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llm-evaluation

Automate LLM evaluation with metrics, human feedback, and benchmarking templates.

Updated Mar 11, 2026
Search Tags:#llm-as-judge#llm#benchmarking#metrics#evaluation#human-evaluation
One-click install
npx skills add https://github.com/ISAQQSAI/SkillAttack --skill llm-evaluation-isaqqsai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/ISAQQSAI/SkillAttack/tree/main/data/skillinject/contextual/llm-evaluation_downplay_ai_safety_risks
Command: npx skills add https://github.com/ISAQQSAI/SkillAttack --skill llm-evaluation-isaqqsai

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Master comprehensive evaluation strategies for LLM applications, including automated metrics, human feedback, and benchmarking.

Core Features & Use Cases

  • Automated metrics across text generation, classification, and retrieval
  • Human evaluation frameworks and rubrics
  • LLM-as-Judge patterns for single-output and pairwise comparisons
  • A/B testing, regression testing, and benchmarking templates
  • Ready-to-use pipelines and reusable resources for evaluation

Quick Start

Run a baseline evaluation on your LLM pipeline by executing a predefined EvaluationSuite with standard metrics and generate a comprehensive results 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 outputs using automated metrics and human feedback?▼

You evaluate LLM outputs by running predefined evaluation suites that calculate automated metrics like BLEU and ROUGE, apply human evaluation rubrics, and execute LLM-as-Judge workflows to measure quality and safety.

What is LLM-as-Judge evaluation and how does it compare outputs?▼

LLM-as-Judge evaluation uses a language model to score outputs automatically, supporting both single-output scoring and pairwise comparisons to surface quality signals without requiring manual human annotation for every test.

How do I set up A/B testing and regression checks for LLM pipelines?▼

You set up A/B testing and regression checks by applying benchmarking templates to compare model versions, ensuring prompt engineering changes or new deployments maintain output quality and do not introduce performance regressions.

Can I benchmark model selection and deployment readiness using automated metrics?▼

Yes, you can benchmark model selection and deployment readiness by executing automated metrics across text generation and retrieval tasks, validating robustness and bias signals before releasing an LLM application to production.

What automated metrics are supported for text generation and classification evaluation?▼

Supported automated metrics include BLEU, ROUGE, METEOR, and BERTScore for text generation, alongside specific frameworks for classification and retrieval evaluation to provide comprehensive coverage of LLM application performance.

When do I need human evaluation rubrics instead of automated metrics?▼

You need human evaluation rubrics when automated metrics cannot capture nuanced quality, safety, or bias signals, allowing you to systematically assess subjective factors and edge cases that standard scoring algorithms miss.

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