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llm-evaluation

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

6|2|Updated Oct 19, 2025
Search Tags:#testing#quality assurance#llm#benchmarking#metrics#evaluation
One-click install
npx skills add https://github.com/amurata/cc-tools --skill llm-evaluation-amurata
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/amurata/cc-tools/tree/main/plugins/llm-application-dev/skills/llm-evaluation
Command: npx skills add https://github.com/amurata/cc-tools --skill llm-evaluation-amurata

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of systematically measuring and improving the quality and performance of Large Language Model (LLM) applications.

Core Features & Use Cases

  • Automated Metrics: Utilizes metrics like BLEU, ROUGE, BERTScore, Accuracy, Precision, Recall, F1, MRR, and NDCG for quantitative assessment.
  • Human Evaluation: Facilitates manual assessment across dimensions such as accuracy, coherence, relevance, fluency, safety, and helpfulness.
  • LLM-as-Judge: Leverages powerful LLMs to evaluate outputs from other models, enabling scalable qualitative assessment.
  • A/B Testing & Regression Detection: Provides frameworks for statistical comparison of model variants and continuous monitoring for performance degradation.
  • Use Case: You have developed a new chatbot and want to ensure its responses are accurate and helpful before deploying it to users. This Skill allows you to run automated tests, compare different prompt strategies using LLM-as-Judge, and set up a system to catch performance drops in production.

Quick Start

Use the llm-evaluation skill to evaluate your model's accuracy and BLEU score against a set of test cases.

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?▼

Evaluate LLM application performance by calculating automated quantitative metrics such as BLEU, ROUGE, BERTScore, Accuracy, Precision, Recall, F1, MRR, and NDCG against test cases.

What is the best way to scale qualitative LLM evaluation without manual review?▼

Scale qualitative LLM evaluation by using the LLM-as-Judge approach, which leverages powerful LLMs to evaluate outputs from other models, enabling scalable qualitative assessment without manual review.

Can I set up regression detection to catch LLM performance drops in production?▼

Yes, you can catch LLM performance drops in production by establishing a robust evaluation framework that provides continuous monitoring and regression detection for performance degradation.

Does LLM benchmarking require human evaluation for dimensions like safety and coherence?▼

LLM benchmarking can incorporate human evaluation to manually assess dimensions like accuracy, coherence, relevance, fluency, safety, and helpfulness, complementing automated quantitative metrics.

How do I run A/B testing to compare different LLM prompt strategies?▼

Run A/B testing for LLM prompt strategies by using frameworks that provide statistical comparison of model variants, allowing you to evaluate and compare different approaches systematically.

What libraries do I need to calculate NLP metrics for LLM testing?▼

Calculating NLP metrics for LLM testing requires libraries for NLP metrics, statistical analysis, and potentially LLM interaction to perform judge-based evaluations.

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