llm-evaluation

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

Updated Jan 20, 2026

Search Tags:#benchmarking #metrics #NLP #llm-evaluation #human-evaluation #AI-assessment

Ready to run

npx skills add https://github.com/ollieb89/ugro --skill llm-evaluation-ollieb89

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/ollieb89/ugro/tree/main/.windsurf/skills/llm-evaluation
Command: npx skills add https://github.com/ollieb89/ugro --skill llm-evaluation-ollieb89

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 and benchmark LLM outputs using automated metrics, human judgments, and benchmarking pipelines, enabling reproducible improvements in AI applications.

Core Features & Use Cases

Automated Metrics: Compute standard NLP metrics (BLEU, ROUGE, METEOR, BERTScore) and model confidence (perplexity) to quantify output quality.
Human Evaluation: Systematic human rating across accuracy, coherence, relevance, fluency, safety, and helpfulness.
LLM-as-Judge: Use larger models to judge outputs via pointwise, pairwise, and reference-based approaches to guide model refinement.
Benchmarking & Regression: Run end-to-end benchmarks across datasets and track progress over time.

Quick Start

Run the provided evaluation harness on your model to generate a benchmark report from the included sample tests and data.

Frequently Asked Questions about llm-evaluation

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

FAQPage Schema

How do I benchmark LLM performance using automated metrics?▼

Automated metrics like BLEU, ROUGE, METEOR, and BERTScore quantify LLM output quality by comparing generated text against reference outputs. This Skill provides harnesses to compute these metrics end-to-end, enabling reproducible measurement of model improvements across benchmark datasets.

What's the difference between automated metrics and LLM-as-Judge evaluation?▼

Automated metrics use statistical formulas to score outputs against references; LLM-as-Judge uses larger models to evaluate via pointwise, pairwise, and reference-free approaches. This Skill implements both methods so you can combine quantitative rigor with semantic judgment for comprehensive assessment.

Can I run human evaluation workflows alongside automated metrics?▼

Yes. This Skill structures human evaluation across six dimensions—accuracy, coherence, relevance, fluency, safety, and helpfulness—and integrates them with automated metrics and LLM-as-Judge approaches in a single benchmarking pipeline for reproducible, multi-method assessment.

How do I track LLM performance improvements over time?▼

The Skill's benchmarking and regression workflows run end-to-end evaluation across datasets and log results, enabling you to track progress over model iterations and detect regressions systematically without manual re-evaluation.

What Python libraries and NLP tools does this evaluation framework require?▼

The Skill requires Python and optional NLP tools to execute evaluation harnesses, sample datasets, and benchmarking pipelines. It has no external dependencies listed, so you bring your standard Python environment and NLP stack to run the provided scripts and metrics.

Can I evaluate model confidence alongside output quality?▼

Yes. Beyond output metrics, this Skill computes perplexity to measure model confidence, giving you both quality assessment and confidence scoring in a single evaluation pass for deeper insight into model behavior.