What file formats are supported for test cases?

Test cases are managed using standard JSON files, making them easy to integrate into CI/CD pipelines or version control systems like Git.

What is a regression loop in this context?

A regression loop involves storing a baseline prompt version and re-running your A/B test suite every time instructions or models change to ensure quality hasn't degraded.

Can I track changes made to my prompts over time?

Yes, the toolkit includes a versioning system that records the author, timestamp, and change rationale for every revision, allowing you to diff and roll back as needed.

How does the A/B tester score prompt performance?

The toolkit uses quantitative scoring based on structured test cases that check for expected content coverage, forbidden phrase violations, regex compliance, and output length sanity.

Does this skill help with AI safety?

Absolutely. It allows you to define 'must_not_contain' constraints and negative test cases to automatically flag unsafe or disallowed content in AI outputs.

Prompt Engineering Toolkit

Name: Prompt Engineering Toolkit
Author: oabdelmaksoud

byoabdelmaksoud

0•

마케팅 자동화

Optimizes and versions AI prompts through systematic A/B testing, quantitative scoring, and regression workflows for production-ready LLM outputs.

The Prompt Engineering Toolkit provides a structured framework for moving AI prompt development from ad-hoc experimentation to professional production assets. It enables developers to perform measurable A/B evaluations, maintain immutable version histories, and perform semantic diffs to understand how instruction changes impact behavior. By implementing quantitative scoring against positive and negative constraints, this skill ensures that LLM outputs remain reliable and safe even as models or requirements evolve, making it essential for teams scaling AI-assisted workflows.

주요 기능

01Semantic diffing to visualize changes in prompt logic

020 GitHub stars

03Automated A/B prompt evaluation with quantitative scoring

04Configurable safety checks for forbidden content and regex compliance

05Structured regression testing against 'golden' test suites

06Immutable prompt versioning and historical changelog tracking

사용 사례

01Ensuring output consistency when migrating between different AI models

02Validating production-grade LLM features before deployment to minimize hallucinations

03Managing collaborative prompt development across multi-author marketing teams

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add oabdelmaksoud/agention prompt-engineer-toolkit

For use in Claude.ai and ChatGPT

주요 기능

01Semantic diffing to visualize changes in prompt logic

020 GitHub stars

03Automated A/B prompt evaluation with quantitative scoring

04Configurable safety checks for forbidden content and regex compliance

05Structured regression testing against 'golden' test suites

06Immutable prompt versioning and historical changelog tracking

사용 사례

01Ensuring output consistency when migrating between different AI models

02Validating production-grade LLM features before deployment to minimize hallucinations

03Managing collaborative prompt development across multi-author marketing teams

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add oabdelmaksoud/agention prompt-engineer-toolkit

For use in Claude.ai and ChatGPT