Token导航 LogoToken导航TokenDH.com
研究检索敏感数据clawhub未标认证来源可访问clear审计提醒

midscene-computer-automation中场计算机自动化

Agent Skill

midscene-computer-automation 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

88,982

周安装

3,565

GitHub Stars

3

下载量

28,805
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:midscene-computer-automation(中场计算机自动化)
来源仓库:https://github.com/quanru/midscene-computer-automation
安装命令:
openclaw skills install midscene-computer-automation
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install midscene-computer-automation

简介

通过视觉识别实现桌面系统自动化操作。midscene-computer-automation 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

  • 支持 macOS、Windows、Linux 平台,无需 DOM 依赖。
  • 使用自然语言命令控制窗口、点击与应用启动。
  • 需授予屏幕录制与应用控制权限方可正常运行。
  • 建议先测试基础指令以确保环境兼容性。

SKILL.md

name
desktop-computer-automation
description
|
Triggers
open app, press key, desktop, computer, click on screen, type text, screenshot desktop,
allowed-tools

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., system_profiler) may not be found if the PATH is incomplete. Before running any midscene commands, ensure the PATH includes the standard system directories:
   export PATH="/usr/sbin:/usr/bin:/bin:/sbin:$PATH"

This prevents screenshot failures caused by missing system utilities.

  1. Always report results after completion: After finishing the automation task, you MUST proactively present the results to the user without waiting for them to ask. This includes: (1) the answer to the user's original question or the outcome of the requested task, (2) key data extracted or observed during execution, (3) screenshots and other generated files with their paths, (4) a brief summary of steps taken. Do NOT silently finish after the last automation command — the user expects complete results in a single interaction.

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

适合场景

01

调用多模型

02

代码和文本生成

03

Agent 推理流程

04

OpenRouter 模型接入

能力概览

能力 1

统一调用多种 LLM

能力 2

支持 Claude、Gemini、Kimi 等模型

能力 3

适合聊天、代码和推理任务

能力 4

可作为 Agent 模型调用入口

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

81.32%
按下载量换算23,424

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills