SKILLEMALL.ai

BC hardware-info

全面查询电脑硬件信息。当用户询问"电脑配置"、"硬件信息"、"系统信息"、"查看配置"、"电脑什么配置"、"硬件详情"、"设备信息"、"查看硬件"、"系统配置"、"电脑型号"、"CPU信息"、"内存多大"、"硬盘多大"、"显卡信息"等时触发。 支持 macOS、Linux 和 Windows 系统,自动检测平台并使用对应命令获取全面的硬件信息,包括: - 系统概览(型号、序列号、操作系统) - CPU 信息(型号、核心数、架构、频率) - 内存信息(总容量、类型、频率、插槽) - 存储设备(硬盘/SSD 容量、型号、SMART状态、分区) - 显卡/GPU(型号、显存、分辨率) - 显示器(分辨率、刷新率、类型) - 网络设备(网卡、MAC地址、接口) - 电池信息(健康度、循环次数、容量)- 笔记本 - USB/雷电设备 - 蓝牙设备 - 传感器/温度(如可用) - 实时状态(CPU负载、内存使用、磁盘使用)

ClawHub Agent Skills author: BraveHeartZJH v1.0.0 MIT-0 2 files body ≈ 850 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 53/100 · 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
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
C
53/100
Has gaps
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

  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 · 0

✓ No critical or high findings

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 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 18 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 850 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 412: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 18 items
  • +4Has examples (23 code blocks)

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

External checks

ClawHub: clean
This skill is a straightforward hardware-report helper, but its reports can expose device identifiers if shared.
LLM: benign (high) · VirusTotal: · 29 May 2026