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llm-evaluation

Implement automated metrics, human feedback, and LLM-as-Judge evaluations for LLM applications.

38.6k|4.1k|Updated Jul 24, 2025
Search Tags:#testing#quality assurance#llm#benchmarking#metrics#evaluation
One-click install
npx skills add https://github.com/wshobson/agents --skill llm-evaluation-wshobson
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/wshobson/agents/tree/main/plugins/llm-application-dev/skills/llm-evaluation
Command: npx skills add https://github.com/wshobson/agents --skill llm-evaluation-wshobson

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a comprehensive framework for evaluating the performance and quality of Large Language Model (LLM) applications, ensuring reliability and identifying areas for improvement.

Core Features & Use Cases

  • Automated Metrics: Implement standard metrics like BLEU, ROUGE, BERTScore, Accuracy, Precision, Recall, F1, MRR, and NDCG for quantitative assessment.
  • Human Evaluation: Define dimensions and guidelines for manual review of LLM outputs.
  • LLM-as-Judge: Leverage powerful LLMs to evaluate responses based on predefined criteria.
  • A/B Testing & Regression Detection: Facilitate controlled experiments and monitor performance over time.
  • Use Case: You've developed a new chatbot and need to ensure its responses are accurate, relevant, and safe before deploying it to users. This Skill allows you to systematically test its performance against various benchmarks and human feedback.

Quick Start

Use the llm-evaluation skill to create an evaluation suite with accuracy and BLEU metrics for your model.

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 application performance using automated metrics?▼

To evaluate LLM application performance, you can implement automated metrics like BLEU, ROUGE, BERTScore, Accuracy, Precision, Recall, F1, MRR, and NDCG for quantitative scoring and systematic quality measurement.

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

The LLM-as-Judge approach evaluates responses by leveraging powerful LLMs to assess outputs based on predefined criteria, providing a scalable way to perform qualitative assessment without relying solely on manual human review.

Can I use this framework for A/B testing and regression detection in chatbots?▼

Yes, you can use this framework for A/B testing and regression detection in chatbots to facilitate controlled experiments, monitor performance over time, and ensure responses remain accurate before deployment.

What's the best way to combine human evaluation with quantitative benchmarking?▼

The best way to combine human evaluation with quantitative benchmarking is to define dimensions and guidelines for manual review alongside automated metrics, ensuring both qualitative assessment and comparative analysis are captured.

Do I need to install transformers and detoxify to measure LLM safety?▼

Yes, you need to install dependencies like transformers and detoxify to support toxicity detection and safety measurements, ensuring your LLM applications are rigorously evaluated for safe deployment.

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