What is included in the test report?

Reports include overall scores, case-type breakdowns, qualitative analysis of strengths and weaknesses, and a final recommendation verdict.

How many test cases are recommended?

For statistical significance, it is recommended to use at least 20 test cases, including a 15-20% mix of edge cases.

What metrics does this skill measure?

The skill evaluates Quality (accuracy, compliance, consistency), Efficiency (tokens, latency, cost), and Robustness (edge cases, jailbreak resistance).

How do I start a new prompt test?

You can initialize a test by using the '/prompt test create' command, defining your test name and providing a JSON dataset of test cases.

Can I compare two different prompt versions?

Yes, the skill is designed for A/B testing, allowing you to run a baseline (Variant A) against a challenger (Variant B) to see statistical deltas.

Prompt Performance Testing

Name: Prompt Performance Testing
Author: fusengine

byfusengine

•

Security & Testing

Measures and compares prompt efficacy through A/B testing, performance metrics, and detailed analytical reports.

This skill provides a systematic framework for evaluating AI prompts by conducting rigorous A/B testing against custom datasets. It enables developers to quantify prompt quality through metrics like accuracy and consistency, optimize for efficiency by tracking token usage and latency, and ensure robustness against edge cases. By automating the data collection and comparison process, it transforms prompt engineering from a trial-and-error process into a data-driven discipline, providing clear recommendations on when to adopt prompt iterations.

Key Features

011 GitHub stars

02Structured test dataset management with edge case support

03Comprehensive A/B testing workflow for comparing prompt variants

04Quantitative decision criteria for adopting prompt improvements

05Detailed performance tracking for quality, efficiency, and robustness

06Automated generation of professional Markdown test reports

Use Cases

01Validating prompt robustness against specific edge cases and error scenarios

02Optimizing LLM costs by identifying prompts with lower token consumption

03Benchmarking a new prompt version against a production baseline

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add fusengine/agents prompt-testing

For use in Claude.ai and ChatGPT

Download Skill