SKILLEMALL.ai
RU Sign in

AC adb-bot

AI 驱动的 Android 自动化 — 截屏、点击、滑动、输入、启动应用、UI 识别、多设备群控 | Android automation via ADB, screen capture, tap, swipe, type text, app control, multi-device, phone RPA, adb bot

ClawHub Agent Skills author: hilbp v1.1.0 MIT-0 3 files body ≈ 790 tokens Open the sourceclawhub.ai↗ analyzed 35 h ago

AI 驱动的 Android 自动化 — 截屏、点击、滑动、输入、启动应用、UI 识别、多设备群控 | Android automation via ADB, screen capture, tap, swipe, type text, app control, multi-device, phone RPA…

As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, progress reporting

ProcedureGitHubAI and agentstype 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
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: 3. 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 59/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 26 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 790 tokens
    • 100Running it twice. No mutating operations
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +3Description length 164: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (4 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

    External checks

    ClawHub: suspicious
    This skill is for legitimate Android automation, but it gives an agent powerful phone-control and screen-reading abilities while under-disclosing privacy and state-change risks.
    LLM: suspicious (high) · 6 Aug 2026