Evaluate model responses as safe or unsafe using Llama-Guard templates.
npx skills add https://github.com/wuyoscar/ISC-Bench --skill aiml-llamaguard-eval
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
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.
Run the test harness in this skill directory to validate the Llama-Guard templates against safe and unsafe responses.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
qcmuuClassifies LLM user prompts and outputs into predefined safety categories for gating logic.
xqinagAnalyze ML experiment results to compute statistics and generate comparison tables.
petrkindlmannValidate AI features by testing prompts, tool calls, and nondeterministic outputs.
wuyyyyyouFilter user-generated content with model armor templates and sanitization rules.
