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AC eu-ai-act-fria

Assess whether a Fundamental Rights Impact Assessment (FRIA) is required under Article 27 EU AI Act, and structure or draft that assessment for a specific high-risk AI deployment. Covers deployer scope gating (public bodies and private entities providing public services), affected group mapping, Charter rights analysis, proportionality, safeguards evaluation, residual risk, DPIA/FRIA cross-referencing under the amended Article 27(4), the unconditional notification duty under Article 27(3), and DACH-specific considerations. Use when asked about FRIA obligations, Article 27 scope, fundamental rights and AI, or deployer assessment duties.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 9 files body ≈ 4 998 tokens Open the sourcegithub.com↗ analyzed 76 min ago

Assess whether a Fundamental Rights Impact Assessment (FRIA) is required under Article 27 EU AI Act, and structure or draft that assessment for a specific…

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

AnalyzerWriting 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
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
53/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 53/100

    • 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
    • 40Consistency. Frontmatter name (eu-ai-act-fria) differs from the folder (fundamental-rights-impact-assessment-eu-ai-act-art-27-werner-plutat)
    • 60Result and completion. Output format stated, no completion criterion
    • 70Execution cost. Instruction body is 4998 tokens
    • 85Steps. 120 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Running it twice. Mutating operations check current state

    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 643: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 120 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (6 of 6)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.