nemo-guardrails

Name: nemo-guardrails
Availability: InStock
Author: hhhi21g

Implement runtime safety checks for LLM applications.

Updated Apr 11, 2026

Search Tags:#fact-checking #jailbreak detection #PII protection #runtime security #LLM safety

Ready to run

npx skills add https://github.com/hhhi21g/HealthCenter --skill nemo-guardrails-hhhi21g

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: nemo-guardrails
Source: https://github.com/hhhi21g/HealthCenter/tree/main/.codex/skills/nemo-guardrails
Command: npx skills add https://github.com/hhhi21g/HealthCenter --skill nemo-guardrails-hhhi21g

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nemoguardrails, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Nemo Guardrails addresses the challenge of securing large language model (LLM) applications by providing a runtime safety framework with features like jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, and toxicity detection.

Core Features & Use Cases

Safety Alignment: Offers a range of safety mechanisms including jailbreak detection and input/output validation.
Fact-Checking and Hallucination Detection: Verifies factual claims and detects potential inaccuracies in the model's outputs.
PII and Toxicity Filtering: Protects sensitive information and filters out harmful content.
Use Case: Ideal for deploying LLMs in production environments where security and reliability are paramount.

Quick Start

To activate Nemo Guardrails, run the following command:

pip install nemoguardrails

Frequently Asked Questions about nemo-guardrails

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

FAQPage Schema

How do I add runtime security and jailbreak detection to my LLM application?▼

Runtime security for LLM applications is achieved by implementing a framework that validates inputs and outputs to prevent jailbreaks. This approach intercepts requests to ensure secure interactions and reliability in production environments.

What is the best way to prevent PII leakage and toxicity in LLM outputs?▼

Preventing PII leakage and toxicity in LLM outputs requires a runtime filtering mechanism that scans generated text. This framework identifies and redacts sensitive information while blocking harmful content before it reaches the end user.

How do I implement fact-checking and hallucination detection for large language models?▼

Fact-checking and hallucination detection for large language models involve implementing a runtime validation layer. This layer verifies factual claims against defined criteria and flags potential inaccuracies during the generation process to maintain output reliability.

Does the nemoguardrails framework work for production LLM environments?▼

Yes, the nemoguardrails framework is specifically designed for production LLM environments where security and reliability are paramount. It provides the necessary runtime safety checks, including input validation and toxicity detection, to secure deployed applications.

How do I set up LLM safety alignment and input validation for my application?▼

Setting up LLM safety alignment and input validation requires installing the nemoguardrails runtime framework. Once integrated, it enforces safety mechanisms by intercepting and evaluating user prompts before they are processed by the model.