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dynamic-prompt-registry

Register, discover, and compose reusable prompts with runtime references.

6|1|Updated Nov 29, 2025
Search Tags:#registry#meta-prompting#dynamic-prompt#prompt-composition#reader-monad#quality-tracking
One-click install
npx skills add https://github.com/manutej/categorical-meta-prompting --skill dynamic-prompt-registry
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dynamic-prompt-registry
Source: https://github.com/manutej/categorical-meta-prompting/tree/main/.claude/skills/dynamic-prompt-registry
Command: npx skills add https://github.com/manutej/categorical-meta-prompting --skill dynamic-prompt-registry

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Dynamic Prompt Registry solves the fragmentation of AI prompts by providing a unified, dynamic registry that enables discovery, composition, and runtime reference of prompts. It centralizes prompts, supports unified syntax like @skills:discover() and @skills:compose(), and tracks quality for reusable meta-prompts.

Core Features & Use Cases

  • Unified discovery and filtering: Locate prompts by domain, relevance, and tags.
  • Prompt composition: Build complex prompts via tensor products, sequential, and monadic composition.
  • Deferred resolution and runtime references: Resolve prompts on demand, enabling dynamic libraries and meta-prompts.
  • Quality tracking: Attach quality scores to prompts for selection and optimization.
  • Use Case: Create a test suite by composing a few prompts and resolving them at runtime to generate a production-ready prompt.

Quick Start

From here, to use the registry, register prompts and then compose them with unified syntax to build a meta-prompt.

Frequently Asked Questions about dynamic-prompt-registry

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

FAQPage Schema
How do I manage prompt fragmentation and reuse meta-prompts across different domains?▼

A unified registry solves prompt fragmentation by centralizing discovery, composition, and runtime references, allowing you to build reusable meta-prompts with quality tracking across testing and design domains.

What is the best way to compose complex AI prompts dynamically at runtime?▼

You can compose complex AI prompts dynamically using tensor-product and Kleisli-style composition, resolving them on demand through a runtime lookup environment to execute composite prompts.

How do I build a production-ready prompt from a library of smaller test prompts?▼

Build a production-ready prompt by registering smaller prompts in a library, composing them via unified syntax, and resolving them at runtime using a Reader-like lookup environment.

Can I track and optimize prompt quality using a dynamic registry?▼

Yes, you can track prompt quality by attaching quality scores to prompts in the registry, which facilitates selection and optimization for reusable meta-prompts.

Do I need a specific environment setup to use dynamic prompt composition and discovery?▼

Yes, dynamic prompt composition requires a registered prompt library and a runtime lookup environment, such as a Reader-like monad, along with optional scripts and references to resolve composite prompts.

When should I not use a centralized registry for prompt management?▼

Avoid a centralized registry if your workflow lacks a registered prompt library or runtime lookup environment, as deferred resolution and composition depend on these components to execute.

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