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prompt-optimizer skill

/prompt-optimizer

This skill transforms vague prompts into precise, testable specifications using the EARS method to enhance AI-generated code, products, or documents.

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SKILL.md
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---
name: prompt-optimizer
description: Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach to Requirements Syntax) methodology. This skill should be used when users provide loose requirements, ambiguous feature descriptions, or need to enhance prompts for AI-generated code, products, or documents. Triggers include requests to "optimize my prompt", "improve this requirement", "make this more specific", or when raw requirements lack detail and structure.
---

# 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

## When to Use

Apply when:
- User provides vague feature requests ("build a dashboard", "create a reminder app")
- Requirements lack specific conditions, triggers, or measurable outcomes
- Natural language descriptions need conversion to testable specifications
- User explicitly requests prompt optimization or requirement refinement

## Six-Step Optimization Workflow

### Step 1: Analyze Original Requirement

Identify weaknesses:
- **Overly broad** - "Add user authentication" → Missing password requirements, session management
- **Missing triggers** - "Send notifications" → Missing when/why notifications trigger
- **Ambiguous actions** - "Make it user-friendly" → No measurable usability criteria
- **No constraints** - "Process payments" → Missing security, compliance requirements

### Step 2: Apply EARS Transformation

Convert requirements to EARS patterns. See `references/ears_syntax.md` for complete syntax rules.

**Five core patterns:**
1. **Ubiquitous**: `The system shall <action>`
2. **Event-driven**: `When <trigger>, the system shall <action>`
3. **State-driven**: `While <state>, the system shall <action>`
4. **Conditional**: `If <condition>, the system shall <action>`
5. **Unwanted behavior**: `If <condition>, the system shall prevent <unwanted action>`

**Quick example:**
```
Before: "Create a reminder app with task management"

After (EARS):
1. When user creates a task, the system shall guide decomposition into executable sub-tasks
2. When task deadline is within 30 minutes AND user has not started, the system shall send notification with sound alert
3. When user completes a sub-task, the system shall update progress and provide positive feedback
```

**Transformation checklist:**
- [ ] Identify implicit conditions and make explicit
- [ ] Specify triggering events or states
- [ ] Use precise action verbs (shall, must, should)
- [ ] Add measurable criteria ("within 30 minutes", "at least 8 characters")
- [ ] Break compound requirements into atomic statements
- [ ] Remove ambiguous language ("user-friendly", "fast")

### Step 3: Identify Domain Theories

Match requirements to established frameworks. See `references/domain_theories.md` for full catalog.

**Common domain mappings:**
- **Productivity** → GTD, Pomodoro, Eisenhower Matrix
- **Behavior Change** → BJ Fogg Model (B=MAT), Atomic Habits
- **UX Design** → Hick's Law, Fitts's Law, Gestalt Principles
- **Security** → Zero Trust, Defense in Depth, Privacy by Design

**Selection process:**
1. Identify primary domain from requirement keywords
2. Match to 2-4 complementary theories
3. Apply theory principles to specific features
4. Cite theories in enhanced prompt for credibility

### Step 4: Extract Concrete Examples

Generate specific examples with real data:
- User scenarios: "When user logs in on mobile device..."
- Data examples: "Product: 'Laptop', Price: $999, Stock: 15"
- Workflow examples: "Task: Write report → Sub-tasks: Research (2h), Draft (3h), Edit (1h)"

Examples must be **realistic**, **specific**, **varied** (success/error/edge cases), and **testable**.

### Step 5: Generate Enhanced Prompt

Structure using the standard framework:

```markdown
# Role
[Specific expert role with domain expertise]

## Skills
- [Core capability 1]
- [Core capability 2]
[List 5-8 skills aligned with domain theories]

## Workflows
1. [Phase 1] - [Key activities]
2. [Phase 2] - [Key activities]
[Complete step-by-step process]

## Examples
[Concrete examples with real data, not placeholders]

## Formats
[Precise output specifications:
- File types, structure requirements
- Design/styling expectations
- Technical constraints
- Deliverable checklist]
```

**Quality criteria:**
- **Role specificity**: "Product designer specializing in time management apps" > "Designer"
- **Theory grounding**: Reference frameworks explicitly
- **Actionable workflows**: Clear inputs/outputs and decision points
- **Concrete examples**: Real data, not "Example 1", "Example 2"
- **Measurable formats**: Specific requirements, not "good design"

### Step 6: Present Optimization Results

Output in structured format:

```markdown
## Original Requirement
[User's vague requirement]

**Identified Issues:**
- [Issue 1: e.g., "Lacks specific trigger conditions"]
- [Issue 2: e.g., "No measurable success criteria"]

## EARS Transformation
[Numbered list of EARS-formatted requirements]

## Domain & Theories
**Primary Domain:** [e.g., Authentication Security]

**Applicable Theories:**
- **[Theory 1]** - [Brief relevance]
- **[Theory 2]** - [Brief relevance]

## Enhanced Prompt
[Complete Role/Skills/Workflows/Examples/Formats prompt]

---

**How to use:**
[Brief guidance on applying the prompt]
```

## Advanced Techniques

For complex scenarios, see `references/advanced_techniques.md`:
- **Multi-stakeholder requirements** - EARS statements for each user type
- **Non-functional requirements** - Performance, security, scalability with quantified thresholds
- **Complex conditional logic** - Nested conditions with boolean operators

## Quick Reference

**Do's:**
✅ Break down compound requirements (one EARS statement per requirement)
✅ Specify measurable criteria (numbers, timeframes, percentages)
✅ Include error/edge cases
✅ Ground in established theories
✅ Use concrete examples with real data

**Don'ts:**
❌ Avoid vague language ("fast", "user-friendly")
❌ Don't assume implicit knowledge
❌ Don't mix multiple actions in one statement
❌ Don't use placeholders in examples

## Resources

Load these reference files as needed:

- **`references/ears_syntax.md`** - Complete EARS syntax rules, all 5 patterns, transformation guidelines, benefits
- **`references/domain_theories.md`** - 40+ theories mapped to 10 domains (productivity, UX, gamification, learning, e-commerce, security, etc.)
- **`references/examples.md`** - Four complete transformation examples (procrastination app, e-commerce product page, learning dashboard, password reset security) with before/after comparisons and reusable template
- **`references/advanced_techniques.md`** - Multi-stakeholder requirements, non-functional specs, complex conditional logic patterns

**When to load references:**
- EARS syntax clarification needed → `ears_syntax.md`
- Domain theory selection requires extensive options → `domain_theories.md`
- User requests multiple optimization examples → `examples.md`
- Complex requirements with multiple stakeholders or non-functional specs → `advanced_techniques.md`

Overview

This skill transforms vague prompts and loose requirements into precise, testable specifications using the EARS (Easy Approach to Requirements Syntax) methodology. It converts natural language into structured EARS statements, grounds features in domain theories, and produces a ready-to-run prompt template for AI agents or development teams. Use it to turn ambiguous ideas into actionable work items with measurable acceptance criteria.

How this skill works

The optimizer analyzes the original requirement to surface missing triggers, states, conditions, and measurable criteria. It applies EARS patterns (Ubiquitous, Event-driven, State-driven, Conditional, Unwanted-behavior), maps features to relevant domain theories, and extracts concrete examples and edge cases. Finally it emits a structured prompt in Role/Skills/Workflows/Examples/Formats format that is ready for immediate use by an AI or engineering team.

When to use it

  • You have a vague feature request like "build a dashboard" and need testable requirements
  • Requirements lack triggers, measurable outcomes, or explicit constraints
  • You want to convert natural-language product or technical specs into atomic acceptance criteria
  • You need an improved prompt for AI-generated code, documents, or designs
  • You want domain-theory grounding (UX, security, behavior change) added to requirements

Best practices

  • Provide the original raw requirement and any context (users, environment, constraints)
  • Indicate target domain or desired frameworks when known (e.g., GTD, Fogg, Zero Trust)
  • Request explicit examples and edge-case scenarios for test coverage
  • Ask for non-functional thresholds (response time, security level, error rates)
  • Iterate: refine the enhanced prompt with real feedback and test cases

Example use cases

  • Convert "create reminder app" into EARS statements with triggers, notifications, and success criteria
  • Refine a product brief like "improve onboarding" into measurable UX tasks grounded in Gestalt and Hick's Law
  • Turn a loose security requirement "secure payments" into conditional EARS specs and compliance checks
  • Enhance an AI prompt to generate production-ready API code by adding role, workflows, examples, and formats
  • Produce acceptance tests and realistic sample data for QA from a single sentence requirement

FAQ

What inputs do you need to optimize a prompt?

A short raw requirement or prompt, context about users or environment, and any known constraints or preferred frameworks.

Can it handle non-functional requirements?

Yes — the workflow includes explicit handling of performance, security, and scalability with quantified thresholds when provided.