aiml-llamaguard-eval

Evaluate model responses as safe or unsafe using Llama-Guard templates.

934|152|Updated Mar 1, 2026

Search Tags:#testing #templates #validation #evaluation #guardrails #ai-safety

Ready to run

npx skills add https://github.com/wuyoscar/ISC-Bench --skill aiml-llamaguard-eval

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: aiml-llamaguard-eval
Source: https://github.com/wuyoscar/ISC-Bench/tree/main/templates/aiml_guard
Command: npx skills add https://github.com/wuyoscar/ISC-Bench --skill aiml-llamaguard-eval

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The AIML Guard evaluation template provides a reproducible framework to assess how frontier language models respond to safety tasks by distinguishing safe vs. unsafe outputs and validating guardrails in a controlled workflow.

Core Features & Use Cases

Structured test harness and validation rules (including placeholder checks, minimum response length, and deterministic classification) to ensure consistent safety assessments.
Reusable templates for prompt construction, response evaluation, and result aggregation across experiments.
Use Case: researchers can rapidly compare multiple models or settings (e.g., different guardrails) on a common evaluation suite.

Quick Start

Run the test harness in this skill directory to validate the Llama-Guard templates against safe and unsafe responses.

Frequently Asked Questions about aiml-llamaguard-eval

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

FAQPage Schema

How do I evaluate LLM responses for AI safety using Llama-Guard?▼

You can evaluate AI safety by applying a reproducible guard-rail template to Llama-Guard style prompts, enforcing structured test cases and validation rules to classify model responses as safe or unsafe.

What is a guardrail validation template and how does it work for LLM testing?▼

A guardrail validation template is a structured test harness for LLM testing that applies deterministic classification rules, placeholder checks, and minimum response length constraints to ensure consistent safety assessments.

How do I set up reproducible AI safety evaluation workflows for multiple models?▼

You can set up reproducible AI safety evaluation workflows by using reusable templates for prompt construction, response evaluation, and result aggregation to rapidly compare multiple models or guardrail settings on a common suite.

Can I compare different guardrail settings across language models consistently?▼

Yes, you can compare different guardrail settings across language models consistently by running a common evaluation suite with structured validation rules and a minimal dependency surface to ensure reproducible results.

Do I need external dependencies to run Llama-Guard safety classifications?▼

No, you do not need external dependencies to run Llama-Guard safety classifications because the evaluation template enforces a minimal dependency surface to ensure consistent and reproducible safety assessment results.