ai-prompt-optimizer

Analyze and rewrite prompts to clarify intent and structure.

6|Updated Mar 6, 2026

Search Tags:#templates #optimization #llm #prompt-engineering #prompt #codeagent #memoryagent

Ready to run

npx skills add https://github.com/jnotsknab/mux-swarm --skill ai-prompt-optimizer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ai-prompt-optimizer
Source: https://github.com/jnotsknab/mux-swarm/tree/main/Skills/bundled-docker/ai-prompt-optimizer
Command: npx skills add https://github.com/jnotsknab/mux-swarm --skill ai-prompt-optimizer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill transforms vague or poorly structured prompts into clear, well-scoped prompts that maximize the quality of LLM responses and reduce back-and-forth.

Core Features & Use Cases

Analyze the original prompt with CodeAgent to identify ambiguities, missing constraints, and opportunities for structure.
Generate optimized prompt versions that clarify intent, define output format, and tailor tone or persona.
Create domain-specific prompt templates for coding, writing, research, and other tasks.
Store successful optimized prompts in MemoryAgent for reuse and future retrieval.

Quick Start

Provide the prompt you want to improve and request an optimized version with rationale.

Frequently Asked Questions about ai-prompt-optimizer

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

FAQPage Schema

How do I optimize a vague prompt for better LLM responses?▼

To optimize a vague prompt, analyze it to identify ambiguities and missing constraints, then clarify intent, define output format, and tailor tone to generate a well-scoped prompt that maximizes LLM response quality.

What's the best way to create reusable prompt templates for coding and writing?▼

Create reusable prompt templates by generating domain-specific structures for coding or writing, then storing the successful optimized versions in MemoryAgent for future retrieval and repeated use across similar tasks.

Why does my LLM output lack structure and require constant back-and-forth?▼

LLM output lacks structure when original prompts are poorly scoped. Applying prompt optimization clarifies intent, defines output format, and adds necessary constraints to reduce back-and-forth and improve response quality.

Can I store and retrieve optimized prompts directly within my workflow?▼

Yes, you can store and retrieve optimized prompts directly by saving successful domain-specific templates in MemoryAgent, allowing seamless reuse and future retrieval without manual prompt reconstruction.

Does prompt engineering work for research and domain-specific tasks?▼

Prompt engineering works for research tasks by applying optimization techniques to clarify intent and structure, generating tailored domain-specific templates that deliver precise LLM responses for specialized research queries.

What are the limitations of using automated prompt optimization?▼

Automated prompt optimization requires providing the original prompt to analyze for ambiguities. It works best when the initial intent can be clarified, though highly subjective or undefined goals may still need manual refinement.