SKILLEMALL.ai

AC retail-sales-performance-analysis

门店销售业绩环比分析工具。支持门店/导购业绩同比分析(本期 vs 上期),识别业绩波动原因,量化归因,输出诊断结论和改进建议。 使用场景: 1. 门店整体业绩分析(销售额、订单数、客单价、连带率) 2. 导购个人业绩分析(排名、业绩占比、能力雷达图) 3. 多门店/多导购对比分析 4. 业绩波动归因(订单贡献 vs 客单贡献) 5. 异常识别与风险预警 触发条件: - 用户询问业绩(如"本周业绩怎么样") - 用户分析业绩下滑原因(如"为什么销售下降") - 用户对比业绩(如"比上月业绩如何") - 用户需要业绩诊断(如"业绩达标了吗")

ClawHub Agent Skills author: Xtechmerge.AI v1.0.0 MIT-0 3 files body ≈ 957 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. 5 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 957 tokens
  • 100Running it twice. No mutating operations
  • low 11 top-level sections: this looks like several domains in one skill

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 4 example trigger phrases
  • +3Description length 274: enough signal without eating the budget
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 5 items
  • +4Has examples (7 code blocks)

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

External checks

ClawHub: clean
This is a purpose-aligned retail sales analysis skill, with manageable risks around business data access and an unbundled local API client dependency.
LLM: benign (high) · VirusTotal: · 29 May 2026