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LauraFlorentin
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guardrails

Inspect inputs and outputs to enforce safety and policy compliance.

1|Updated Feb 11, 2026
Search Tags:#safety#input-validation#policy#guardrails#sandboxing#output-filtering
One-click install
npx skills add https://github.com/LauraFlorentin/skills-marketplace --skill guardrails-lauraflorentin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: guardrails
Source: https://github.com/LauraFlorentin/skills-marketplace/tree/main/agentic-skills/skills/guardrails
Command: npx skills add https://github.com/LauraFlorentin/skills-marketplace --skill guardrails-lauraflorentin

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Guardrails are the firewall of an AI system. They sit between the user and the agent (Input Guardrail) and between the agent and the user (Output Guardrail). They enforce policy, security, and tone. Unlike the main agent, which tries to be helpful, the guardrail tries to be safe and compliant.

Core Features & Use Cases

  • Input Guardrail: validate and filter prompts before processing.
  • Output Guardrail: monitor and redact or block unsafe responses.
  • Sandboxing: isolate and constrain agent actions to safe boundaries.

Quick Start

Configure the guardrail to validate user prompts before processing and sanitize the generated responses to enforce safety policies.

Frequently Asked Questions about guardrails

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prevent jailbreaks and PII leakage in AI interactions?▼

You can prevent jailbreaks and PII leakage by applying input and output guardrails that inspect prompts and responses, enforcing safety policies before and after agent processing.

What is the best way to enforce policy compliance on LLM outputs?▼

The best way to enforce policy compliance is implementing output guardrails that monitor, redact, or block unsafe AI-generated responses before they reach the user.

How do I validate user prompts before processing them in an AI agent?▼

You validate user prompts by configuring an input guardrail to inspect and filter incoming requests, ensuring they comply with safety policies before the agent processes them.

How does sandboxing work for constraining AI agent actions?▼

Sandboxing isolates and constrains agent actions to safe boundaries, preventing unauthorized operations and ensuring the agent operates within a secure, policy-compliant environment.

Can I use a modular implementation pattern for input validation and output filtering?▼

Yes, you can apply a modular implementation pattern to separately configure input validation for incoming prompts and output filtering for generated responses, ensuring comprehensive safety coverage.

When do I need output moderation for AI-generated responses?▼

You need output moderation when your AI system handles sensitive data or user-facing content, requiring response sanitization to prevent unsafe content, PII leakage, and policy violations.

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