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AD screen-control

屏幕控制技能 - 通过OpenClaw Node + pyautogui实现电脑屏幕识别和鼠标键盘操控。功能：(1) 截图获取屏幕画面，(2) OCR文字识别定位，(3) 图片匹配定位，(4) 鼠标移动/点击/拖拽，(5) 键盘输入/快捷键，(6) 基于视觉信息的自动化操作。Use when: (1) 需要远程操电脑屏幕，(2) 需要自动操作桌面应用（非浏览器），(3) 需要批量处理电脑上的文件或软件，(4) 需要通过视觉识别自动化操作。Triggers: '操作电脑', '屏幕控制', '自动点击', '截图识别', '桌面自动化', '远程操控', '操控鼠标', '屏幕识别'。

ClawHub Agent Skills author: ncsimok v1.0.0 MIT-0 5 files body ≈ 440 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructuretype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
D
41/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 5. 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 41/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (screen-control) differs from the folder (doorstep-screen-control)
    • 60Tools and files. Uses tools (node) that frontmatter does not declare
    • 100Steps. 10 steps
    • 100Execution cost. Instruction body is 440 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
    • -31 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 295: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 10 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: clean
    This skill openly provides desktop viewing and control, with no evidence of hidden exfiltration or deceptive behavior, but it is powerful and should only be used under supervision.
    LLM: benign (high) · VirusTotal: · 29 May 2026