AC retail-clerk-performance-analysis
导购个人业绩深度分析工具。支持普通门店（POS数据）和AIoT门店（POS+AIoT数据）。 输出导购个人详细诊断报告，包含： 1. 核心业绩指标（销售额、排名、业绩占比） 2. 雷达图能力对比（6维能力 vs 门店平均） 3. 商品特征分析（品类/价格带/包型/颜色/新品偏好） 4. Top5 SKU爆品分析（门店贡献率、SPU集中度、上市时间） 5. 订单结构分析（折扣/连带/会员结构） 6. AIoT高试用低转化分析（仅AIoT门店） 7. AIoT客户漏斗分析（仅AIoT门店） 8. 14天销售趋势分析 9. 综合诊断与行动建议 触发条件： - 用户询问导购业绩（如"李翠业绩怎么样"） - 用户分析导购能力（如"导购销售能力如何"） - 用户需要导购诊断（如"导购有什么问题"）
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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-whendescription 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. 120 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3007 tokens
- 100Running it twice. No mutating operations
- low 13 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
- -228 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 349: enough signal without eating the budget
- +4Structure: 35 headings
- +3Step-by-step instructions: 120 items
- +4Has examples (7 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.