Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add malue-ai/dazee-small --skill claude-computer-usegit clone --depth 1 https://github.com/malue-ai/dazee-smallWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/malue-ai/dazee-small/claude-computer-use)<a href="https://agentmods.dev/skills/malue-ai/dazee-small/claude-computer-use"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/claude-computer-use.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.02350 |
| Opus 5 | $0.00034 | $0.01175 |
| Sonnet 5 | $0.00013 | $0.00470 |
| Haiku 4.5 | $0.00007 | $0.00235 |
Grade A, and why
claude-computer-use scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(["screencapture", "-x", "/tmp/screen.png"]) How it starts
The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claude Computer Use
Anthropic 官方桌面自动化 API(beta)。通过截屏 + Claude 视觉理解 + 坐标操作控制任意桌面应用。
何时使用
这是终极后备方案,仅在以下情况使用:
- peekaboo (macOS) / pywinauto (Windows) / browser (网页) 无法操作目标应用
- 需要视觉判断(看屏幕内容决定下一步)
- 目标应用不暴露 Accessibility API
- 用户明确要求精确坐标控制
优先使用轻量方案(成本更低、速度更快):
- 网页 → browser tool
- macOS 桌面 → peekaboo
- Windows 桌面 → pywinauto
支持的操作
| 操作 | 说明 |
|---|---|
| screenshot | 截取当前屏幕 |
| left_click | 在坐标 [x, y] 点击 |
| right_click | 右键点击 |
| double_click | 双击 |
| triple_click | 三击(全选文本) |
| middle_click | 中键点击 |
| mouse_move | 移动鼠标到坐标 |
| left_click_drag | 拖拽(从当前位置到目标坐标) |
| scroll | 滚动(方向 + 数量) |
| type | 输入文本 |
| key | 按键/组合键(如 "ctrl+s") |
| hold_key | 按住某键指定时长 |
| wait | 等待指定秒数 |
工作流程
循环:
1. 截屏 (screencapture / pyautogui.screenshot)
2. 发送截图给 Claude Computer Use API
3. Claude 分析截图,返回 tool_use (action + coordinates)
4. 在本地执行操作 (cliclick/xdotool/pyautogui)
5. 如果任务未完成,回到步骤 1
完整示例
macOS 实现
import anthropic
import subprocess
import base64
import json
client = anthropic.Anthropic()
def screenshot():
"""截取 macOS 屏幕"""
subprocess.run(["screencapture", "-x", "/tmp/screen.png"], check=True)
with open("/tmp/screen.png", "rb") as f:
return base64.b64encode(f.read()).decode()
def execute_action(action_type, **kwargs):
"""执行鼠标/键盘操作 (macOS 使用 cliclick 或 AppleScript)"""
if action_type == "left_click":
x, y = kwargs["coordinate"]
subprocess.run(["cliclick", f"c:{x},{y}"])
elif action_type == "type":
text = kwargs["text"]
subprocess.run(["cliclick", f"t:{text}"])
elif action_type == "key":
key = kwargs["key"]
# 通过 AppleScript 发送按键
subprocess.run(["osascript", "-e",
f'tell application "System Events" to keystroke "{key}"'])
elif action_type == "scroll":
direction = kwargs.get("direction", "down")
amount = kwargs.get("amount", 3)
delta = -amount if direction == "down" else amount
# AppleScript 模拟滚动
subprocess.run(["osascript", "-e",
f'tell application "System Events" to scroll area 1 by {delta}'])
elif action_type == "mouse_move":
x, y = kwargs["coordinate"]
subprocess.run(["cliclick", f"m:{x},{y}"])
elif action_type == "screenshot":
pass # 下一轮循环自动截屏
def computer_use_loop(task: str, max_iterations: int = 10):
"""Computer Use 主循环"""
messages = [{"role": "user", "content": [
{"type": "text", "text": task}
]}]
for i in range(max_iterations):
# 1. 截屏
img_b64 = screenshot()
# 2. 追加截图到消息
if i > 0:
messages.append({"role": "user", "content": [
{"type": "tool_result", "tool_use_id": tool_use_id,
"content": [{"type": "image", "source": {
"type": "base64", "media_type": "image/png", "data": img_b64
}}]}
]})
else:
messages[0]["content"].append({
"type": "image", "source": {
"type": "base64", "media_type": "image/png", "data": img_b64
}
})
# 3. 调用 Claude Computer Use API
response = client.beta.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
tools=[{
"type": "computer_20250124",
"name": "computer",
"display_width_px": 1920,
"display_height_px": 1080,
}],
messages=messages,
betas=["computer-use-2025-01-24"],
)
# 4. 解析响应
messages.append({"role": "assistant", "content": response.content})
# 检查是否完成
if response.stop_reason == "end_turn":
print("任务完成")
break
# 5. 执行操作
for block in response.content:
if block.type == "tool_use":
tool_use_id = block.id
action = block.input.get("action")
print(f" 执行: {action} {block.input}")
execute_action(action, **block.input)
return messages
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 252 lines · 67 tokens per session scan A 5ccbd95c521f
claude-computer-use is a skill published in the GitHub repository malue-ai/dazee-small (36 stars, last pushed 5mo ago), licensed MIT. It adds 67 tokens to every session and 2,350 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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