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HermeticOrmus
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prompt-benchmark

Evaluate AI prompt effectiveness on standardized benchmarks like MATH and GSM8K.

1|Updated Feb 2, 2026
Search Tags:#prompt engineering#evaluation#benchmark#math#gsm8k#mmlu#human-eval
One-click install
npx skills add https://github.com/HermeticOrmus/hermetic-claude --skill prompt-benchmark-hermeticormus
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-benchmark
Source: https://github.com/HermeticOrmus/hermetic-claude/tree/main/claude/skills/prompt-benchmark
Command: npx skills add https://github.com/HermeticOrmus/hermetic-claude --skill prompt-benchmark-hermeticormus

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a systematic framework for evaluating and comparing the effectiveness of different AI prompts across various standardized benchmarks, enabling quantitative analysis of prompt quality and improvement.

Core Features & Use Cases

  • Benchmark Execution: Runs prompts against MATH, GSM8K, HumanEval, and MMLU datasets.
  • Prompt Strategy Comparison: Facilitates A/B testing of prompt engineering techniques like Chain-of-Thought and Tree-of-Thought.
  • Quantitative Analysis: Provides accuracy metrics, statistical summaries, and confidence intervals for reliable evaluation.
  • Use Case: Compare the performance of a new meta-prompting strategy against a baseline for solving arithmetic reasoning problems, ensuring a statistically significant improvement before deployment.

Quick Start

Run the prompt-benchmark skill to compare Chain-of-Thought and Tree-of-Thought prompts on the Game of 24 benchmark.

Frequently Asked Questions about prompt-benchmark

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

FAQPage Schema
How do I evaluate and compare the accuracy of different prompt engineering strategies?▼

Benchmarking AI prompts allows you to quantitatively evaluate and compare prompt strategy effectiveness by running them against standardized datasets like MATH, GSM8K, and HumanEval to measure accuracy and provide statistical analysis.

Can I use Chain-of-Thought and Tree-of-Thought prompts for A/B testing on math benchmarks?▼

Yes, you can A/B test Chain-of-Thought and Tree-of-Thought prompts on math benchmarks like GSM8K and the Game of 24, using the framework to measure accuracy and ensure statistically significant improvements before deployment.

What standardized datasets are supported for quantitative prompt evaluation?▼

Supported standardized datasets for quantitative prompt evaluation include MATH, GSM8K, HumanEval, and MMLU, enabling reproducible comparability and statistical rigor across different prompt engineering techniques.

How does statistical analysis work when comparing prompt effectiveness?▼

Statistical analysis of prompt effectiveness works by providing accuracy metrics, statistical summaries, and confidence intervals, ensuring reproducibility and rigor when comparing different prompt strategies through a composable benchmark runner.

What's the best way to benchmark a new meta-prompting strategy for arithmetic reasoning?▼

The best way to benchmark a new meta-prompting strategy for arithmetic reasoning is to run it against a baseline using standardized benchmarks like Game of 24, measuring accuracy to verify a statistically significant improvement before deployment.

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