SKILLEMALL.ai
GitHub ↗ RU Sign in

AD eu-ai-act-roles-oliver-schmidt-prietz

Determines the organization's role (provider, deployer, importer, distributor, or quasi-provider) and assesses Art. 25 quasi-provider risk of the EU AI Act. This skill should be used when the user asks to "determine AI Act roles", "check if we are provider or deployer", "assess quasi-provider status", "check Art. 25 substantial modification", "check value chain responsibilities", or mentions "Betreiber", "Anbieter", "wesentliche Veränderung", or finetuning implications under the AI Act.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 13 files body ≈ 4 089 tokens Open the sourcegithub.com↗ analyzed 63 min ago

Determines the organization's role (provider, deployer, importer, distributor, or quasi-provider) and assesses Art.

As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureInfrastructureData and analyticsAI 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
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
D
48/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 13. 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 48/100

    • 0Result and completion. Does not say what the result is
    • 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
    • 30Running it twice. 10 mutating operations with no state check
    • 40Consistency. Frontmatter name (eu-ai-act-roles-oliver-schmidt-prietz) differs from the folder (eu-ai-act-role-determination-oliver-schmidt-prietz)
    • 70When it triggers. States when to use, but not when not to
    • 70Execution cost. Instruction body is 4089 tokens
    • 85Steps. 21 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • medium 3 test cases, all positive: not one "should refuse" or "should ask first"

    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

    • +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
    • +5Description quotes 8 example trigger phrases
    • +3Description length 491: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 21 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (8 of 9)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.