SKILLEMALL.ai

AC retail-customer-list-analysis

客户清单分析工具。基于Shop API客户清单数据,快速查询不同类型客户的数量、试用情况汇总、导购匹配情况。 核心能力: 1. 客户类型分布统计(普通/潜在/意向/成交客户数量及占比) 2. 客户试用情况汇总(感兴趣商品数、试用商品数、试用后成交转化) 3. 导购匹配分析(各导购关联客户数、匹配失败数量及原因) 4. 客户明细列表查询(支持分页、筛选、导出) 数据源:POST /api/v1/customer/list 使用场景: - 晨会/周会快速查询客户情况 - 实时监控客户类型分布 - 导购客户分配检查 - 匹配失败客户处理 触发条件: - 用户查询客户清单(如"今天有多少意向客户") - 用户统计客户类型(如"各类型客户占比多少") - 用户检查导购匹配(如"有多少客户匹配失败") - 用户查看客户试用情况(如"客户试用情况如何")

ClawHub Agent Skills author: Xtechmerge.AI v1.0.0 MIT-0 4 files body ≈ 749 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

IntegrationInfrastructuretype 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: 4. 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. 6 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 749 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 4 example trigger phrases
  • +3Description length 379: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 6 items
  • +4Has examples (5 code blocks)

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

External checks

ClawHub: clean
This skill is a disclosed customer-list analytics tool, but users should treat its outputs as sensitive customer and business data.
LLM: benign (medium) · VirusTotal: · 29 May 2026