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查询美国进口交易统计 — 按州或城市维度返回美国进口交易统计，包含进口记录数、集装箱数及近90天数据，支持游标分页。 Trigger: 美国进口统计，按州查询进口，按城市查询进口，美国进口记录，美国集装箱数据

ClawHub Agent Skills author: upkuajing openapi skills v1.0.1 MIT-0 10 files body ≈ 1 093 tokens Open the sourceclawhub.ai↗ analyzed 33 h ago

查询美国进口交易统计 — 按州或城市维度返回美国进口交易统计，包含进口记录数、集装箱数及近90天数据，支持游标分页。 Trigger: 美国进口统计，按州查询进口，按城市查询进口，美国进口记录，美国集装箱数据

As a process F 35/100 · Will not run — References files that are not bundled: scripts/*.py

ProcedureSoftware developmentData and analyticsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
99
Quality 40%
76
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: scripts/*.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Exfiltration read-dotenv SKILL.md:39
    Reads a .env file
    cat ~/.upkuajing/.env

Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/*.py

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: scripts/*.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/*.py
  • 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
  • 100Steps. 62 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1093 tokens
  • 100Running it twice. No mutating operations
  • low 11 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)
  • +3Description length 105: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -32 of 5 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 62 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
The skill mostly matches a paid trade-data lookup use case, but it also handles plaintext API keys, billing-related flows, error reporting, and an undisclosed version check that users should review before installing.
LLM: suspicious (high) · 24 Aug 2026