llm-evaluation

Implement LLM evaluation strategies with automated metrics and human feedback.

3|1|Updated Dec 30, 2025

Search Tags:#metrics #evaluation #llm-judge #human-evaluation

Ready to run

npx skills add https://github.com/48Nauts-Operator/opencode-baseline --skill llm-evaluation-48nauts-operator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/48Nauts-Operator/opencode-baseline/tree/main/.opencode/skill/llm-dev/llm-evaluation
Command: npx skills add https://github.com/48Nauts-Operator/opencode-baseline --skill llm-evaluation-48nauts-operator

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking.

Core Features & Use Cases

Automated metrics (BLEU, ROUGE, METEOR, BERTScore, Perplexity)
Human evaluation dimensions (Accuracy, Coherence, Relevance, Fluency, Safety)
LLM-as-judge patterns (single output, pairwise)

Quick Start

Create an evaluation suite with BLEU, ROUGE, and human ratings for a QA dataset.