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AD desktop-automation-pro

Desktop GUI automation toolkit for browser, mobile devices, and native applications. 桌面 GUI 自动化工具包，支持浏览器、移动设备和原生应用。 Use this skill when: 使用此技能的场景： - Automating web browsers (Chromium-based) - 控制 Chromium 浏览器 - Controlling paired mobile devices (Android/iOS/macOS) - 控制配对的移动设备 - Taking screenshots of desktop/windows/regions - 桌面/窗口/区域截图 - Automating Windows native applications via Python - 通过 Python 自动化 Windows 原生应用 - Mouse/keyboard simulation - 鼠标键盘模拟 Triggers: 桌面自动化, GUI自动化, 浏览器自动化, 手机控制, 截图, desktop automation, GUI automation, browser automation, mobile control, screenshot, pyautogui, pywinauto

ClawHub Agent Skills author: systiger v1.0.0 MIT-0 4 files body ≈ 1 327 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 46/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (desktop-automation-pro) differs from the folder (desktop-automation-pro-systiger)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 100Steps. 9 steps
    • 100Execution cost. Instruction body is 1327 tokens
    • 100Running it twice. No mutating operations

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 604: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (6 code blocks)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    This skill is a legitimate automation guide, but it gives broad control over browsers, paired devices, screenshots, local Python execution, and system-wide input without enough safety boundaries.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026