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prompt-testing

Compares two AI prompts on a dataset using accuracy, compliance, tokens, and latency metrics.

22|3|Updated Jan 10, 2026
Search Tags:#testing#prompt engineering#optimization#llm#performance metrics#a/b testing
One-click install
npx skills add https://github.com/fusengine/agents --skill prompt-testing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-testing
Source: https://github.com/fusengine/agents/tree/main/plugins/prompt-engineer/skills/prompt-testing
Command: npx skills add https://github.com/fusengine/agents --skill prompt-testing

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of objectively measuring and improving the performance of AI prompts by providing a structured framework for A/B testing and performance analysis.

Core Features & Use Cases

  • A/B Testing: Compare two prompt variants (A and B) head-to-head on a defined dataset.
  • Performance Metrics: Quantify prompt effectiveness using metrics like Accuracy, Compliance, Tokens, and Latency.
  • Use Case: You have two versions of a customer support prompt. Use this Skill to run an A/B test on a dataset of customer queries to determine which prompt yields more accurate and helpful responses, and then select the better-performing one.

Quick Start

Use the prompt-testing skill to run an A/B test comparing 'prompt_a.md' against 'prompt_b.md' using the 'tests.json' dataset.

Frequently Asked Questions about prompt-testing

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

FAQPage Schema
How do I run A/B testing on LLM prompts to measure performance?▼

You can compare two prompt variants head-to-head on a defined dataset to measure performance. The skill quantifies effectiveness using metrics like accuracy, compliance, token usage, and latency for data-driven optimization.

What metrics are used to evaluate prompt optimization during testing?▼

Prompt optimization uses metrics like accuracy, compliance, token usage, and latency. These indicators evaluate the quality, efficiency, and robustness of prompt variants across structured test cases in your dataset.

Can I compare two prompt versions on a custom dataset of customer queries?▼

Yes, you can compare two prompt versions on a custom dataset of customer queries. By defining structured test cases, the skill evaluates which prompt variant yields more accurate and helpful responses for your specific data.

How do I generate detailed reports for prompt A/B test results?▼

You generate detailed reports by running A/B tests on your defined dataset. The report outputs performance metrics for each prompt variant, enabling data-driven selection of the better-performing prompt.

Do I need a specific testing framework to define structured test cases for prompts?▼

No specific testing framework is required as a dependency. You define structured test cases using a dataset file, such as a JSON file, to evaluate prompt variants and measure their performance metrics.

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