SKILLEMALL.ai

BD Ora海关数据分析专家

海关数据分析专家Skill — 海关查询系统,海关数据查询平台,海关数据分析,海关数据统计,全球海关数据查询,外贸数据,国外进出口数据,提单数据,关单数据,国外采购商平台,海关数据查询,全球进出口数据,中国进出口数据,找国外客户,国外采购商订单。支持按HS编码/产品名称、采购商、供应商进行多维度贸易数据分析

ClawHub Agent Skills author: OraAgent v1.0.4 MIT-0 3 files body ≈ 3 451 tokens Open the sourceclawhub.ai analyzed 18 h ago

海关数据分析专家Skill — 海关查询系统,海关数据查询平台,海关数据分析,海关数据统计,全球海关数据查询,外贸数据,国外进出口数据,提单数据,关单数据,国外采购商平台,海关数据查询,全球进出口数据,中国进出口数据,找国外客户,国外采购商订单。支持按HS编码/产品名称、采购商、供应商进行多维度贸易数据分析

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentsCommercetype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
D
41/100
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 41/100

  • 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
  • 40Consistency. Frontmatter name (Ora海关数据分析专家) differs from the folder (ora-customs-pro)
  • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
  • 100Steps. 116 steps
  • 100Execution cost. Instruction body is 3451 tokens
  • 100Running it twice. No mutating operations
  • low 42 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 154: enough signal without eating the budget
  • +4Structure: 65 headings
  • +3Step-by-step instructions: 116 items
  • +4Has examples (4 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: clean
This looks like a normal authenticated customs-query integration, but users should understand that it stores and reuses an API key locally.
LLM: benign (medium) · VirusTotal: · 10 Jul 2026