SKILLEMALL.ai

AC home-appliance-analyst

家电行业分析专家技能。涵盖厨房电器、生活电器、个人护理保健、大家电四大品类。支持:行业信息搜集、市场大盘分析、产品比价研究、供应链拆解、产品原理与技术演进分析、合规标准查询,以及按需生成调研报告。当用户询问家电行业相关问题时触发,包括但不限于:搜索家电市场数据、分析某品类/品牌/型号、查询国标认证要求、拆解产品结构与成本、竞品对比、换皮识别、区域合规要求、产品工作原理、技术迭代路线、除醛/净化等技术问题、家电行业分析报告等。

ClawHub Agent Skills author: konnyacer v1.0.0 MIT-0 52 files body ≈ 1 023 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
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: 52. 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. 60 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1023 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 214: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 60 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
This appliance-analysis skill is mostly a plain reference-and-search skill, but one disinfectant-generator reference contains overconfident safety/use claims that users should review before relying on it.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026