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llm-testing

Test and evaluate LLM applications with BLEU, ROUGE-L, and LLM-as-judge.

1|Updated Jan 6, 2026
Search Tags:#testing#quality assurance#llm#benchmarking#evaluation#red teaming
One-click install
npx skills add https://github.com/hyukudan/ai-skills --skill llm-testing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-testing
Source: https://github.com/hyukudan/ai-skills/tree/main/examples/skills/llm-testing
Command: npx skills add https://github.com/hyukudan/ai-skills --skill llm-testing

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of reliably evaluating and testing Large Language Models (LLMs), which are inherently probabilistic and subjective. It provides frameworks to ensure LLM outputs meet desired quality standards.

Core Features & Use Cases

  • Evaluation Metrics: Implements standard metrics like BLEU, ROUGE-L, and semantic similarity for quantitative assessment.
  • LLM-as-Judge: Leverages LLMs themselves to evaluate responses, enabling scalable quality checks.
  • Regression & Safety Testing: Includes tools for behavioral testing, snapshotting, and red teaming to catch regressions and identify safety vulnerabilities.
  • Use Case: A team developing a customer service chatbot can use this Skill to automatically test new model versions against a suite of prompts, ensuring that responses remain accurate, helpful, and safe, and that performance doesn't degrade over time.

Quick Start

Use the llm-testing skill to run regression tests on your model with the provided test suite.

Frequently Asked Questions about llm-testing

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

FAQPage Schema
How do I evaluate LLM outputs using quantitative metrics?▼

You can evaluate LLM outputs quantitatively by calculating standard text similarity scores like BLEU, ROUGE-L, and semantic similarity. These metrics provide baseline measurements to ensure your model's responses meet desired quality standards.

What is the best way to scale quality assurance for LLM applications?▼

Scaling LLM quality assurance is best achieved using the LLM-as-judge approach, where an LLM evaluates responses. This enables scalable quality checks without manual review to verify accuracy and helpfulness.

How do I perform regression testing on an LLM chatbot?▼

Perform LLM regression testing using behavioral testing and snapshotting approaches. This catches performance degradations by automatically comparing new model versions against a suite of established prompts.

Can I use red teaming to test LLM safety vulnerabilities?▼

Yes, you can test LLM safety vulnerabilities through red teaming and adversarial prompts. This process identifies safety weaknesses by intentionally challenging the model with malicious or edge-case inputs.

Does this LLM testing framework require specific external dependencies?▼

No, this LLM testing framework requires no external dependencies to run. It provides comprehensive testing strategies and tools entirely through its internal scripts and references.

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