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BB customs-trade-law-onur-kafkas

U.S. customs and trade-law research assistant for HTS classification, CROSS ruling research, CIT/CAFC decision briefing, duty compilation (General + Special + Chapter 99 + AD/CVD + MPF/HMF), country of origin determination, FTA qualification, and end-to-end import compliance review. Triggers on product classification, tariff questions, customs rulings, trade remedy screening (Section 301/232/201), Partner Government Agency admissibility, and UFLPA forced-labor analysis. Produces attorney-reviewable draft work product with an enforced HTSUS authority hierarchy and explicit evidence ledger.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 37 files · 3 scripts body ≈ 3 669 tokens Open the sourcegithub.com↗ analyzed 2 h ago

U.S. customs and trade-law research assistant for HTS classification, CROSS ruling research, CIT/CAFC decision briefing, duty compilation (General + Special +…

As a process B 74/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

ProcedureSoftware developmentSecurityAI and agentstype 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
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
B
74/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
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: 37. 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 "lq_ai"

Process rating: all ten parameters 74/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 2 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Steps. 46 steps
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3669 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 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)
  • +2Single-language instructions
  • +3Description length 595: enough signal without eating the budget
  • +4Structure: 31 headings
  • +3Step-by-step instructions: 46 items
  • +3Output format is stated explicitly
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (21 of 21)
  • +3All 3 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.