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llm-evaluation

Construct evaluation sets and implement LLM-as-judge metrics for regression testing.

25|3|Updated Jul 14, 2026
Search Tags:#testing#quality assurance#metrics#evaluation#regression#llm-judge
One-click install
npx skills add https://github.com/nimadorostkar/Claude-Skills-collection --skill llm-evaluation-nimadorostkar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/nimadorostkar/Claude-Skills-collection/tree/main/skills/ai/llm-evaluation
Command: npx skills add https://github.com/nimadorostkar/Claude-Skills-collection --skill llm-evaluation-nimadorostkar

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill eliminates the guesswork in LLM development by providing a structured, evidence-based approach to measuring model performance, preventing regressions, and validating prompt changes.

Core Features & Use Cases

  • Evaluation Set Construction: Build robust test sets using real-world usage data rather than synthetic examples.
  • Metric Selection & Validation: Implement tiered metrics ranging from exact matches to validated LLM-as-judge rubrics.
  • Regression Testing: Integrate automated gates into CI/CD pipelines to ensure prompt updates do not degrade system performance.

Quick Start

Use the llm-evaluation skill to define a rubric-based judge and run a regression test on the current summarization prompt.

Frequently Asked Questions about llm-evaluation

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

FAQPage Schema
How do I prevent prompt regressions when updating LLM features?▼

Build LLM evaluation sets using real-world usage data instead of synthetic examples to accurately measure model performance and prevent silent quality degradation during prompt updates.

How does LLM-as-judge evaluation work for quality assurance?▼

Validate LLM-as-judge metrics by defining precise rubrics and applying human-in-the-loop validation, which ensures metric reliability and prevents silent quality degradation in your evaluation framework.

Can I integrate LLM regression testing into CI/CD pipelines?▼

Integrate regression gates into CI/CD pipelines to automate LLM quality assurance, catching performance degradation from prompt updates before deployment.

What is the best way to measure LLM feature quality systematically?▼

Establish a systematic LLM evaluation framework using real-world data sets, validated LLM-as-judge rubrics, and CI/CD regression gates to measure feature quality and prevent performance degradation.

Do I need human-in-the-loop validation for LLM evaluation metrics?▼

Human-in-the-loop validation is required to ensure metric reliability and prevent silent quality degradation when implementing validated LLM-as-judge rubrics in your evaluation framework.

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