quality-enriched-prompting

Name: quality-enriched-prompting
Availability: InStock
Author: manutej

Assess and improve prompt quality using a numerical scoring model.

6|1|Updated Nov 29, 2025

Search Tags:#category-theory #LLM-evaluation #prompt-quality #enriched-category #multi-dimensional-metrics #prompts-optimization #pareto-frontier

Ready to run

npx skills add https://github.com/manutej/categorical-meta-prompting --skill quality-enriched-prompting

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: quality-enriched-prompting
Source: https://github.com/manutej/categorical-meta-prompting/tree/main/.claude/skills/quality-enriched-prompting
Command: npx skills add https://github.com/manutej/categorical-meta-prompting --skill quality-enriched-prompting

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill implements a 0,1-enriched category framework to enable continuous optimization of prompt quality. It models transformations as quality-rated morphisms and provides methods to evaluate, compare, and improve prompts across multiple dimensions.

Core Features & Use Cases

0,1-enriched category foundations for prompts, outputs, and contexts with quality-graded morphisms.
Multi-dimensional quality metrics and Pareto frontier analysis to identify optimal prompts.
LLM-based evaluation and guided improvements to co-create higher-quality prompts.
Enriched functors that map prompts to quality scores while preserving structure.

Quick Start

To begin, define an initial_prompt and a evaluation function, run iterative improvement until the desired quality is reached.

Frequently Asked Questions about quality-enriched-prompting

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

FAQPage Schema

How do I optimize prompt quality across multiple dimensions?▼

To optimize prompt quality across multiple dimensions, you can use multi-dimensional metrics and LLM-based evaluation to guide continuous improvements. This approach applies Pareto frontier analysis to identify optimal prompts.

What is 0,1-enriched category theory used for in LLM evaluation?▼

0,1-enriched category theory models prompt transformations as quality-rated morphisms. It maps prompts to continuous quality scores using enriched functors, enabling structured evaluation and comparison of LLM outputs.

How do I set up iterative prompt improvement with continuous quality scoring?▼

Define an initial prompt and an evaluation function, then run iterative improvement until the desired quality is reached. The framework uses LLM evaluation to co-create higher-quality prompts through enrichment-based composition.

Can I use Pareto frontier analysis for domain-agnostic prompt optimization?▼

Yes, Pareto frontier analysis supports domain-agnostic quality control by identifying optimal prompts across multiple dimensions. It evaluates trade-offs between different quality metrics to find the best performing prompts.

Do I need specific dependencies to implement enriched category prompt optimization?▼

No specific dependencies are required to implement enriched category prompt optimization. The framework operates independently to model prompt transformations and map them to continuous quality scores using enriched functors.

When should I use multi-dimensional metrics instead of single-score prompt evaluation?▼

Use multi-dimensional metrics when prompt quality involves trade-offs across different criteria. This approach models transformations as quality-graded morphisms, providing a richer evaluation than single scores by mapping to a Pareto frontier.