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llm-evaluation

Evaluate LLM applications using automated metrics, human feedback, and benchmarking.

1|Updated Feb 19, 2026
Search Tags:#testing#llm-as-judge#llm#benchmarking#metrics#evaluation#human evaluation
One-click install
npx skills add https://github.com/yusufcmg/Antigravity-Agents-Workflows --skill llm-evaluation-yusufcmg
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/yusufcmg/Antigravity-Agents-Workflows/tree/main/.agent/skills/ai/llm-evaluation
Command: npx skills add https://github.com/yusufcmg/Antigravity-Agents-Workflows --skill llm-evaluation-yusufcmg

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nltk, rouge-score, bert-score, scikit-learn, scipy, langsmith, transformers, detoxify, anthropic, pydantic, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of objectively measuring and improving the performance of Large Language Models (LLMs) by providing a structured framework for evaluation.

Core Features & Use Cases

  • Automated Metrics: Implement quantitative measures like BLEU, ROUGE, and BERTScore for text generation and classification tasks.
  • Human Evaluation: Define dimensions and guidelines for manual assessment of response quality.
  • LLM-as-Judge: Leverage powerful LLMs to evaluate outputs from other models.
  • A/B Testing & Regression: Statistically compare model variants and detect performance degradation.
  • Use Case: You've developed a new prompt for your customer support chatbot. Use this Skill to run automated metrics and human evaluations on responses generated by the new prompt versus the old one to ensure it's an improvement.

Quick Start

Use the llm-evaluation skill to evaluate your model's responses against a set of test cases using the accuracy and bertscore metrics.

Frequently Asked Questions about llm-evaluation

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

FAQPage Schema
How do I evaluate LLM performance using automated metrics like ROUGE and BERTScore?▼

To evaluate LLM performance, you can compute automated metrics like ROUGE and BERTScore against test cases to quantitatively measure text generation quality and classification accuracy.

What is the LLM-as-judge approach for measuring AI application quality?▼

The LLM-as-judge approach measures AI application quality by leveraging a powerful LLM to evaluate and grade the outputs generated by other models, providing an automated qualitative assessment.

Can I use LangSmith for A/B testing and regression detection in LLM applications?▼

Yes, you can use LangSmith alongside statistical libraries like SciPy to A/B test model variants, statistically compare outputs, and detect performance degradation in LLM applications.

What's the best way to set up human evaluation guidelines for an LLM chatbot?▼

The best way to set up human evaluation is to define specific assessment dimensions and scoring guidelines for manual review, ensuring objective measurement of response quality for your chatbot.

Do I need detoxify and transformers to benchmark toxicity in LLM outputs?▼

Yes, you need the detoxify and transformers libraries to benchmark toxicity levels and classify text safety, allowing you to evaluate harmful content generation within LLM outputs.

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