skills.rest
HuuBar
HuuBarCOMMUNITY
Explore all skills by this author →
GitHub

prompt-optimizer

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

Updated Apr 21, 2026
Search Tags:#templates#ears#prompt-optimization#ai-prompt#requirements-engineering#domain-theories
One-click install
npx skills add https://github.com/HuuBar/skill-routing-experiment --skill prompt-optimizer-huubar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-optimizer
Source: https://github.com/HuuBar/skill-routing-experiment/tree/main/unified_skills/daymade/prompt-optimizer
Command: npx skills add https://github.com/HuuBar/skill-routing-experiment --skill prompt-optimizer-huubar

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Prompts for AI systems are often vague and ambiguous, making it hard to get reliable, testable results. This skill converts unclear requirements into precise, atomic specifications using the EARS (Easy Approach to Requirements Syntax) methodology, domain theory grounding, and structured templates.

Core Features & Use Cases

  • EARS-based transformation: decompose natural language requirements into atomic, verifiable statements.
  • Domain theory grounding: map requirements to established frameworks (productivity, UX design, behavior change) to improve credibility and solvability.
  • Template-driven outputs: produce Role/Skills/Workflows/Examples/Formats prompts that can be reused across projects.
  • Reference-guided quality checks: align outputs with references like ears_syntax.md and domain_theories.md for rigorous prompts.
  • Use cases: convert a vague feature request into a complete enhancement prompt for AI assistants, product managers, and engineers.

Quick Start

Submit a vague requirement and receive a complete, enhanced prompt ready for immediate AI execution.

Frequently Asked Questions about prompt-optimizer

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

FAQPage Schema
How do I convert vague AI prompts into testable requirements?▼

To convert vague AI prompts into testable requirements, the skill decomposes natural language using the EARS syntax, mapping ambiguous requests into atomic, verifiable statements for reliable AI execution.

What is EARS syntax for requirements engineering?▼

EARS (Easy Approach to Requirements Syntax) is a methodology to transform ambiguous product requests into precise, actionable specifications, grounding them in domain theories like UX design and behavior change.

How do I structure an AI prompt using Role/Skills/Workflows templates?▼

You can structure an AI prompt using Role/Skills/Workflows/Examples/Formats templates to generate reusable outputs, ensuring requirements are rigorously aligned with reference patterns like ears_syntax.md.

Can I use prompt-optimization for software and UX design tasks?▼

Yes, you can use this prompt-optimization approach for software and UX design tasks, as it applies domain theory grounding to map requirements to established frameworks across multiple domains.

Does this approach support reference-guided quality checks for prompts?▼

Yes, the approach supports reference-guided quality checks by aligning generated specifications with internal references like domain_theories.md to ensure rigorous, testable prompt outputs.

Related Skills

Explore More Content & Communication Skills→
daymadedaymade
1.3k

prompt-optimizer

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

Community
Advanced
Asharf30Asharf30

speckit-specify

Generate structured feature specifications from natural language descriptions.

Community
Advanced
tachyon-beeptachyon-beep
14

using-simulation-tactics

Route simulation-tactics tasks to the appropriate core skills in sequence.

Community
Advanced
791994545791994545
4

prompt-optimizer

Transform user prompts into structured EARS requirements.

Community
Advanced