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CC customs-trade-compliance

通関書類、関税分類、関税最適化、制限当事者スクリーニング、複数の法域にわたる規制コンプライアンスのための成文化された専門知識。15年以上の経験を持つ貿易コンプライアンス専門家に情報。HS分類ロジック、インコターム適用、FTA利用、ペナルティ軽減を含みます。通関許可、関税分類、貿易コンプライアンス、輸入/輸出ドキュメント、または関税最適化を処理するときに使用します。

affaan-m/everything-claude-code Agent Skills author: affaan-m MIT 1 file body ≈ 298 tokens Open the sourcegithub.com↗ analyzed 26 h ago

通関書類、関税分類、関税最適化、制限当事者スクリーニング、複数の法域にわたる規制コンプライアンスのための成文化された専門知識。15年以上の経験を持つ貿易コンプライアンス専門家に情報。HS分類ロジック、インコターム適用、FTA利用、ペナルティ軽減を含みます。通関許可、関税分類、貿易コンプライアンス、輸入/輸出ドキュメン…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSecuritytype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
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

The same skill appears in 1 more place: ECC

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: 1. 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")
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "origin"

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. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 298 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

  • +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
  • +4No input/output examples
  • +2Single-language instructions
  • +3Description length 183: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 14 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.