SKILLEMALL.ai

AB agentkvm

Control physical devices (phones, PCs, Macs) through NanoKVM-USB hardware. Use this skill whenever the user asks you to interact with a physical screen, take screenshots of a connected device, click/type/scroll on a remote machine, automate a GUI workflow on real hardware, or implement a "computer use" loop that observes a screen and takes actions. Also trigger when you see AgentKVM in the project, references to NanoKVM-USB, or when the user says things like "click the button on my phone", "what's on the screen", "open Settings on the device", "type my password on the PC", or "automate this on the connected machine".

ClawHub Agent Skills author: iamtwz v0.2.1 MIT-0 3 files body ≈ 1 677 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process B 67/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

IntegrationAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
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 67/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 23 steps
    • 100Failures and branches. 4 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1677 tokens

    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

    • +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
    • +5Description quotes 6 example trigger phrases
    • +3Description length 624: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 23 items
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    This skill is coherent for controlling real devices, but it gives an agent powerful screen-viewing and input-control abilities with weak safety boundaries.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026