What is the Prompt Optimizer skill?

It is a specialized tool for Claude Code that analyzes and rewrites user instructions to make them more effective, clear, and actionable for AI models.

Can it help with complex coding tasks?

Yes, it can break down complex requirements into structured workflows or prompt chains, making it much easier for AI models to handle intricate programming challenges.

Does it explain the changes it makes?

Yes, every optimization includes an Analysis Section and an Improvement Explanation so you can understand why specific changes were made and learn better prompting habits.

How does it improve my prompts?

It adds necessary context, removes ambiguity, defines explicit constraints, and applies advanced techniques like Chain-of-Thought to ensure the AI understands exactly what to do.

Prompt Optimizer

Name: Prompt Optimizer
Author: hikanner

byhikanner

•

Productivity & Workflow

Transforms vague user prompts into high-quality, structured instructions optimized for AI models using industry best practices.

About

Prompt Optimizer is a specialized skill designed to bridge the gap between vague user intent and effective AI execution. By applying systematic prompt engineering principles—such as adding context, defining constraints, and utilizing advanced techniques like Chain-of-Thought—it restructures disorganized requests into clear, actionable instructions. This tool is essential for developers and AI users who need to maximize the quality and reliability of model outputs while minimizing hallucination and ambiguity.

Key Features

Systematic analysis of prompt clarity, specificity, and structural gaps
Support for advanced techniques like Chain-of-Thought and Prompt Chaining
Educational feedback loop to help users improve their own prompting skills over time
Application of core engineering principles including context setting and constraint definition
Structured output format featuring detailed improvement explanations and usage tips
7 GitHub stars

Use Cases

Converting complex, multi-stage tasks into logical prompt chains for better reliability
Refining vague or one-sentence requests into detailed technical specifications
Standardizing output formats through explicit schema definitions and prefilling techniques

About

Key Features

Systematic analysis of prompt clarity, specificity, and structural gaps
Support for advanced techniques like Chain-of-Thought and Prompt Chaining
Educational feedback loop to help users improve their own prompting skills over time
Application of core engineering principles including context setting and constraint definition
Structured output format featuring detailed improvement explanations and usage tips
7 GitHub stars

Use Cases

Converting complex, multi-stage tasks into logical prompt chains for better reliability
Refining vague or one-sentence requests into detailed technical specifications
Standardizing output formats through explicit schema definitions and prefilling techniques