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.
git clone --depth 1 https://github.com/briiirussell/cybersecurity-skillsWrote 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/rules/briiirussell/cybersecurity-skills/ai-risk-management)<a href="https://agentmods.dev/rules/briiirussell/cybersecurity-skills/ai-risk-management"><img src="https://agentmods.dev/badge/rules/briiirussell/cybersecurity-skills/ai-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/rules/briiirussell/cybersecurity-skills/ai-risk-management"><img src="https://agentmods.dev/badge/rules/briiirussell/cybersecurity-skills/ai-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 | $0.00176 | $0.03946 |
| Opus 5.5 | $0.00070 | $0.01578 |
| Sonnet 5.5 | $0.00035 | $0.00789 |
| Haiku 4.5 | $0.00018 | $0.00395 |
Grade A, and why
ai-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 today.
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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Risk Management — Beyond Security, the Whole Model Lifecycle
prompt-injection covers the AI security slice — attackers manipulating LLM inputs. This skill covers everything else risk-related about deploying AI / ML systems: governance, fairness, robustness, transparency, monitoring, incident response specific to AI failures, third-party model risk, and compliance with the emerging AI regulatory landscape.
The framing is NIST AI RMF 1.0 (released 2023) — the most widely-adopted voluntary framework — plus the regulatory layer (EU AI Act, US federal policy and state laws, sector-specific guidance). Use this skill when you are deploying AI features beyond a chatbot wrapper, when a regulator asks "how do you govern your AI," or when something has gone wrong with an AI system in production.
Cross-references: prompt-injection for prompt-injection / LLM-specific security attacks; threat-modeling for design-time AI risk modeling; incident-triage and breach-patterns for AI-related incident response patterns; csf-mapping for the broader governance frame that AI RMF sits within.
Authorization
Assess AI systems the user's organization builds, deploys, or buys, using material they're entitled to share. This skill reads documentation, model cards, eval results and code. It doesn't run evaluations against production models with real user data, or against a third-party model in ways its terms forbid. Regulatory conclusions are input for counsel, not legal advice.
The NIST AI RMF — four functions
Just like the cybersecurity framework, the AI RMF organizes the work into functions. Same shape, different content.
| Function | What it covers |
|---|---|
| Govern (GOV) | Policy, accountability, roles, risk appetite, AI principles, board oversight, governance structures |
| Map (MAP) | Context — what is the AI system, what does it do, who is impacted, what could go wrong, what are the legal / ethical constraints |
| Measure (MEAS) | Evaluate the system — fairness, robustness, accuracy, explainability, privacy, security; quantitative + qualitative metrics |
| Manage (MAN) | Treat the risks — mitigations, monitoring, incident response, decommissioning, ongoing review |
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.
- today Changed · +30 lines · +1 tokens per session 72c9e12d2462
- 1mo ago First seen · 227 lines · 175 tokens per session scan A 3b369d41b25c
ai-risk-management is a cursor rule published in the GitHub repository briiirussell/cybersecurity-skills (435 stars, last pushed today), licensed MIT. It adds 176 tokens to every session and 3,946 once invoked, about $0.0007 per session on Opus 5.5. 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 2026-08-30.
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