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prompt-analyzer

Analyze and compare AI prompts to recommend improved elements.

35|10|Updated Dec 29, 2025
Search Tags:#prompt engineering#prompt optimization#prompt analysis#ai prompts#element library#style recommendation
One-click install
npx skills add https://github.com/ttmouse/skills --skill prompt-analyzer-ttmouse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-analyzer
Source: https://github.com/ttmouse/skills/tree/main/prompt-analyzer
Command: npx skills add https://github.com/ttmouse/skills --skill prompt-analyzer-ttmouse

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you understand, compare, and improve your AI prompts by analyzing their components, identifying similarities, and recommending better elements.

Core Features & Use Cases

  • Prompt Analysis: View detailed information about a specific prompt, including used elements, style, and quality score.
  • Prompt Comparison: Identify differences and similarities between two prompts.
  • Similarity Recommendations: Discover prompts similar to a given one.
  • Element Library Statistics: Get insights into the categories and usage of prompt elements.
  • Style-Based Element Recommendations: Find the best elements for a specific prompt style.
  • Use Case: A user wants to understand why one prompt performs better than another. They can use the comparison feature to see the differences in elements and style, then use element statistics to find more effective components for future prompts.

Quick Start

Analyze the details of prompt number 5.

Frequently Asked Questions about prompt-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I analyze AI prompts to understand why one performs better than another?▼

To analyze AI prompts, you can compare their constituent elements, style tags, and historical performance data. This process breaks down the prompts to identify differences in components and quality scores, revealing why one outperforms the other.

What is the best way to optimize prompt engineering workflows using element statistics?▼

Optimizing prompt engineering workflows involves analyzing element library statistics to gain insights into component categories and usage. You can then use these data-driven insights to find and select the most effective elements for your specific prompt style.

Can I compare two AI prompts to find similarities and recommend style improvements?▼

Yes, you can compare two AI prompts to identify exact differences and similarities. The comparative analysis examines style tags and constituent elements, enabling data-driven recommendations for improving prompt style and structure.

How does style-based recommendation work for finding effective prompt elements?▼

Style-based recommendation works by examining the historical performance data and style tags of your AI prompts. It matches these insights against element library statistics to suggest the best performing components for that specific prompt style.

Do I need historical performance data to analyze and refine my AI prompts?▼

Historical performance data is utilized to provide detailed insights and data-driven element suggestions during prompt analysis. While constituent elements and style tags can be examined, performance data enables accurate quality scoring and comparative recommendations.

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