skills.rest
simplysmartai
simplysmartaiCOMMUNITY
Explore all skills by this author →
GitHub

llm-evaluation

Evaluate LLM applications with automated metrics, human feedback, and LLM-as-judge patterns.

Updated Feb 13, 2026
Search Tags:#testing#llm#benchmarking#metrics#evaluation#ai quality
One-click install
npx skills add https://github.com/simplysmartai/5cypressautomation --skill llm-evaluation-simplysmartai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/simplysmartai/5cypressautomation/tree/main/agents/plugins/llm-application-dev/skills/llm-evaluation
Command: npx skills add https://github.com/simplysmartai/5cypressautomation --skill llm-evaluation-simplysmartai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a structured approach to rigorously evaluate the performance of Large Language Models (LLMs) and AI applications, ensuring quality and reliability.

Core Features & Use Cases

  • Automated Metrics: Utilize metrics like BLEU, ROUGE, and BERTScore for quantitative analysis of text generation.
  • Human Evaluation: Define frameworks for manual assessment of accuracy, coherence, and safety.
  • LLM-as-Judge: Leverage powerful LLMs to evaluate outputs from other models.
  • A/B Testing & Regression: Implement statistical testing for comparing model variants and detecting performance degradation.
  • Use Case: Before deploying a new chatbot version, use this Skill to run a suite of automated and human evaluations against a benchmark dataset to confirm it meets performance targets and hasn't regressed.

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 performance using automated metrics?▼

To evaluate LLM performance, this Skill calculates automated metrics like BLEU, ROUGE, and BERTScore. You can run these quantitative analyses against a benchmark dataset to measure text generation accuracy and detect any performance regression.

What is the LLM-as-judge pattern for AI quality evaluation?▼

The LLM-as-judge pattern leverages powerful LLMs to evaluate outputs from other models. It provides an automated evaluation method for AI application quality, complementing traditional metrics and human feedback frameworks.

Can I use this to run A/B testing and regression checks on my chatbot?▼

Yes, you can use this Skill to implement A/B testing and regression checks on your chatbot. It applies statistical testing to compare model variants and detect performance degradation before deploying a new version.

Does this Skill support human evaluation frameworks for LLM testing?▼

Yes, this Skill supports human evaluation by defining frameworks for manual assessment. You can use it to evaluate LLM outputs for accuracy, coherence, and safety alongside automated benchmarking metrics.

What dependencies do I need to measure BLEU and BERTScore for LLM benchmarking?▼

To measure BLEU and BERTScore for LLM benchmarking, you need Python dependencies like nltk, rouge-score, bert-score, transformers, and scikit-learn. These libraries enable the automated evaluation of text generation metrics.

How do I test LLM accuracy against a set of custom test cases?▼

You can test LLM accuracy against custom test cases by applying this Skill's evaluation strategies. It supports custom evaluation functions alongside automated metrics to measure your AI application's quality and performance targets.

Related Skills

Explore More Software Engineering Skills→
bingeli1379bingeli1379
2

llm-evaluation

Automate LLM evaluation with BLEU, ROUGE, and BERTScore metrics.

Community
Advanced
nimadorostkarnimadorostkar
25

llm-evaluation

Construct evaluation sets and implement LLM-as-judge metrics for regression testing.

Community
Advanced
vuralserhat86vuralserhat86
43

llm_evaluation

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

Community
Advanced
lebsrallebsral
11

ai-monitoring

Monitor AI model performance, drift, and cost in production.

Community
Advanced