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llm-evaluation

Evaluate LLM outputs with automated metrics and human judgments.

Updated Apr 12, 2026
Search Tags:#llm#metrics#evaluation#nlp#ab-testing#judgment#human-evaluation
One-click install
npx skills add https://github.com/BoraPerusic/agents --skill llm-evaluation-boraperusic
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/BoraPerusic/agents/tree/main/skills/to%20try/llm-evaluation
Command: npx skills add https://github.com/BoraPerusic/agents --skill llm-evaluation-boraperusic

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Evaluate LLM outputs across automated metrics, human judgments, and verdicts to identify strengths and weaknesses in model performance.

Core Features & Use Cases

  • Automated Metrics: BLEU, ROUGE, METEOR, BERTScore, perplexity, and more to quantify output quality.
  • Human Evaluation: Structured human judgments across accuracy, relevance, coherence, safety, and helpfulness.
  • LLM-as-Judge: Use LLMs to assess other model outputs with pointwise or pairwise evaluation strategies.
  • A/B Testing & Regression: Track performance changes over time and compare model variants under controlled experiments.

Quick Start

Provide a ready-to-run evaluation plan for a selected LLM, including automated metrics, human evaluation criteria, and a comparison protocol.

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 outputs using automated metrics and human judgments?▼

You can evaluate LLM outputs by computing automated metrics like BLEU, ROUGE, and BERTScore, alongside structured human judgments on accuracy, relevance, and safety. This approach identifies specific strengths and weaknesses in model performance.

What is the LLM-as-judge approach for evaluating model outputs?▼

The LLM-as-judge approach uses LLMs to assess other model outputs through pointwise or pairwise evaluation strategies. It provides automated verdicts on output quality, complementing traditional metrics and human evaluation frameworks.

How do I compare model variants and track performance changes over time?▼

You can compare model variants and track performance changes using basic A/B testing and regression analysis. This allows you to validate iterative improvements under controlled experiments in production or research settings.

Can I use this to set up an evaluation plan for comparing different prompts?▼

Yes, you can generate a ready-to-run evaluation plan for comparing prompts or models. The plan includes automated metric computation, human evaluation criteria, and a structured comparison protocol to validate iterative improvements.

What automated NLP metrics are available to quantify LLM output quality?▼

Available automated NLP metrics include BLEU, ROUGE, METEOR, BERTScore, and perplexity. These metrics quantify output quality to help you systematically evaluate LLM performance across different models or prompt variations.

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