How does this skill reduce LLM costs?

It identifies and removes redundant phrasing and filler words from your prompts, significantly reducing the total token count sent to the model.

Will optimizing a prompt change the output quality?

The skill is specifically designed to maintain or improve instruction clarity and intent while using more efficient and direct language.

Can I use this for complex multi-shot prompts?

Yes, it is highly effective at streamlining long-form instructions, complex context blocks, and structured multi-step prompts.

Does it work with models other than Claude?

While used within Claude Code, the optimized prompts it generates are applicable to any major LLM to improve efficiency and reduce overhead.

How do I trigger the optimization process?

You can trigger the skill by using commands like 'optimize prompt', 'reduce LLM cost', or 'improve prompt performance' followed by your instruction.

Prompt Optimization Expert

Name: Prompt Optimization Expert
Author: jeremylongshore

byjeremylongshore

•

883

Data Science & ML

Optimizes LLM prompts to minimize token consumption and maximize response quality for more efficient AI interactions.

About

This skill provides an automated way to refine Large Language Model prompts, focusing on reducing verbosity and redundancy without sacrificing clarity or intent. By analyzing input instructions, it rewrites them into concise, high-performance directives that lower operational costs and accelerate inference times, making it an essential tool for developers and prompt engineers looking to scale their AI workflows efficiently within the Claude Code environment.

Key Features

Automated prompt redundancy and verbosity analysis
Performance-focused prompt rewriting for faster responses
Token usage reduction for lower API costs
Detailed explanations of optimization logic and changes
Seamless integration with prompt architecture agents
883 GitHub stars

Use Cases

Reducing inference latency in real-time AI applications
Improving the instruction clarity and precision of complex tasks
Lowering operational costs for high-volume LLM deployments

About

Key Features

Automated prompt redundancy and verbosity analysis
Performance-focused prompt rewriting for faster responses
Token usage reduction for lower API costs
Detailed explanations of optimization logic and changes
Seamless integration with prompt architecture agents
883 GitHub stars

Use Cases

Reducing inference latency in real-time AI applications
Improving the instruction clarity and precision of complex tasks
Lowering operational costs for high-volume LLM deployments