Wendy logo

Wendy

Organization
wendylabsinc
wendy

Expert guidance on building and deploying apps to WendyOS edge devices. Use when developers mention: (1) Wendy or WendyOS, (2) wendy CLI commands, (3) wendy.json or entitlements, (4) deploying apps to edge devices, (5) remote debugging Swift on ARM64, (6) NVIDIA Jetson or Raspberry Pi apps, (7) cross-compiling Swift for ARM64.

Overview

Publisherwendylabsinc
Repositoryclaude-skills
Skill namewendy
Stars
62
Forks
5
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by wendylabsinc on GitHub. Read the source before you install it.

Installation

Install the Wendy AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/wendylabsinc/claude-skills/tree/main/wendy
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wendylabsinc/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/wendy .claude/skills/wendy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wendy in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wendy on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wendy is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

WendyOS

WendyOS is an Embedded Linux operating system for edge computing. It supports:

  • NVIDIA Jetson devices (production with OTA updates)
  • Raspberry Pi 4/5 (edge devices)
  • ARM64/AMD64 VMs (development)

Learning About Wendy

Before helping with Wendy commands, run this to learn all available commands:

bash
wendy --experimental-dump-help

This outputs a JSON structure with all commands, flags, and documentation.

Whenever you invoke a wendy command, use the JSON structure options to provide structured JSON output. This will also prevent interactive dialogs and errors. Use --json or -j to provide JSON output.

Common Tasks

  • Run an app: wendy run
  • Create a new project: wendy init
  • Discover devices: wendy discover
  • Update agent: wendy device update
  • Configure WiFi: wendy device wifi connect
  • Install WendyOS on an external drive: wendy os install
  • Set a device as default using wendy device set-default

wendy init — Create a New Wendy Lite Project

Creates a new Wendy Lite project with the required scaffolding:

bash
wendy init

This sets up a new project directory with a wendy.json configuration file and the necessary structure for building and deploying a Wendy Lite app.

wendy run — Run a Wendy Lite Project

Builds, uploads, and runs a Wendy Lite project on a connected device:

bash
wendy run

This command handles the full development cycle: compiling the app, transferring the binary to the device, and starting execution. Use --verbose for detailed build output.

wendy device wifi connect — Set Up WiFi

Configures WiFi credentials on a connected device:

bash
wendy device wifi connect

This sends WiFi SSID and password to the device so it can connect to the local network. The device must be reachable over USB or an existing connection first.

Setup and Configuration

Wendy CLI connects to a device over gRPC (TCP) port 50051. If Wendy CLI is not installed yet, you can use brew install wendy to install it.

Devices are discovered over USB or LAN. If a device is not found, ask the user to check the connection or to connect it over USB. If a device is not yet installed, use wendy os install to install the OS to an external drive. For NVIDIA Jetson devices, the OS is commonly installed to NVMe.

Development

WendyOS is a Linux-based containerized operating system. It uses Linux containers to run your apps.

WendyOS uses Swift.org as its flagship language. This uses Swift Package Manager and the Swift Container Plugin to build and run your app. Wendy CLI will cross compile Swift for you.

Other programming languages are supported, but require the use of a Dockerfile to build your app.

Entitlements

WendyOS uses an entitlement system, managed through wendy.json, to manage permissions for your app. This reflects how your container will be set up on the device.

See references/wendy.json.md for detailed entitlement configuration.

Quick Start

  1. Create a new Swift project or navigate to an existing one
  2. Initialize wendy.json: wendy project init
  3. Add required entitlements (e.g., for a web server): wendy project entitlements add network --mode host
  4. Run on device: wendy run

Common Entitlements

EntitlementUse Case
network (host mode)Web servers, HTTP APIs, incoming connections
gpuML inference, computer vision (Jetson only)
videoCamera access, video capture
audioMicrophone, speakers
bluetoothBLE devices, Bluetooth communication

Remote Debugging

WendyOS provides built-in support for remote debugging Swift apps. Use wendy run --debug to include and launch a debugging session. This exposes a GDB server on port 4242.

Connecting from VS Code

  1. Run wendy run --debug
  2. In VS Code, use the CodeLLDB extension
  3. Connect to <device-ip>:4242

Observability

WendyOS runs a local OpenTelemetry collector on each device. Apps should report telemetry (logs, metrics, traces) to this local collector.

Configuration

Use HTTP protocol (not gRPC) for OTel exports:

swift
import OTel

var config = OTel.Configuration.default
config.traces.otlpExporter.protocol = .httpProtobuf
config.traces.otlpExporter.endpoint = "http://localhost:4318"
config.metrics.otlpExporter.protocol = .httpProtobuf
config.metrics.otlpExporter.endpoint = "http://localhost:4318"
config.logs.otlpExporter.protocol = .httpProtobuf
config.logs.otlpExporter.endpoint = "http://localhost:4318"

let observability = try OTel.bootstrap(configuration: config)

Or via environment variables:

bash
OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf
OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318

The local collector handles forwarding telemetry to your backend infrastructure.

Troubleshooting

ProblemSolution
Device not foundCheck USB/LAN connection, run wendy discover
Network access deniedAdd network entitlement with host mode
GPU not detectedAdd gpu entitlement (Jetson only)
Camera not foundAdd video entitlement, verify camera at /dev/video0
Build failsCheck Swift version compatibility, try wendy run --verbose

Reference Files

Load these files as needed for specific topics:

  • references/wendy.json.md - App configuration, entitlements (network, gpu, video, audio, bluetooth), common configurations, CLI commands

Further Reading

WendyOS documentation at https://wendy.sh/docs/

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Wendy AI skill do?

Expert guidance on building and deploying apps to WendyOS edge devices. Use when developers mention: (1) Wendy or WendyOS, (2) wendy CLI commands, (3) wendy.json or entitlements, (4) deploying apps to edge devices, (5) remote debugging Swift on ARM64, (6) NVIDIA Jetson or Raspberry Pi apps, (7) cross-compiling Swift for ARM64.

Why use Wendy on TypingMind?

Because you install it once and use it with any model. Wendy is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wendy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wendylabsinc/claude-skills/tree/main/wendy. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Wendy?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wendy?

As many as you like. As long as a model supports skills, you can use Wendy with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wendy AI skill free?

It is published on GitHub by wendylabsinc. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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