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 Owl-Listener/ai-design-skills --skill guardrail-designgit clone --depth 1 https://github.com/Owl-Listener/ai-design-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/owl-listener/ai-design-skills/guardrail-design)<a href="https://agentmods.dev/skills/owl-listener/ai-design-skills/guardrail-design"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/guardrail-design/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/owl-listener/ai-design-skills/guardrail-design"><img src="https://agentmods.dev/badge/skills/owl-listener/ai-design-skills/guardrail-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk 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 | $0.00017 | $0.00579 |
| Opus 5 | $0.00009 | $0.00290 |
| Sonnet 5 | $0.00003 | $0.00116 |
| Haiku 4.5 | $0.00002 | $0.00058 |
Grade A, and why
guardrail-design 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 — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Guardrail 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
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 · 37 lines · 17 tokens per session scan A 84e996962baf
guardrail-design is a skill published in the GitHub repository Owl-Listener/ai-design-skills (173 stars, last pushed 3mo ago), licensed MIT. It adds 17 tokens to every session and 579 once invoked, about $0.0001 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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