About guardrail-design
An agent skill for teams defining behavioral boundaries in AI products. It treats content, action, tone, scope, and confidence limits as product decisions, with guidance for specifying and communicating them.
Key features
- 01Defines five categories of guardrails
- 02Records rules, rationale, and edge cases
- 03Plans refusals, alternatives, and escalation
- 04Sets guardrail severity levels
- 05Creates guardrail test scenarios
Use cases
- 01Define behavioral boundaries for an AI product
- 02Plan user-facing responses when guardrails activate
- 03Design edge-case tests for guardrails
Install
Copy the command and run it in a terminal.
$
npx skills add https:/ / github.com/ owl-listener/ ai-design-skills --skill guardrail-designAfter installing, type/guardrail-designin Claude Code to confirm it loaded.
Details
From the project READMEGuardrail Design
Guardrails are the behavioral boundaries that define what an AI product will and won't do. They're not just safety constraints — they're design decisions that shape the entire user experience.
Types of Guardrails
- Content guardrails: What topics the AI will and won't discuss. What it generates and refuses to generate.
- Action guardrails: What the AI can do in the world — send emails, make purchases, delete data — and what requires human approval.
- Tone guardrails: How the AI communicates — what language it uses, how formal or casual, when it's direct vs. diplomatic.
- Scope guardrails: What the AI considers in and out of scope for its role. A coding assistant shouldn't give medical advice.
- Confidence guardrails: When the AI should express uncertainty, hedge, or refuse rather than guessing.
Designing Guardrails as Product Decisions
Every guardrail is a product decision with tradeoffs:
- Too strict: The product feels limited, frustrating, and paternalistic. Users route around the guardrails.
- Too loose: The product causes harm, loses trust, and creates liability.
- Inconsistent: Users can't predict what the AI will and won't do, eroding trust. The goal is guardrails that feel like good judgment, not arbitrary restrictions.
Guardrail Specification
For each guardrail, define:
- What it prevents: The specific behavior or output being constrained
- Why it exists: The harm it prevents or the value it protects
- How it manifests: What the user sees when the guardrail activates (refusal message, alternative suggestion, escalation)
- Edge cases: Grey areas where the guardrail might be too strict or too loose
- Override conditions: Whether and how the guardrail can be relaxed (admin settings, user confirmation, context-dependent)
Guardrail Communication
How the AI communicates a guardrail matters as much as the guardrail itself:
- Transparent refusal: "I can't help with that because..." — honest about the boundary
- Redirective refusal: "I can't do X, but I can help you with Y" — offering alternatives
- Silent guardrail: The AI steers away from the boundary without mentioning it
- Escalation: "This needs a human to review" — handing off rather than refusing
Design Artefacts
- Guardrail specification table: Category | Rule | Rationale | User Experience | Edge Cases
- Refusal message templates per guardrail type
- Guardrail severity tiers (hard block vs. soft warning vs. nudge)
- Testing scenarios for each guardrail
Comments
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