skills.rest
LuisSambrano
LuisSambranoCOMMUNITY
Explore all skills by this author →
𝕏@luissambrano_uxGitHub

llm-eval

Evaluate LLM applications using automated metrics and human feedback.

7|2|Updated Jan 28, 2026
Search Tags:#testing#llm#benchmarking#metrics#evaluation#ai quality
One-click install
npx skills add https://github.com/LuisSambrano/antigravity-config --skill llm-eval
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-eval
Source: https://github.com/LuisSambrano/antigravity-config/tree/main/skills/2-ai/llm-eval
Command: npx skills add https://github.com/LuisSambrano/antigravity-config --skill llm-eval

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the critical need for robust evaluation of Large Language Model (LLM) applications, ensuring quality, performance, and reliability.

Core Features & Use Cases

  • Automated Metrics: Utilize metrics like BLEU, ROUGE, BERTScore, Accuracy, Precision, Recall, F1, MRR, and NDCG for quantitative assessment.
  • Human Evaluation: Incorporate human judgment on dimensions such as accuracy, coherence, relevance, fluency, safety, and helpfulness.
  • LLM-as-Judge: Leverage powerful LLMs to evaluate outputs, enabling scalable qualitative assessment.
  • A/B Testing & Regression: Facilitate controlled experiments and continuous monitoring for performance regressions.
  • Use Case: When deploying a new chatbot, use this Skill to systematically measure its response quality against a benchmark dataset using a combination of automated metrics and human review, ensuring it meets performance targets before going live.

Quick Start

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

Frequently Asked Questions about llm-eval

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I measure LLM performance using automated NLP metrics?▼

Evaluate LLM application quality by combining automated metrics with human feedback and LLM-as-judge frameworks. This ensures your model's responses meet accuracy, coherence, and safety targets before deployment.

What is the best way to combine automated metrics and human evaluation for AI testing?▼

Combine automated metrics and human evaluation by applying this Skill's comprehensive evaluation framework. It calculates quantitative scores using libraries like nltk and bert_score while incorporating human judgment on dimensions like coherence, safety, and helpfulness.

Do I need specific Python libraries to benchmark LLM applications?▼

Yes, LLM benchmarking requires Python libraries including nltk, rouge_score, bert_score, transformers, detoxify, openai, scikit-learn, scipy, and numpy to compute quantitative and qualitative machine learning scores.

Can I use a large language model as a judge to evaluate chatbot responses?▼

Yes, you can leverage powerful LLMs as judges to evaluate chatbot outputs, enabling scalable qualitative assessment. This approach works alongside automated metrics like BERTScore and MRR for comprehensive benchmarking.

How do I run A/B testing and regression monitoring for LLM applications?▼

Facilitate A/B testing and regression monitoring for LLM applications by running controlled experiments and continuous performance checks. This Skill tracks metric regressions to ensure your AI application maintains its quality targets over time.

Related Skills

Explore More Software Engineering Skills→
Ritabrata-ChakrabortyRitabrata-Chakraborty

scikit-learn

Automate machine learning workflows with scikit-learn preprocessing, model selection, and evaluation.

Community
Advanced
bugrabilgebugrabilge
10

agent-evaluation

Design benchmark test suites for evaluating LLM agent reliability.

Community
Advanced
wuyoscarwuyoscar
934

aiml-detoxify-benchmark

Automate cross-category toxicity benchmarking for Detoxify classifiers.

Community
Advanced
yusufcmgyusufcmg
1

llm-evaluation

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

Community
Advanced