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 briiirussell/cybersecurity-skills --skill ai-risk-managementgit 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/skills/briiirussell/cybersecurity-skills/ai-risk-management)<a href="https://agentmods.dev/skills/briiirussell/cybersecurity-skills/ai-risk-management"><img src="https://agentmods.dev/badge/skills/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/skills/briiirussell/cybersecurity-skills/ai-risk-management"><img src="https://agentmods.dev/badge/skills/briiirussell/cybersecurity-skills/ai-risk-management.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 | $0.00179 | $0.03221 |
| Opus 5 | $0.00089 | $0.01611 |
| Sonnet 5 | $0.00036 | $0.00644 |
| Haiku 4.5 | $0.00018 | $0.00322 |
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 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 — 228 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 executive orders, 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.
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 |
The framework is voluntary but increasingly cited in contracts, RFPs, executive orders, and emerging regulations. Treat it as the lingua franca of AI risk.
Workflow
Step 1 — Inventory AI systems
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 · 228 lines · 179 tokens per session scan A 189207335d4d
ai-risk-management is a skill published in the GitHub repository briiirussell/cybersecurity-skills (391 stars, last pushed 3mo ago), licensed MIT. It adds 179 tokens to every session and 3,221 once invoked, about $0.0009 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 2026-08-30.
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