SKILLEMALL.ai

BB ios-device-automation

Vision-driven iOS device automation using Midscene CLI. Operates entirely from screenshots — no DOM or accessibility labels required. Can interact with all visible elements on screen regardless of technology stack. Control iOS devices with natural language commands via WebDriverAgent. Triggers: ios, iphone, ipad, ios app, tap on iphone, swipe, mobile app ios, ios device, ios testing, iphone automation, ipad automation, ios screen, ios navigate, test ios app, verify on iphone, QA on ipad, check the app on ios, test on ios device, see if the app works on iphone, end-to-end test on ios, visual verification on ios Powered by Midscene.js (https://midscenejs.com)

ClawHub Agent Skills author: Leyang v1.0.5 MIT-0 2 files body ≈ 2 836 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 66/100 · Nearly there — weak spots: result and completion, when it triggers, consistency

IntegrationInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
95
Quality 40%
72
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
When it triggers w 12
20
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Bash

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 66/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (ios-device-automation) differs from the folder (midscene-ios-automation)
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 20 steps
  • 100Failures and branches. 3 branches, has a failure section
  • 100Execution cost. Instruction body is 2836 tokens
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (4 tags): a typed call is more reliable

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 667: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 20 items
  • +4Has examples (14 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
This appears to be a legitimate iOS automation skill, but it can broadly control a live device and send screen contents to external model providers without enough user-facing safeguards.
LLM: suspicious (high) · VirusTotal: suspicious · 28 May 2026