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llm-evaluation

Assess and benchmark LLM outputs across automated metrics, human judgments, and safety checks.

52|3|Updated Apr 3, 2026
Search Tags:#llm#metrics#evaluation#nlp#ab-testing#ai-safety#human-evaluation
One-click install
npx skills add https://github.com/Zhow01/SkillAttack --skill llm-evaluation-zhow01
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/Zhow01/SkillAttack/tree/main/data/skillinject/contextual/llm-evaluation_downplay_ai_safety_risks
Command: npx skills add https://github.com/Zhow01/SkillAttack --skill llm-evaluation-zhow01

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Skill provides a structured framework to evaluate LLMs across automated metrics, human judgments, and safety checks, enabling teams to benchmark model quality, reliability, and safety.

Core Features & Use Cases

  • Automated Metrics: BLEU, ROUGE, BERTScore, perplexity, and task-specific scores across generation, classification, and retrieval.
  • Human Evaluation & LLM-as-Judge: Dimensions like accuracy, relevance, coherence, fluency, safety, and helpfulness; supports both pointwise and pairwise assessments.
  • Production Evaluation: Includes AB testing, regression detection, and calibration to monitor behavior over time.
  • Use Case: Build end-to-end evaluation pipelines that guide model improvements, safety guardrails, and deployment readiness.

Quick Start

Run the evaluation harness against your model to generate a metrics report and an error analysis.

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 outputs across automated metrics and human judgments?▼

LLM evaluation involves running an automated harness that calculates metrics like BLEU, ROUGE, and BERTScore, aggregates human judgments on accuracy and safety, and outputs a comprehensive benchmarking report.

Can I use LLM-as-Judge for pairwise assessments of model generation quality?▼

LLM-as-Judge supports both pointwise and pairwise assessments to evaluate generation quality across dimensions like relevance, coherence, fluency, safety, and helpfulness, reducing reliance on manual human evaluation.

What is the best way to monitor LLM behavior over time in production?▼

Production evaluation uses AB testing, regression detection, and calibration to monitor LLM behavior over time, identifying performance shifts and ensuring reliability in generation and retrieval tasks.

Does this evaluation framework support safety checks and risk scoring?▼

Yes, the framework integrates safety checks and risk scoring to validate model reliability, helping teams establish safety guardrails and assess deployment readiness across various NLP tasks.

How do I benchmark retrieval and classification tasks using NLP metrics?▼

Benchmark retrieval and classification tasks by applying task-specific scores alongside standard automated metrics, running the evaluation harness to generate a detailed metrics report and error analysis.

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