What is the LLM Eval Framework skill?

It is a specialized Claude Code capability designed to execute structured evaluations on AI-generated text using a specific rubric and scoring matrix.

How does the weighted scoring work?

The skill assigns scores of 1-5 to various dimensions, multiplies them by a predefined decimal weight, and sums them to provide a final score and pass/fail status.

Can I use this for prompt engineering?

Yes, it is specifically designed to help refine prompts by identifying exactly which dimensions of an output are failing and suggesting concrete rewrites.

What do I need to run an evaluation?

You need a scoring rubric (dimensions and weights), the outputs you want to test, and the original prompt that generated those outputs.

LLM Eval Framework

Name: LLM Eval Framework
Author: ats-kinoshita-iso

byats-kinoshita-iso

0•

Security & Testing

Evaluates LLM outputs against structured rubrics to produce detailed, weighted scoring reports and actionable feedback.

The LLM Eval Framework skill empowers Claude to perform rigorous, objective quality assurance on AI-generated content. By applying standardized scoring rubrics with weighted dimensions—such as correctness, formatting, and clarity—it transforms subjective assessment into data-driven performance reports. This skill is essential for developers who need to benchmark different prompts, compare outputs from various models, or ensure that agentic workflows meet specific production-grade thresholds through consistent and repeatable evaluation metrics.

Key Features

01Multi-dimensional scoring using 1-5 anchor scale descriptions

02Consistency checking to flag ambiguous rubric dimensions

03Comprehensive Markdown report generation with comparative matrices

04Actionable suggestions for prompt improvement and output refinement

050 GitHub stars

06Automated weighted total calculations and pass/fail thresholding

Use Cases

01Benchmarking multiple prompt iterations to identify the most effective instructions

02Conducting A/B testing between different LLM versions or model configurations

03Automating quality control for high-volume AI-generated documentation or code

Key Features

01Multi-dimensional scoring using 1-5 anchor scale descriptions

02Consistency checking to flag ambiguous rubric dimensions

03Comprehensive Markdown report generation with comparative matrices

04Actionable suggestions for prompt improvement and output refinement

050 GitHub stars

06Automated weighted total calculations and pass/fail thresholding

Use Cases

01Benchmarking multiple prompt iterations to identify the most effective instructions

02Conducting A/B testing between different LLM versions or model configurations

03Automating quality control for high-volume AI-generated documentation or code