SKILLEMALL.ai

CF sanctions-screening-legal-analysis-skill-english-gillan-saleh

Sanctions and export control analysis tool for Claude Desktop. Real-time individual screening across 30+ official lists (UN, EU, OFAC, OFSI, French DGT...), sectoral analysis, dual-use goods (EU Reg. 2021/821), US/China extraterritorial regimes (EAR, ITAR, FDPR, ECL), USD/SWIFT risk and jurisdictional mapping across 30+ countries. All results sourced and verified in real time — no memory-based answers. Responds in French, English, German, Spanish, Russian and Chinese. Updated 19 May 2026 (EU 20th package, UK SEUC, Cuba EO). Indicative guidance only — not legal advice.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 2 files body ≈ 10 392 tokens Open the sourcegithub.com↗ analyzed 77 min ago

Sanctions and export control analysis tool for Claude Desktop.

As a process F 32/100 · No process to follow — References files that are not bundled: references/regimes.md, references/qualification-juridique.md, references/sources-officielles.md

AnalyzerAI and agentsResearchtype and topics are labelled automatically from the skill text
JSON
Technical rating
C
83/100
safety, quality, tests
Safety 60%
99
Quality 40%
60
Run on models
none yet
Process rating
F
32/100
No process to follow
References files that are not bundled: references/regimes.md, references/qualification-juridique.md, references/sources-officielles.md
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 SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  2. 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 Risky intent intent-offensive-security SKILL.md:219
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - **Managed security services** (cybersecurity risk management, penetration testing, security audits) prohibited to Russian government and Russia-established entities (effective 25 May 2026)

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 10392 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: references/regimes.md
  • warning missing-ref reference to a missing file: references/qualification-juridique.md
  • warning missing-ref reference to a missing file: references/sources-officielles.md

Process rating: all ten parameters 32/100

Will not run. References files that are not bundled: references/regimes.md, references/qualification-juridique.md, references/sources-officielles.md
  • 0Tools and files. 3 referenced file(s) missing: references/regimes.md, references/qualification-juridique.md, references/sources-officielles.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 6 mutating operations with no state check
  • 40Consistency. Frontmatter name (sanctions-screening-legal-analysis-skill-english-gillan-saleh) differs from the folder (sanctions-and-export-analysis-gillan-saleh)
  • 40Execution cost. Instruction body is 10392 tokens: crowds the task out of the window
  • 85Steps. 155 steps, 3 vague phrases
  • 100Failures and branches. 1 branches, has a failure section
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 14 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
  • -215 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 574: enough signal without eating the budget
  • +4Structure: 46 headings
  • +3Step-by-step instructions: 155 items
  • +4Has examples (6 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.