SKILLEMALL.ai

AC retail-store-assortment-analysis

陈列货盘分析工具。从货盘视角分析引起客户意向的商品和试用行为变化。 核心能力: 1. 三漏斗交叉分析(displayFunnel陈列SKU + behaviorFunnel试用次数 + customerFunnel客户) 2. 引起意向的商品变化(引起意向SKU数、引起意向SKU占比) 3. 客户对意向商品的试用深度(平均试用深度) 4. 客户意向分散度(深度试用次数/有深度交互的客户数) 5. 货盘成交效率(货盘成交率、客户成交率) 触发条件: - 用户询问货盘(如"货盘怎么样") - 用户分析陈列效果(如"哪些商品引起客户兴趣") - 用户需要动销分析(如"货盘成交率如何")

ClawHub Agent Skills author: Xtechmerge.AI v1.0.0 MIT-0 3 files body ≈ 567 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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
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: 3. 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. 11 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 567 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

  • +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 2 example trigger phrases
  • +3Description length 295: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 11 items
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: suspicious
The skill appears to be a legitimate retail analytics helper, but it depends on an unbundled local API client from a hard-coded personal path before reading store BI data.
LLM: suspicious (high) · VirusTotal: · 29 May 2026