Transform vague prompts into precise, testable specifications using EARS methodology and domain theory grounding.
Works with
Converts natural language requirements into five EARS patterns (ubiquitous, event-driven, state-driven, conditional, unwanted behavior) with explicit triggers, conditions, and measurable criteria
Applies relevant domain frameworks (GTD, BJ Fogg, Gestalt, Zero Trust, etc.) to enhance requirements with established best practices
Generates structured prompts using Role/Skill
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionprompt-optimizerExecute the skills CLI command in your project's root directory to begin installation:
Fetches prompt-optimizer from daymade/claude-code-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate prompt-optimizer. Access via /prompt-optimizer in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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:
Apply when:
Identify weaknesses:
Convert requirements to EARS patterns. See references/ears_syntax.md for complete syntax rules.
Five core patterns:
The system shall <action>When <trigger>, the system shall <action>While <state>, the system shall <action>If <condition>, the system shall <action>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:
Match requirements to established frameworks. See references/domain_theories.md for full catalog.
Common domain mappings:
Selection process:
Generate specific examples with real data:
Examples must be realistic, specific, varied (success/error/edge cases), and testable.
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:
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]
For complex scenarios, see references/advanced_techniques.md:
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
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 patternsWhen to load references:
ears_syntax.mddomain_theories.mdexamples.mdadvanced_techniques.mdMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
ailabs-393/ai-labs-claude-skills
I recommend prompt-optimizer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: prompt-optimizer is focused, and the summary matches what you get after install.
Keeps context tight: prompt-optimizer is the kind of skill you can hand to a new teammate without a long onboarding doc.
prompt-optimizer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
We added prompt-optimizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: prompt-optimizer is focused, and the summary matches what you get after install.
We added prompt-optimizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
prompt-optimizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
prompt-optimizer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
prompt-optimizer has been reliable in day-to-day use. Documentation quality is above average for community skills.
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