AC retail-clerk-comparison-analysis
导购对比分析工具。基于clerk-performance-analysis的扩展，提供导购业绩的多维度对比分析。 核心能力： 1. 时间维度对比（培训前后、活动前后、月度环比） 2. 横向对比（多人排名、标杆学习、差距分析） 3. 高频场景（晨会对比、周会报告、绩效对标） 4. 导购能力雷达图对比 5. 业绩贡献度对比 触发条件： - 用户对比导购（如"李翠和杨丽谁业绩好"） - 用户需要排名（如"导购业绩排名"） - 用户分析差距（如"标杆导购和其他人的差距"）
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.
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-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. 10 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1240 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 3 example trigger phrases
- +3Description length 236: enough signal without eating the budget
- +4Structure: 20 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (12 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.
External checks
ClawHub: suspicious
This skill appears to support its stated business-reporting purpose, but it handles sensitive employee performance reports and can send them to enterprise WeChat without clear authorization or recipient safeguards.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026