Drive Desktop App logo

Drive Desktop App

OrganizationPopular
reticlehq
drive-desktop-app

Drive and verify an Electron or Tauri desktop app from the inside, including the main-process and Rust IPC calls a browser tool cannot see. Use when a desktop app needs testing, when a feature works in the browser but not in the packaged app, when an IPC or invoke call needs proving, when a desktop screenshot or visual diff is wanted, or when you need a headless run of a desktop UI in CI.

Overview

Publisherreticlehq
Repositoryreticle
Skill namedrive-desktop-app
Stars
1.2K
Forks
176
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Drive Desktop App 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/reticlehq/reticle/tree/main/skills/drive-desktop-app
  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/reticlehq/reticle.git /tmp/reticle
mkdir -p .claude/skills
cp -r /tmp/reticle/skills/drive-desktop-app .claude/skills/drive-desktop-app
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Drive Desktop App 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 Drive Desktop App 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 Drive Desktop App 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.

Drive a desktop app and prove what happened

A desktop app reaches its backend over IPC, not HTTP. Patching fetch/XHR cannot see that, so a browser-shaped tool is blind to every backend call the app makes: the network log reads empty, an action has no in-flight request to settle on, and asserting on the network is vacuously true. That is a false green by construction.

Reticle observes the renderer and the IPC boundary, so a desktop verdict means what a web one does. Not installed? RETICLE_INSTALL_SOURCE=npx_skill npx @reticlehq/server@latest init, then the install-and-verify skill.

Electron: two lines, none in your app code

ts
// vite.config.ts — desktop:true lets connect() start in a packaged renderer (NODE_ENV=production).
// A default (production-mode) `vite build` ships no Reticle code; to drive a packaged
// renderer with no dev server, build it with `vite build --mode development`
export default defineConfig({
  base: './', // file:// needs relative asset paths
  plugins: [react(), reticle({ desktop: true })],
});
js
// electron/preload.cjs — FIRST line. This is what makes main-process IPC visible.
require('@reticlehq/electron/preload');

It must be in the preload and it must be first. contextBridge.exposeInMainWorld hands the renderer a deeply frozen object, so nothing in the page can instrument it afterwards. The preload is the last point where ipcRenderer.invoke is still writable, and the shim has to run before your preload captures its own reference.

A sandboxed preload cannot resolve node_modules, so the bare require fails. Either bundle the preload (electron-vite and Forge do by default) or set sandbox: false.

Tauri: the CSP step is required and its failure is silent

The frontend is the same as any web app. The part people miss is that Tauri's default CSP blocks the bridge WebSocket before it opens, so the app runs perfectly and simply never connects:

json
{
  "app": {
    "security": {
      "csp": "default-src 'self' ipc: http://ipc.localhost; connect-src 'self' ipc: http://ipc.localhost ws://localhost:4400 ws://127.0.0.1:4400"
    }
  }
}

Keep ipc: http://ipc.localhost: Tauri v2 needs it for invoke itself. Dev-only; drop the ws:// entries from your release config.

IPC observation needs nothing on the Rust side: an invoke('load_todos') already reaches Reticle as ipc://load_todos. The reticle-tauri crate is only for screenshots and headless, and it is versioned independently of the npm packages.

Also: use a hash router. A packaged renderer is served from file://, where history-based routing does not resolve.

Verify

Same loop as the web, with IPC in the predicates:

reticle_act_and_wait({ sessionId, ref, action: "click", until: { kind: "allOf", predicates: [
  { kind: "net",     urlContains: "ipc://todos:archive", status: 200 },
  { kind: "element", query: { testid: "..." } },
  { kind: "console", level: "error", absent: true },
]}})

IPC has no status code. 200/500 are synthetic, mapped from whether the command succeeded, precisely so the same predicates keep working. On Tauri you will see status: 500 next to statusText: "OK". That is not a bug: the transport answered fine and the 500 is the command's own verdict. ok is authoritative.

reticle_look { action: "state" } reads the live store exactly as on the web. reticle_screenshot and reticle_visual_diff work once the platform's capture step is wired. Electron needs nothing extra; Tauri needs the crate. Headless on Tauri is RETICLE_HEADLESS=1, and screenshots keep working because the capture renders the webview rather than the screen.

What a missing observer looks like

A missing Electron preload is declared, not silent: verdicts come back with coverage: partial naming the line you did not add, instead of reading clean over a blind spot. If you see that, add the preload line before trusting anything.

If IPC calls never appear while the app works fine: on Electron, the shim's require is not first. On Tauri, invoke from @tauri-apps/api/core is observed, but a hand-rolled postMessage protocol is not.

Honesty

unknown is not a pass on the desktop either. And do not weaken an IPC assertion to make a red verdict green: a desktop false green is the exact failure this wiring exists to remove.


Full desktop reference: curl https://docs.reticle.sh/desktop.md. Everything else: curl https://docs.reticle.sh/llms.txt.

Frequently asked questions

What does the Drive Desktop App AI skill do?

Drive and verify an Electron or Tauri desktop app from the inside, including the main-process and Rust IPC calls a browser tool cannot see. Use when a desktop app needs testing, when a feature works in the browser but not in the packaged app, when an IPC or invoke call needs proving, when a desktop screenshot or visual diff is wanted, or when you need a headless run of a desktop UI in CI.

Why use Drive Desktop App on TypingMind?

Because you install it once and use it with any model. Drive Desktop App 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 Drive Desktop App in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/reticlehq/reticle/tree/main/skills/drive-desktop-app. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Drive Desktop App?

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 Drive Desktop App?

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

Is the Drive Desktop App AI skill free?

Yes. It is published on GitHub by reticlehq under the Apache-2.0 license. 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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