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universal-learner

Extract and categorize reusable elements from natural language prompts across seven domains.

35|10|Updated Dec 29, 2025
Search Tags:#prompt engineering#reusability#knowledge extraction#ai learning#element extraction#domain classification
One-click install
npx skills add https://github.com/ttmouse/skills --skill universal-learner-ttmouse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: universal-learner
Source: https://github.com/ttmouse/skills/tree/main/universal-learner
Command: npx skills add https://github.com/ttmouse/skills --skill universal-learner-ttmouse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the extraction of reusable elements from prompts across various domains, building a continuously growing knowledge base for AI.

Core Features & Use Cases

  • Element Extraction: Identifies and extracts key components like product types, design styles, or photographic techniques from prompts.
  • Domain Classification: Automatically categorizes prompts into 7 predefined domains (portrait, product, design, art, video, interior, common).
  • Tagging & Reusability Scoring: Assigns relevant tags and estimates the reusability of extracted elements.
  • Database Management: Stores extracted elements in a structured database for future use.
  • Use Case: When you discover a particularly effective prompt for generating product photography, this Skill can extract the core elements (e.g., lighting, camera angle, material descriptions) so you can easily reuse them in new prompts.

Quick Start

Analyze and learn from this prompt: A premium collector's edition book photographed with Phase One camera, featuring Italian calfskin binding...

Frequently Asked Questions about universal-learner

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

FAQPage Schema
How do I extract reusable elements from natural language prompts for AI knowledge accumulation?▼

To extract reusable elements from natural language prompts, this Skill parses input text to identify key components like product types, design styles, and photographic techniques, storing them in a structured database for AI knowledge accumulation.

What domains does prompt classification support for element extraction?▼

Prompt classification supports element extraction across seven predefined domains: portrait, product, design, art, video, interior, and common photography, ensuring categorized AI knowledge accumulation.

How does reusability scoring work for extracted prompt elements?▼

Reusability scoring for extracted prompt elements works by assigning relevant tags and estimating the reusability of specific components like lighting or material textures, calculating a score to determine future prompt utility.

Can I use this to learn and categorize effective product photography prompts?▼

Yes, you can use this to learn and categorize effective product photography prompts by extracting core elements such as camera angles, lighting, and material descriptions to easily reuse them in new prompts.

Do I need a specific database setup to store extracted prompt elements?▼

No external database dependencies are required. The Skill manages database storage internally, structuring the extracted prompt elements and reusability scores directly within the environment for continuous knowledge base growth.

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