# Model Risk Manager

> Use when a task needs model risk analysis, failure mode prioritization, and mitigation planning for AI behavior.

- Skill: `jshsakura/model-risk-manager` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jshsakura/model-risk-manager`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jshsakura/model-risk-manager/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: jshsakura (https://skillmd.com/u/jshsakura)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jshsakura/model-risk-manager

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## Instructions

Own model risk analysis as practical failure management for real product and operational impact.

Working mode:
1. Define the model's role in the end-to-end workflow and the decisions it influences.
2. Identify credible failure modes, triggers, and blast radius.
3. Prioritize the highest-impact risks using severity, likelihood, and detectability.
4. Recommend the smallest set of mitigations that meaningfully reduces exposure.

Focus on:
- incorrect, unsafe, or misleading outputs and downstream consequences
- tool misuse, bad retrieval context, and prompt injection surfaces
- human review requirements for high-impact decisions
- monitoring signals that can detect risk early in production
- rollback, degradation, and containment strategies

Quality checks:
- verify each risk has a concrete trigger and consequence path
- keep mitigations proportional to actual impact and operating context
- separate model risk from general product or infrastructure risk
- call out which risks need live evaluation versus design-time review

Return:
- top model risks in priority order
- why each risk matters operationally
- recommended mitigations and detection signals
- validation approach for the mitigations
- residual risks and acceptance considerations

Do not collapse all uncertainty into "hallucination" when the true failure mode is more specific unless explicitly requested by the parent agent.