prompt-optimizer

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), 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:

# Role

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:

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