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App Control

OrganizationPopular
vellum-ai
app-control

Drive a specific named macOS app via raw input bypassing the Accessibility tree

Overview

Publishervellum-ai
Repositoryvellum-assistant
Skill nameapp-control
Stars
1.3K
Forks
186
Bundled files
10
LicenseMIT
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.

  • 10 bundled files

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

  • Open source

    Published by vellum-ai on GitHub. Read the source before you install it.

Installation

Install the App Control 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.
  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/vellum-ai/vellum-assistant.git /tmp/vellum-assistant
mkdir -p .claude/skills
cp -r /tmp/vellum-assistant/assistant/src/config/bundled-skills/app-control .claude/skills/app-control
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable App Control 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 App Control 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 App Control 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.

This skill exposes the app_control_* proxy tools for driving a single named macOS application via raw input — keyboard, mouse, screenshot — that bypasses the system Accessibility tree. Use it only when explicitly directed to a specific app where the AX tree is unhelpful (emulators, games, OpenGL canvases, custom-rendered Electron apps). For general macOS UI navigation prefer the computer-use skill.

Tools in this skill are proxy tools — execution is forwarded to the connected macOS client, never handled locally by the assistant.

Cadence

Take 2-3 actions per turn, then yield with a short narration so the user can interject. Do not chain long sequences without surfacing what you are doing.

Always observe before acting

Call app_control_observe before your first input action whenever the screen state matters (e.g. you need to know what is on screen, where a UI element is, or whether the app is even running). Re-observe after actions that may have moved the window or changed visibility.

observe waits a short settle delay (default ~200ms) before capturing so the target app and the WindowServer can flush pending input and composite a fresh frame. If the captured screenshot looks one input behind the latest state (common with emulators or other slow-feedback apps), pass a larger settle_ms. For static UIs where you just want a quick snapshot, pass settle_ms: 0 to skip the wait.

Input choice

  • Prefer app_control_sequence over multiple back-to-back app_control_press calls when sending an ordered batch of presses (e.g. menu navigation, repeated movement). Sequence runs in a single round-trip — the target app is activated once at the start and the keys are sent serially without any window for keyboard focus to drift to another app between presses. Each step may carry its own duration_ms (hold) and gap_ms (pause after).
  • Prefer app_control_combo over rapid sequential app_control_press for simultaneous inputs (e.g. cmd+shift+4). combo holds every key at once; sequential presses interleave key-down and key-up events.
  • Use app_control_type for literal text into a focused field.

Coordinate caveat

app_control_click and app_control_drag use window-relative coordinates. The window may move or resize between observation and click — if you are uncertain whether the window has shifted, re-observe first.

App targeting

Use bundle IDs (e.g. com.example.app) when possible — they are the most reliable identifier. Fall back to localized process names if a bundle ID is unavailable.

Ending the session

Call app_control_stop when you are done. Do not auto-quit the controlled app — stop only ends the app-control session, leaving the app running.

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 App Control AI skill do?

Drive a specific named macOS app via raw input bypassing the Accessibility tree

Why use App Control on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vellum-ai/vellum-assistant/tree/main/assistant/src/config/bundled-skills/app-control. 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 App Control?

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 App Control?

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

Is the App Control AI skill free?

Yes. It is published on GitHub by vellum-ai under the MIT 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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