Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add niels-emmer/myace --skill eu-ai-act-high-risk-obligationsgit clone --depth 1 https://github.com/niels-emmer/myaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/niels-emmer/myace/eu-ai-act-high-risk-obligations)<a href="https://agentmods.dev/skills/niels-emmer/myace/eu-ai-act-high-risk-obligations"><img src="https://agentmods.dev/badge/skills/niels-emmer/myace/eu-ai-act-high-risk-obligations/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/niels-emmer/myace/eu-ai-act-high-risk-obligations"><img src="https://agentmods.dev/badge/skills/niels-emmer/myace/eu-ai-act-high-risk-obligations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.01339 |
| Opus 5 | $0.00026 | $0.00669 |
| Sonnet 5 | $0.00010 | $0.00268 |
| Haiku 4.5 | $0.00005 | $0.00134 |
Grade A, and why
EU AI Act High-Risk Obligations scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Give reviewers a concrete, article-by-article checklist for the obligations on providers of high-risk AI systems under Regulation (EU) 2024/1689. Use it to review a system against the requirements and to produce PASS/FAIL/N/A findings with evidence.
When to use it
Whenever a system has been classified high-risk (Annex I or Annex III) and needs to be reviewed against its obligations, or when drafting the documentation that evidences those obligations. Pair with eu-ai-act-risk-classification (confirm the tier first) and eu-ai-act-documentation (produce the evidence).
The obligations
Art 8 — Compliance with requirements
The system must comply with Arts 9-15, taking into account its intended purpose and the state of the art, proportionately to the risk-management system.
Art 9 — Risk management system
A documented, iterative risk-management process across the lifecycle: identify and analyse known and foreseeable risks to health, safety, and fundamental rights; estimate and evaluate risk; adopt risk-management measures; test the system against those measures. Must be maintained and updated as the system evolves.
Art 10 — Data governance
Training, validation, and testing datasets must be relevant, sufficiently representative, and free of errors and bias, appropriate to the intended purpose. Document: design choices, data collection and origin (and, for personal data, the original purpose), data-preparation operations (annotation, labelling, cleaning, enrichment, aggregation), assumptions about what the data measures, and an assessment of availability, quantity, and suitability. Special attention to bias and to special-category personal data.
Art 11 — Technical documentation
Draw up technical documentation before placing on the market, kept up to date, demonstrating compliance and enabling a competent authority to assess it. See Annex IV for the required contents (covered by the eu-ai-act-documentation skill).
Art 12 — Record-keeping / automatic logging
The system must automatically record events over its lifetime, enabling traceability of its operation and monitoring for risks. Logs must be sufficient to interpret the system's output and identify situations that may lead to risk or substantial modification. Retention period must be appropriate to the intended purpose and applicable law.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 74 lines · 52 tokens per session scan A 46afd9c52d5d
EU AI Act High-Risk Obligations is a skill published in the GitHub repository niels-emmer/myace (1 stars, last pushed 5d ago), licensed MIT. It adds 52 tokens to every session and 1,339 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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