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 lexbeam-software/eu-ai-governance-plugin --skill risk-managementgit clone --depth 1 https://github.com/lexbeam-software/eu-ai-governance-pluginWrote 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/lexbeam-software/eu-ai-governance-plugin/risk-management)<a href="https://agentmods.dev/skills/lexbeam-software/eu-ai-governance-plugin/risk-management"><img src="https://agentmods.dev/badge/skills/lexbeam-software/eu-ai-governance-plugin/risk-management/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/lexbeam-software/eu-ai-governance-plugin/risk-management"><img src="https://agentmods.dev/badge/skills/lexbeam-software/eu-ai-governance-plugin/risk-management.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 | $0.00056 | $0.00634 |
| Opus 5 | $0.00028 | $0.00317 |
| Sonnet 5 | $0.00011 | $0.00127 |
| Haiku 4.5 | $0.00006 | $0.00063 |
Grade A, and why
risk-management 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 9d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Manage AI risk
Separate three layers: legal requirements, organisational risk appetite, and operating controls. Do not present an internal score as a legal classification.
Scope the system
Record intended purpose, foreseeable misuse, actors, affected persons, lifecycle stage, model and data dependencies, environment, change history, and legal classification. Follow LEGAL-SOURCE-PROTOCOL.md for legal conclusions.
Identify risk
Assess concrete harms and failure modes across:
- health, safety, and fundamental rights
- discrimination, accessibility, and representation
- privacy, confidentiality, and data governance
- accuracy, robustness, cybersecurity, and resilience
- transparency, explainability, contestability, and human oversight
- manipulation, misuse, prohibited practices, and synthetic content
- supplier, model-chain, concentration, and change risk
- legal, operational, financial, reputational, and environmental effects
Identify affected groups, exposure paths, existing controls, and uncertainty. Do not treat a generic taxonomy as evidence that a risk exists.
Evaluate and treat
Use the organisation’s approved likelihood and severity scales. Record inherent risk, control design, control operation, residual risk, owner, treatment, due date, evidence, and acceptance authority. Define measurable thresholds and stop-use criteria where feasible.
For high-risk providers, map the iterative lifecycle process to Article 9 and connect testing, technical documentation, instructions, post-market monitoring, and corrective action. Do not imply Article 9 applies to every deployer or minimal-risk system.
Monitor
Define metric, population, threshold, frequency, owner, data source, response, and limitations. Include performance, drift, subgroup effects, override rates, complaints, incidents, uptime, security events, input changes, model changes, and human-oversight effectiveness where relevant.
Handle incidents
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.
- 9d ago First seen · 56 lines · 56 tokens per session scan A ea4308098abb
risk-management is a skill published in the GitHub repository lexbeam-software/eu-ai-governance-plugin (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 56 tokens to every session and 634 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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