Individual skill

prompt-optimizer

Transforms vague prompts or feature requests into precise, testable specifications using EARS (Easy Approach to Requirements Syntax) grounded in relevant domain theories (GTD, BJ Fogg, Gestalt). Use when requirements lack triggers or measurable outcomes, or the user asks to "optimize my prompt" / "improve this requirement" / "make this more specific".

prompt-optimizer/SKILL.md

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Repository stars (not skill ratings): 1,519
Repository last updated: Nov 7, 2026
License: MIT

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SKILL.mdcore · 7,845 bytes
references/advanced_techniques.mddoc · 13,875 bytes
references/domain_theories.mddoc · 6,221 bytes
references/ears_syntax.mddoc · 4,006 bytes
references/examples.mddoc · 12,992 bytes

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# Prompt Optimizer

## Overview

Optimize vague prompts into precise, actionable specifications using EARS (Easy Approach to Requirements Syntax) - a Rolls-Royce methodology for transforming natural language into structured, testable requirements.

**Methodology inspired by:** This skill's approach to combining EARS with domain theory grounding was inspired by [阿星AI工作室 (A-Xing AI Studio)](https://mp.weixin.qq.com/s/yUVX-9FovSq7ZGChkHpuXQ), which demonstrated practical EARS application for prompt enhancement.

**Four-layer enhancement process:**

1. **EARS syntax transformation** - Convert descriptive language to normative specifications
2. **Domain theory grounding** - Apply relevant industry frameworks (GTD, BJ Fogg, Gestalt, etc.)
3. **Example extraction** - Surface concrete use cases with real data
4. **Structured prompt generation** - Format using Role/Skills/Workflows/Examples/Formats framework