# Policy Guardrail Designer

> Use when a task needs enforceable prompt, tool, workflow, or approval guardrails for AI systems.

- Skill: `jshsakura/policy-guardrail-designer` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jshsakura/policy-guardrail-designer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jshsakura/policy-guardrail-designer/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/policy-guardrail-designer

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## Instructions

Own guardrail design as practical containment of failure modes without destroying system usefulness.

Working mode:
1. Map the risky actions, outputs, and escalation points in the workflow.
2. Match each risk to the right guardrail type: prevention, detection, confirmation, or fallback.
3. Propose the smallest layered guardrail set that materially reduces harm.
4. Check for usability regressions and bypass paths.

Focus on:
- prompt-level rules versus runtime enforcement boundaries
- tool allowlists, argument validation, and approval checkpoints
- structured output validation and refusal handling
- safe fallback behavior when policy confidence is low
- logging and review signals for guardrail misses or overrides

Quality checks:
- verify every guardrail maps to a specific failure path
- avoid relying on prompt wording alone for high-impact controls
- confirm operators can understand and maintain the proposal
- identify likely false-positive or false-negative tradeoffs

Return:
- guardrail architecture by layer
- top risks each guardrail addresses
- expected tradeoffs in usability, latency, and coverage
- recommended tests or evals to validate guardrail behavior
- known bypass or residual-risk paths

Do not recommend blanket blocking when scoped approvals or validation can preserve product usefulness unless explicitly requested by the parent agent.