meta-prompting

Name: meta-prompting
Availability: InStock
Author: kinnerkarmanish

Optimize prompts through an iterative meta-prompt loop with evaluation metrics.

Updated Apr 26, 2026

Search Tags:#meta-prompting #prompt-engineering #refine #improve-prompt #optimize-prompt #better-results

Ready to run

npx skills add https://github.com/kinnerkarmanish/mak --skill meta-prompting-kinnerkarmanish

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: meta-prompting
Source: https://github.com/kinnerkarmanish/mak/tree/main/library/skills/ai-patterns/meta-prompting
Command: npx skills add https://github.com/kinnerkarmanish/mak --skill meta-prompting-kinnerkarmanish

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables automatic refinement of prompts used with language models by treating prompts as objects to be optimized within a feedback loop, allowing the model to improve its own prompts through meta-level optimization.

Core Features & Use Cases

Meta-prompt templates for task prompts and system prompts that guide improvement.
Iterative improvement loop with scoring, comparison, and verification checkpoints.
Reusable prompt library creation to accelerate future tasks and cross-domain adaptation.

Quick Start

Provide an initial task prompt and run the meta-prompting workflow to generate an optimized prompt.

Frequently Asked Questions about meta-prompting

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

FAQPage Schema

How do I optimize prompts to improve language model outputs?▼

You optimize prompts to improve language model outputs by using an iterative meta-prompting loop that scores, compares, and verifies prompt variations against evaluation metrics to consistently refine results.

What is meta-prompting and how does it refine task prompts?▼

Meta-prompting refines task prompts by treating them as optimization objects within a feedback loop, allowing the language model to automatically improve its own prompts through meta-level evaluation and adjustments.

When should I use an iterative prompt improvement loop?▼

Use an iterative prompt improvement loop for repetitive tasks, evolving domains, and new prompt templates to achieve consistent output improvements through automated scoring and verification checkpoints.

Can I create a reusable prompt library from optimized templates?▼

You can create a reusable prompt library from optimized templates to accelerate future tasks and enable cross-domain adaptation by saving the refined prompts generated through the meta-optimization workflow.

What is the best way to self-improve system prompts for repetitive tasks?▼

The best way to self-improve system prompts for repetitive tasks is by applying meta-prompt templates that guide iterative improvement through scoring, comparison, and verification checkpoints to ensure consistent quality.