skills.rest
daymade
daymadeCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

prompt-optimizer

Transform vague prompts into EARS specifications with structured Role/Skills/Workflows/Examples/Formats output.

1.3k|212|Updated Oct 22, 2025
Search Tags:#requirements#EARS#prompt-engineering#prompt-optimization#domain-theory#automated-prompting
One-click install
npx skills add https://github.com/daymade/claude-code-skills --skill prompt-optimizer-daymade
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-optimizer
Source: https://github.com/daymade/claude-code-skills/tree/main/prompt-optimizer
Command: npx skills add https://github.com/daymade/claude-code-skills --skill prompt-optimizer-daymade

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill transforms vague, ambiguous prompts and requirements into precise, actionable specifications using EARS and domain theory grounding, saving you time and preventing miscommunications.

Core Features & Use Cases

  • Prompt refinement: Rewrites loose requirements into measurable, testable statements using EARS patterns.
  • Domain grounding: Maps requirements to standard theories (GTD, BJ Fogg, Gestalt, etc.) for credibility.
  • Structured prompts: Outputs a complete Role/Skills/Workflows/Examples/Formats prompt ready for AI augmentation.

Quick Start

Use the prompt-optimizer to convert a vague requirement into a structured, actionable Prompt Ready for use by an AI agent.

Frequently Asked Questions about prompt-optimizer

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

FAQPage Schema
How do I turn vague requirements into precise specifications?▼

EARS (Easy Approach to Requirements Syntax) transforms loose requirements into measurable, testable statements by applying structured patterns. This Skill applies domain theory grounding and concrete examples to convert ambiguous prompts into actionable specs ready for AI generation or implementation.

What is EARS and when should I use it for prompt optimization?▼

EARS is a normative specification syntax that structures requirements into testable conditions. Use it when prompts need optimization for AI-generated code or documents, or when feature descriptions are ambiguous and need measurable acceptance criteria.

How do I structure a prompt using the Role/Skills/Workflows/Examples/Formats framework?▼

This framework organizes prompts by defining who performs the task (Role), what capabilities are needed (Skills), the sequence of actions (Workflows), concrete instances (Examples), and expected output structure (Formats). The Skill generates complete structured prompts following this pattern.

Can I use domain theory grounding to improve requirement clarity?▼

Yes. Domain theory grounding maps requirements to established frameworks like GTD, BJ Fogg behavioral science, or Gestalt principles, adding credibility and precision. This grounds vague statements in recognized theory before generating specifications.

What's the difference between rewriting requirements and optimizing prompts for AI?▼

Requirement rewriting applies EARS patterns to clarify acceptance criteria; prompt optimization structures those precise requirements into a Role/Skills/Workflows/Examples/Formats format designed for AI agents to consume and act on reliably.

Related Skills

Explore More Software Engineering Skills→
petbrainspetbrains
13

self-improve

Transform vague user requests into enriched descriptions with traceable source citations.

Official
Basic
Foo-xFoo-x
2

rdd

Generate requirement definition documents using EARS notation.

Community
Intermediate
CaptainCrouton89CaptainCrouton89
497

Testing Code

Automate software tests to verify functionality against acceptance criteria.

Community
Basic
HuuBarHuuBar

prompt-optimizer

Transform vague prompts into precise EARS-based specifications with structured templates.

Community
Advanced