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 jabrena/plinth --skill 801-regulations-eu-ai-actgit clone --depth 1 https://github.com/jabrena/plinthWrote 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/jabrena/plinth/801-regulations-eu-ai-act)<a href="https://agentmods.dev/skills/jabrena/plinth/801-regulations-eu-ai-act"><img src="https://agentmods.dev/badge/skills/jabrena/plinth/801-regulations-eu-ai-act/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/jabrena/plinth/801-regulations-eu-ai-act"><img src="https://agentmods.dev/badge/skills/jabrena/plinth/801-regulations-eu-ai-act.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00108 | $0.02019 |
| Opus 5 | $0.00054 | $0.01009 |
| Sonnet 5 | $0.00022 | $0.00404 |
| Haiku 4.5 | $0.00011 | $0.00202 |
Grade A, and why
801-regulations-eu-ai-act 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 8d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EU AI Act Regulation for Java Enterprise Development with AI Systems and AI Agents
Use this Skill to review Java enterprise applications that include AI capabilities, AI agents, tool-calling workflows, RAG systems, workflow automation, or model-driven decision support.
Apply this Skill to determine what engineering controls are required before the system is released, deployed, or connected to corporate systems of record.
This Skill is not legal advice. It helps Java engineers, architects, tech leads, platform teams, and reviewers identify when EU AI Act concerns may apply and how to translate policy expectations into enterprise architecture controls such as policy gates, human oversight, least privilege, audit evidence, monitoring, escalation workflows, and approval processes.
The purpose of this Skill is to increase awareness of potential gaps in the system and create engineering evidence for qualified review. The response produced by this Skill does not represent legal advice, a legal opinion, or a final regulatory determination.
The main question is:
When does a Java application or AI agent require EU AI Act-aware engineering controls, and what should developers build differently?
External reference: European Parliament legislative resolution TA-9-2024-0138.
EU AI Act chapters summary reference: EU AI Act chapters summary.
Java engineering examples reference: EU AI Act engineering examples.
Questionnaire asset: EU AI Act engineering review questionnaire.
Report template asset: EU AI Act engineering review report template.
Scope
This Skill applies to:
- Java applications embedding AI models or LLMs
- Spring AI, LangChain4j, Quarkus AI, and custom AI integrations
- RAG applications and enterprise knowledge assistants
- AI agents capable of calling enterprise tools
- Workflow automation driven by AI decisions or recommendations
- AI systems interacting with databases, APIs, message brokers, filesystems, IAM platforms, CI/CD pipelines, cloud resources, or external services
- AI-generated code, SQL, Flyway migrations, Liquibase changelogs, infrastructure definitions, operational runbooks, or deployment actions
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 123 lines · 108 tokens per session scan A 1cce982e274b
801-regulations-eu-ai-act is a skill published in the GitHub repository jabrena/plinth (439 stars, last pushed yesterday), licensed Apache-2.0. It adds 108 tokens to every session and 2,019 once invoked, about $0.0005 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 2026-09-03.
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