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Skills/daymade/claude-code-skills/prompt-optimizer
prompt-optimizer logo

prompt-optimizer

daymade/claude-code-skills
1K installs1K stars
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Installation

npx skills add https://github.com/daymade/claude-code-skills --skill prompt-optimizer

Summary

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.

SKILL.md

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
[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:

## 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

Score

0–100
65/ 100

Grade

C

Popularity17/30

1,105 installs — growing adoption. Source repo has 1,182 GitHub stars.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Prompt Optimizer skill score badge previewScore badge

Markdown

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Prompt Optimizer FAQ

How do I install the Prompt Optimizer skill?

Run “npx skills add https://github.com/daymade/claude-code-skills --skill prompt-optimizer” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Prompt Optimizer skill do?

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. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Prompt Optimizer skill free?

Yes. Prompt Optimizer is a free, open-source skill published from daymade/claude-code-skills. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Prompt Optimizer work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Prompt Optimizer works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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