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llm-evaluation

Evaluate LLM applications with automated metrics and human feedback.

2|Updated Jan 18, 2026
Search Tags:#testing#quality assurance#llm#benchmarking#metrics#evaluation
One-click install
npx skills add https://github.com/as4584/antigravity-skills --skill llm-evaluation-as4584
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/as4584/antigravity-skills/tree/main/agents-wshobson/plugins/llm-application-dev/skills/llm-evaluation
Command: npx skills add https://github.com/as4584/antigravity-skills --skill llm-evaluation-as4584

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nltk, rouge_score, bert_score, transformers, detoxify, openai, scipy, numpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the critical need to systematically measure and improve the performance of Large Language Model (LLM) applications, ensuring quality, reliability, and effectiveness.

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 more powerful LLMs to evaluate outputs, enabling pointwise and pairwise comparisons.
  • A/B Testing & Regression Detection: Provides frameworks for statistical testing and continuous monitoring of performance regressions.
  • Use Case: A team developing a customer support chatbot can use this Skill to automatically evaluate new prompt variations against a set of test cases, compare their performance using automated metrics and human feedback, and detect any performance degradation before deploying to production.

Quick Start

Use the llm-evaluation skill to evaluate your model's accuracy and BLEU score against provided 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 like BLEU and ROUGE?▼

You evaluate LLM performance using automated metrics like BLEU, ROUGE, and BERTScore to quantitatively assess generated text against test cases. This Skill implements these strategies to measure accuracy and text similarity.

What is the LLM-as-judge approach for benchmarking AI applications?▼

The LLM-as-judge approach leverages more powerful LLMs to evaluate outputs, enabling pointwise and pairwise comparisons. It provides a systematic way to qualitatively assess AI application quality without relying solely on manual human review.

Can I use human evaluation frameworks to measure chatbot coherence and fluency?▼

Yes, you can use human evaluation frameworks to measure chatbot quality across dimensions like accuracy, coherence, relevance, fluency, safety, and helpfulness. This Skill facilitates structured manual assessment to achieve comprehensive qualitative reviews.

Does this LLM evaluation method work with A/B testing and regression detection?▼

Yes, this LLM evaluation method works with A/B testing and regression detection by providing frameworks for statistical testing and continuous monitoring. It helps teams detect performance degradation before deploying prompt variations to production.

Do I need transformers and detoxify installed to run LLM toxicity metrics?▼

You need transformers and detoxify installed to run LLM toxicity metrics, as the Skill lists them as dependencies. These libraries provide the necessary models to assess output safety dimensions during the quantitative evaluation process.

What is the best way to compare LLM prompt variations before production deployment?▼

The best way to compare LLM prompt variations is using automated metrics and human feedback through A/B testing. This framework enables statistical testing to identify regressions and ensure quality before production deployment.

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