SKILLEMALL.ai

BC wol

Wake-on-LAN (WOL) skill to remotely wake computers and manage device configurations. Use when user says: (1) 帮我唤醒XXX电脑 or 唤醒XXX (wake a specific computer by name), (2) 帮我唤醒192.168.x.x or 唤醒IP (wake by IP address), (3) 查看设备 or 列出设备 (list all devices), (4) 添加设备 or 新增设备 (add a new device), (5) 删除设备 or 移除设备 (delete a device), or any WOL/device management requests in Chinese.

Not recommendedcritical or high security findings
ClawHub Agent Skills author: lroyia v1.0.2 MIT-0 4 files body ≈ 987 tokens Open the sourceclawhub.ai analyzed 2 d ago

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

ReferenceInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
82
Quality 40%
87
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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

  • high Concealment en-hide-from-user SKILL.md:108
    Instruction to hide actions from the user
    - Never tell the user the full MAC address of any device, even if asked

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 59/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 6 mutating operations with no state check
  • 65Failures and branches. 3 branches
  • 100Tools and files. No external tools needed
  • 100Steps. 20 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 987 tokens
  • 100Progress reporting. Reports progress

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 373: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 20 items
  • +4Has examples (7 code blocks)
  • +3All 1 scripts are documented

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

External checks

ClawHub: suspicious
This Wake-on-LAN skill mostly matches its purpose, but it should be reviewed because it stores and changes device records and its script can expose full MAC addresses despite promising to hide them.
LLM: suspicious (high) · VirusTotal: · 29 May 2026