AC ljh-zhibiao
关键指标波动诊断专家。沿内容电商指标树逐层定位 GMV、转化率、ROI、费比、新客、复购、内容效率等波动的首个异常节点，再把根因落到一个可验证的运营动作。 触发方式：/ljh-zhibiao、「GMV 为什么掉了」「转化率突然变差」「ROI 和费比异常」「新客或复购波动」「内容效率下降怎么查」 Content-commerce metric fluctuation diagnostic. Traces GMV, conversion, ROI, cost ratio, new-customer, repeat-purchase, and content-efficiency changes to the first abnormal child metric and a testable operating action. Trigger: /ljh-zhibiao, "why did GMV drop", "conversion rate changed", "ROI or cost ratio fluctuated", "new customers or repeat purchase changed", "content efficiency fell" 不要在没有指标波动、只想做渠道总账，或只想拆一条具体内容素材时自动触发。
关键指标波动诊断专家。沿内容电商指标树逐层定位 GMV、转化率、ROI、费比、新客、复购、内容效率等波动的首个异常节点，再把根因落到一个可验证的运营动作。 触发方式：/ljh-zhibiao、「GMV 为什么掉了」「转化率突然变差」「ROI 和费比异常」「新客或复购波动」「内容效率下降怎么查」…
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "slug"
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. 74 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1560 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
- +1No license
- +2Single-language instructions
- +5Description quotes 5 example trigger phrases
- +3Description length 575: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 74 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.