This gives the assistant DAT-specific coding patterns, setup flow, and test guidance.

Add MCP for live docs lookup — Add the DAT MCP server and tell your AI tool to use the Wearables MCP endpoint https://mcp.developer.meta.com/wearables to call search_dat_docs before choosing SDK APIs. MCP is the live-docs layer; it should complement the GitHub plugin or skills rather than replace them. Request Markdown-formatted results when your MCP client supports response formats.

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).

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