SKILLEMALL.ai

CC EU-Legislation

Access EU law. Search and retrieve Directives, Regulations, Treaties and CJEU case law from EUR-Lex.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 2 files body ≈ 5 322 tokens Open the sourcegithub.com↗ analyzed 90 min ago

Access EU law. Search and retrieve Directives, Regulations, Treaties and CJEU case law from EUR-Lex.

As a process C 58/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

ProcedureWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
80/100
safety, quality, tests
Safety 60%
95
Quality 40%
57
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash(curl:*)
    allowed-tools: Bash(curl:*) WebFetch

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

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 5322 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 58/100

  • 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
  • 40Consistency. Frontmatter name (EU-Legislation) differs from the folder (eu-legislation-malik-taiar)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 5322 tokens
  • 85Steps. 25 steps, 2 vague phrases
  • 100Tools and files. Tools declared in frontmatter
  • 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)
  • +3Description length 100: 120–800 characters recommended
  • +1No license
  • +2Single-language instructions
  • +4Structure: 49 headings
  • +3Step-by-step instructions: 25 items
  • +3Output format is stated explicitly
  • +4Has examples (22 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.