skills.rest
carlopezzuto
carlopezzutoCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

llm-evaluation

Compute automated metrics and capture human feedback for LLM evaluation.

3|1|Updated Nov 5, 2025
Search Tags:#benchmarking#metrics#nlp#ab-testing#llm-evaluation#human-feedback#model-assessment
One-click install
npx skills add https://github.com/carlopezzuto/agents --skill llm-evaluation-carlopezzuto
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/carlopezzuto/agents/tree/main/.claude/skills/llm-evaluation
Command: npx skills add https://github.com/carlopezzuto/agents --skill llm-evaluation-carlopezzuto

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill provides a structured framework to evaluate LLM systems using automated metrics, human feedback, and benchmarking, enabling consistent measurement of quality across iterations.

Core Features & Use Cases

  • Automated Metrics: compute standard NLP metrics (BLEU, ROUGE, METEOR, BERTScore) and task-specific scores for text generation, classification, and retrieval.
  • Human Evaluation: formalize dimensions such as accuracy, relevance, coherence, safety, and usefulness with scalable annotation practices.
  • A/B Testing & Benchmarking: support controlled comparisons, statistical analysis, and baselining across model variants and prompts.
  • End-to-end Evaluation Harness: integrate metrics, human judgments, and reporting into a repeatable evaluation workflow.

Quick Start

Provide a model and a test suite; run the evaluation harness to generate a comprehensive report.

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 NLP metrics?▼

To evaluate LLM performance, you compute standard NLP metrics like BLEU, ROUGE, METEOR, and BERTScore. This skill provides an evaluation harness to calculate these task-specific scores for text generation, classification, and retrieval systems.

What is the best way to compare LLM model variants and prompt versions?▼

The best way to compare LLM model variants and prompt versions is through A/B testing and benchmarking. This skill supports controlled comparisons and statistical analysis to detect regressions, validate improvements, and establish baselines.

How do I formalize human feedback for LLM evaluation?▼

You formalize human feedback for LLM evaluation by capturing annotations across specific dimensions. This skill helps structure human judgments for accuracy, relevance, coherence, safety, and usefulness into a scalable annotation practice.

Can I integrate LLM evaluation metrics into a repeatable reporting workflow?▼

Yes, you can integrate LLM evaluation metrics into a repeatable workflow. This skill provides an end-to-end evaluation harness that combines automated metrics, human judgments, and reporting to generate comprehensive performance reports.

When do I need to run an LLM evaluation harness?▼

You need to run an LLM evaluation harness when applying model comparisons across deployment scenarios. It helps detect regressions, validate prompt improvements, and build performance baselines over time using test suites.

Related Skills

Explore More Data & Analytics Skills→
apassuelloapassuello

llm-evaluation

Evaluate LLM outputs with automated metrics, human judgments, and benchmarking.

Community
Advanced
TimHL5TimHL5

benchmark

Monitors web page performance metrics and detects deviations from established baselines.

Community
Advanced
ollieb89ollieb89

llm-evaluation

Evaluate LLM outputs with automated metrics and LLM-as-Judge workflows.

Community
Advanced
ondataondata
57

data-exploration

Analyze CKAN portal datasets and CSV files for quality and trends.

Official
Intermediate