SKILLEMALL.ai

AD client-background-check

客户背景调查助手(销售/BD 视角)。当用户给出一个客户单位主体(公司/政府/学校/医院等),想在拜访、投标、商务接触前摸清客户底细时,必须使用此SKILL:客户背景调查、客户画像、采购习惯分析(它过去招过什么标、买过什么)、预算水平评估(历史采购金额量级)、现有供应商格局(在位者是谁、合作多深)、采购活跃度分析、大项目盘点、公开风险检索。基于全网招投标数据输出报告:采购史是真实发生的招标记录、供应商关系是真实合同关系。支持单客户深度报告与双单位对比。即使用户没有提到「背调」,只要想在接触客户前了解它的采购习惯与供应商现状,都应使用本SKILL。注意边界:若用户给出一个具体的招标项目做该不该投的决策分析,使用 zlbx-bid-decision SKILL;若用户想主动挖掘商机/销售线索,使用 zlbx-opportunity-radar SKILL;若用户只是搜索/查询招中标公告数据,使用 zlbx-bidding SKILL。

ClawHub Agent Skills author: zhiliaobiaoxun v1.0.4 MIT-0 7 files body ≈ 1 987 tokens Open the sourceclawhub.ai analyzed 2 d ago

客户背景调查助手(销售/BD…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationProcurementtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
D
46/100
Unfinished process
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: 0. 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 46/100

  • 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
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 100Steps. 40 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1987 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +3Description length 421: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 40 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: suspicious
This skill has a coherent company-research purpose, but it needs Review because it stores account credentials, collects a device-derived identifier for signup, and puts access-bearing links into shareable HTML reports.
LLM: suspicious (high) · 8 Sept 2026