desktop-computer-automation

name	desktop-computer-automation
description	Vision-driven desktop automation using Midscene. Control your desktop (macOS, Windows, Linux) with natural language commands. Operates entirely from screenshots — no DOM or accessibility labels required. Can interact with all visible elements on screen regardless of technology stack. Triggers: open app, press key, desktop, computer, click on screen, type text, screenshot desktop, launch application, switch window, desktop automation, control computer, mouse click, keyboard shortcut, screen capture, find on screen, read screen, verify window, close app, minimize window, maximize window Powered by Midscene.js (https://midscenejs.com)
allowed-tools	["Bash"]

Desktop Computer Automation

CRITICAL RULES — VIOLATIONS WILL BREAK THE WORKFLOW:

1. Never run midscene commands in the background. Each command must run synchronously so you can read its output (especially screenshots) before deciding the next action. Background execution breaks the screenshot-analyze-act loop.
2. Run only one midscene command at a time. Wait for the previous command to finish, read the screenshot, then decide the next action. Never chain multiple commands together.
3. Allow enough time for each command to complete. Midscene commands involve AI inference and screen interaction, which can take longer than typical shell commands. A typical command needs about 1 minute; complex act commands may need even longer.
4. Always report task results before finishing. After completing the automation task, you MUST proactively summarize the results to the user — including key data found, actions completed, screenshots taken, and any relevant findings. Never silently end after the last automation step; the user expects a complete response in a single interaction.

Control your desktop (macOS, Windows, Linux) using npx @midscene/computer@1. Each CLI command maps directly to an MCP tool — you (the AI agent) act as the brain, deciding which actions to take based on screenshots.

Prerequisites

Midscene requires models with strong visual grounding capabilities. The following environment variables must be configured — either as system environment variables or in a .env file in the current working directory (Midscene loads .env automatically):

MIDSCENE_MODEL_API_KEY="your-api-key"
MIDSCENE_MODEL_NAME="model-name"
MIDSCENE_MODEL_BASE_URL="https://..."
MIDSCENE_MODEL_FAMILY="family-identifier"

Example: Gemini (Gemini-3-Flash)

MIDSCENE_MODEL_API_KEY="your-google-api-key"
MIDSCENE_MODEL_NAME="gemini-3-flash"
MIDSCENE_MODEL_BASE_URL="https://generativelanguage.googleapis.com/v1beta/openai/"
MIDSCENE_MODEL_FAMILY="gemini"

Example: Qwen 3.5

MIDSCENE_MODEL_API_KEY="your-aliyun-api-key"
MIDSCENE_MODEL_NAME="qwen3.5-plus"
MIDSCENE_MODEL_BASE_URL="https://dashscope.aliyuncs.com/compatible-mode/v1"
MIDSCENE_MODEL_FAMILY="qwen3.5"
MIDSCENE_MODEL_REASONING_ENABLED="false"
# If using OpenRouter, set:
# MIDSCENE_MODEL_API_KEY="your-openrouter-api-key"
# MIDSCENE_MODEL_NAME="qwen/qwen3.5-plus"
# MIDSCENE_MODEL_BASE_URL="https://openrouter.ai/api/v1"

Example: Doubao Seed 2.0 Lite

MIDSCENE_MODEL_API_KEY="your-doubao-api-key"
MIDSCENE_MODEL_NAME="doubao-seed-2-0-lite"
MIDSCENE_MODEL_BASE_URL="https://ark.cn-beijing.volces.com/api/v3"
MIDSCENE_MODEL_FAMILY="doubao-seed"

Commonly used models: Doubao Seed 2.0 Lite, Qwen 3.5, Zhipu GLM-4.6V, Gemini-3-Pro, Gemini-3-Flash.

If the model is not configured, ask the user to set it up. See Model Configuration for supported providers.

Commands

Connect to Desktop

npx @midscene/computer@1 connect
npx @midscene/computer@1 connect --displayId <id>

List Displays

npx @midscene/computer@1 list_displays

Take Screenshot

npx @midscene/computer@1 take_screenshot

After taking a screenshot, read the saved image file to understand the current screen state before deciding the next action.

Perform Action

Use act to interact with the computer and get the result. It autonomously handles all UI interactions internally — clicking, typing, scrolling, waiting, and navigating — so you should give it complex, high-level tasks as a whole rather than breaking them into small steps. Describe what you want to do and the desired effect in natural language:

# specific instructions
npx @midscene/computer@1 act --prompt "type hello world in the search field and press Enter"
npx @midscene/computer@1 act --prompt "drag the file icon to the Trash"

# or target-driven instructions
npx @midscene/computer@1 act --prompt "search for the weather in Shanghai using the Chrome browser, tell me the result"

Disconnect

npx @midscene/computer@1 disconnect

Workflow Pattern

Since CLI commands are stateless between invocations, follow this pattern:

1. Connect to establish a session
2. Health check — observe the output of the connect command. If connect already performed a health check (screenshot and mouse movement test), no additional check is needed. If connect did not perform a health check, do one manually: take a screenshot and verify it succeeds, then move the mouse to a random position (act --prompt "move the mouse to a random position") and verify it succeeds. If either step fails, stop and troubleshoot before continuing. Only proceed to the next steps after both checks pass without errors.
3. Launch the target app and take screenshot to see the current state, make sure the app is launched and visible on the screen.
4. Execute action using act to perform the desired action or target-driven instructions.
5. Disconnect when done
6. Report results — summarize what was accomplished, present key findings and data extracted during the task, and list any generated files (screenshots, logs, etc.) with their paths

Best Practices

1. Always run a health check first: After connecting, observe the output of the connect command. If connect already performed a health check (screenshot and mouse movement test), no additional check is needed. If it did not, do one manually: take a screenshot and move the mouse to a random position. Both must succeed (no errors) before proceeding with any further operations. This catches environment issues early.
2. Bring the target app to the foreground before using this skill: For best efficiency, bring the app to the foreground using other means (e.g., open -a <AppName> on macOS, start <AppName> on Windows) before invoking any midscene commands. Then take a screenshot to confirm the app is actually in the foreground. Only after visual confirmation should you proceed with UI automation using this skill. Avoid using Spotlight, Start menu search, or other launcher-based approaches through midscene — they involve transient UI, multiple AI inference steps, and are significantly slower.
3. Be specific about UI elements: Instead of vague descriptions, provide clear, specific details. Say "the red close button in the top-left corner of the Safari window" instead of "the close button".
4. Describe locations when possible: Help target elements by describing their position (e.g., "the icon in the top-right corner of the menu bar", "the third item in the left sidebar").
5. Never run in background: Every midscene command must run synchronously — background execution breaks the screenshot-analyze-act loop.
6. Check for multiple displays: If you launched an app but cannot see it on the screenshot, the app window may have opened on a different display. Use list_displays to check available displays. You have two options: either move the app window to the current display, or use connect --displayId <id> to switch to the display where the app is.
7. Batch related operations into a single act command: When performing consecutive operations within the same app, combine them into one act prompt instead of splitting them into separate commands. For example, "search for X, click the first result, and scroll down to see more details" should be a single act call, not three. This reduces round-trips, avoids unnecessary screenshot-analyze cycles, and is significantly faster.
8. Set up PATH before running (macOS): On macOS, some commands (e.g., ) may not be found if the is incomplete. Before running any midscene commands, ensure the includes the standard system directories: This prevents screenshot failures caused by missing system utilities.

Example — Context menu interaction:

npx @midscene/computer@1 act --prompt "right-click the file icon and select Delete from the context menu"
npx @midscene/computer@1 take_screenshot

Example — Dropdown menu:

npx @midscene/computer@1 act --prompt "open the File menu and click New Window"
npx @midscene/computer@1 take_screenshot

Troubleshooting

macOS: Accessibility Permission Denied

Your terminal app does not have Accessibility access:

1. Open System Settings > Privacy & Security > Accessibility
2. Add your terminal app and enable it
3. Restart your terminal app after granting permission

macOS: Xcode Command Line Tools Not Found

xcode-select --install

API Key Not Set

Check .env file contains MIDSCENE_MODEL_API_KEY=<your-key>.

macOS: Screenshot Fails with system_profiler Not Found

If take_screenshot fails with an error like system_profiler: command not found, the PATH environment variable is likely incomplete. Fix it by running:

export PATH="/usr/sbin:/usr/bin:/bin:/sbin:$PATH"

Then retry the screenshot command.

AI Cannot Find the Element

1. Take a screenshot to verify the element is actually visible
2. Use more specific descriptions (include color, position, surrounding text)
3. Ensure the element is not hidden behind another window