Does this skill generate new prompts?

No, the Prompt Analyzer is focused exclusively on analyzing, comparing, and providing insights on prompts that have already been generated in your history.

What data does the Prompt Analyzer depend on?

It relies on a history of generated prompts and elements stored in a database (like elements.db), typically populated by an intelligent prompt generator.

How does the comparison feature work?

It takes two prompt IDs and provides a similarity score, identifies shared elements, and lists unique elements found in one but not the other.

Can I see which elements are most effective in my library?

Yes, the element statistics feature allows you to view usage frequency and average quality scores for specific prompt categories and elements.

Prompt Analyzer

Name: Prompt Analyzer
Author: ttmouse

byttmouse

•

Data Science & ML

Analyzes generated AI prompts to provide structural insights, style comparisons, and data-driven optimization recommendations.

About

The Prompt Analyzer is a specialized diagnostic tool designed for AI prompt engineering within the Claude Code environment. It deconstructs existing prompts into their core elements, providing deep insights into style tags, quality scores, and structural patterns. By comparing different prompt versions and tracking element usage statistics, it helps developers identify high-performing prompt components. Whether you need to find similar prompts based on historical data or discover the best element combinations for a specific aesthetic like Cyberpunk or Ancient Chinese style, this skill provides the analytical bridge between raw prompt generation and optimized results.

Key Features

Intelligent recommendation of similar prompts based on shared elements and intent
Comprehensive element library statistics including usage frequency and quality metrics
31 GitHub stars
Style-based recommendations for optimal element combinations across different themes
Side-by-side comparison of two prompts with similarity scoring and diffing
Structural analysis of prompts to identify specific elements and attributes

Use Cases

Benchmarking new prompt variations against successful historical prompts to ensure consistency
Analyzing existing prompts to understand why certain versions produce higher quality results
Researching style-specific element combinations to speed up creative prompt engineering

About

Key Features

Intelligent recommendation of similar prompts based on shared elements and intent
Comprehensive element library statistics including usage frequency and quality metrics
31 GitHub stars
Style-based recommendations for optimal element combinations across different themes
Side-by-side comparison of two prompts with similarity scoring and diffing
Structural analysis of prompts to identify specific elements and attributes

Use Cases

Benchmarking new prompt variations against successful historical prompts to ensure consistency
Analyzing existing prompts to understand why certain versions produce higher quality results
Researching style-specific element combinations to speed up creative prompt engineering