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BB eu-ai-act-specialist

EU AI Act (Regulation EU 2024/1689) compliance specialist. Use for AI system risk classification, provider/deployer obligations, GPAI model compliance, conformity assessments, bias and fairness testing, and AI governance programs.

borghei/Claude-Skills Agent Skills author: borghei 7 files · 3 scripts body ≈ 7 161 tokens Open the sourcegithub.com↗ analyzed 8 h ago

EU AI Act (Regulation EU 2024/1689) compliance specialist.

As a process B 66/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerSecurityWriting and documentsInfrastructuretype 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
66/100
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. 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 · 0

✓ No critical or high findings

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7161 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 248): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 66/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 14 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 50When it triggers. No condition that starts the skill
  • 70Execution cost. Instruction body is 7161 tokens
  • 85Steps. 70 steps, 2 vague phrases
  • 100Tools and files. No external tools needed
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • low 17 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (8 tags): a typed call is more reliable

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
  • +2Single-language instructions
  • +3Description length 230: enough signal without eating the budget
  • +4Structure: 39 headings
  • +3Step-by-step instructions: 70 items
  • +4Has examples (7 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 3 scripts are documented
  • +1License stated

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