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guardrails-safety

Instrument AI agent inputs and outputs with safety and policy guardrails.

7|1|Updated Dec 26, 2025
Search Tags:#safety#policy#guardrails#hallucination-check#content-filtering#pii-detection#guardrail-pipeline
One-click install
npx skills add https://github.com/nexus-labs-automation/agent-observability --skill guardrails-safety
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: guardrails-safety
Source: https://github.com/nexus-labs-automation/agent-observability/tree/main/skills/guardrails-safety
Command: npx skills add https://github.com/nexus-labs-automation/agent-observability --skill guardrails-safety

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Guardrails-safety provides a structured approach to instrument safety checks, detect harmful content, and enforce policy-compliant outputs across AI agent interactions.

Core Features & Use Cases

  • Input guardrails detect prompt injections and filter content before user input is processed.
  • Output guardrails detect PII, hallucinations, and unsafe content, optionally redacting or blocking responses.
  • Guardrail pipeline, metrics, and async guards enable scalable, observable safety across production agents.
  • Use cases include defending production agents from data leakage, maintaining compliance, and reducing unsafe completions.

Quick Start

Integrate the guardrail pipeline into your agent flow to automatically enforce input and output safety checks.

Frequently Asked Questions about guardrails-safety

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

FAQPage Schema
How do I enforce LLM safety and prevent prompt injection in production agents?▼

PII detection and redaction are implemented as output guardrails, which automatically identify and remove sensitive information from AI agent responses before they reach users. This enforces safe-output compliance and defends against data leakage.

How do I check for hallucinations in AI agent outputs?▼

Yes, the guardrail pipeline supports optional async execution and context-rich metadata logging, enabling scalable and observable safety checks across multi-stage workflows and real-time agent interactions without blocking performance.

What is the best way to continuously improve AI content filtering accuracy?▼

The best way to improve content filtering accuracy is using a guardrail pipeline with a built-in feedback loop. This allows continuous accuracy improvements by routing context-rich metadata and metrics from safety checks back into the system.

Can I use guardrails to block harmful content in real-time agent interactions?▼

Yes, guardrails can block harmful content in real-time agent interactions by running input and output safety checks through a modular pipeline. This ensures unsafe completions are filtered or blocked before delivery to the end user.

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