prompt-optimizer
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- Author updated Jun 14, 2026, 10:01 AM
- Author repo claude-code-skills
- Domain
- AI
- Compatible agents
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- Claude Code
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- Cline
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- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @daymade · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: prompt-optimizer
description: Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach t…
category: ai
runtime: no special runtime
---
# prompt-optimizer output preview
## PART A: Task fit
- Use case: 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..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Overview / When to Use / Six-Step Optimization Workflow” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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.”.
- **02** When the source has headings, the agent prioritizes “Overview / When to Use / Six-Step Optimization Workflow” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Validate with a small run
Start with a small task and check whether the result follows “Overview / When to Use / Six-Step Optimization Workflow”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: prompt-optimizer
description: Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach t…
category: ai
source: daymade/claude-code-skills
---
# prompt-optimizer
## When to use
- Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach to Requirements Syntax)…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Overview / When to Use / Six-Step Optimization Workflow” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "prompt-optimizer" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Overview / When to Use / Six-Step Optimization Workflow
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}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:
- EARS syntax transformation - Convert descriptive language to normative specifications
- Domain theory grounding - Apply relevant industry frameworks (GTD, BJ Fogg, Gestalt, etc.)
- Example extraction - Surface concrete use cases with real data
- 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:
- Ubiquitous:
The system shall <action> - Event-driven:
When <trigger>, the system shall <action> - State-driven:
While <state>, the system shall <action> - Conditional:
If <condition>, the system shall <action> - 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 feedbackTransformation 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:
- Identify primary domain from requirement keywords
- Match to 2-4 complementary theories
- Apply theory principles to specific features
- 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, benefitsreferences/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 templatereferences/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
Decide Fit First
Design Intent
How To Use It
Boundaries And Review