AC retail-clerk-structure-analysis
导购结构分析工具。分析门店导购的表现结构，识别导购波动对门店业绩波动的贡献度。 核心能力： 1. 导购表现结构分析（人效分布、帕累托分析） 2. TOP/腰部/尾部导购识别 3. 业绩集中度评估 4. 导购波动归因（各导购对门店业绩变化的贡献度） 5. 增长型vs下滑型导购分类 6. 基于累计贡献度的关键人识别 触发条件： - 用户分析导购结构（如"导购团队结构如何"） - 用户需要波动归因（如"业绩下滑是谁导致的"） - 用户识别关键人（如"哪些导购对业绩影响最大"）
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. 9 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 797 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 239: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 9 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.
External checks
ClawHub: suspicious
The skill appears to perform the advertised retail clerk analysis, but it loads unreviewed local code and fetches sensitive staff performance data through an undeclared API client.
LLM: suspicious (high) · VirusTotal: · 29 May 2026