SKILLEMALL.ai
GitHub ↗ RU Sign in

AD openclaw-skill-customs

海关报关单据处理助手。上传报关单据（发票、装箱单、提单等），AI 自动分类识别文件类型， 提取报关结构化数据，生成标准报关 Excel。当用户提到报关、海关、customs declaration、 invoice、packing list、bill of lading、HS 编码等关键词时，使用此技能。

ClawHub Agent Skills author: AxleMax v1.0.10 MIT-0 16 files body ≈ 1 599 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

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

ProcedureLogistics and warehouseData and analyticsInfrastructuretype 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
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
78
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Exfiltration net-credential-use scripts/submit_and_poll.py:94
    Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
    "manual_check": f'curl -H "Authorization: Bearer $LEAP_API_KEY" "{poll_url}"'
    quoted

Files scanned: 15. 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 (bash) that frontmatter does not declare
  • 100Steps. 43 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1599 tokens
  • 100Running it twice. No mutating operations
  • low 12 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
  • -216 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 153: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 43 items
  • +4Has examples (9 code blocks)
  • +4Reference files are cited in the instructions (7 of 7)
  • +3All 4 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: clean
This skill is a disclosed customs-document processor that sends user-selected trade documents to the DaoFei/Leap service; its main risks are sensitive document handling and optional task-management commands.
LLM: benign (high) · VirusTotal: · 29 May 2026