How does this skill reduce LLM costs?

It identifies and removes redundant phrasing and unnecessary tokens in your prompts, leading to lower API billing per request.

Will optimizing my prompt affect the quality of the output?

The skill is designed to maintain or improve output quality by making instructions more direct and less ambiguous, reducing model confusion.

When should I use the LLM Prompt Optimizer?

Use it when you need to improve response speed, lower token counts for high-volume tasks, or clean up complex prompts.

Does it work with specific AI models?

Yes, it provides general optimizations that work across all major LLMs and can be used alongside the llm-integration-expert for specific API needs.

LLM Prompt Optimizer

Name: LLM Prompt Optimizer
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Refines and compresses LLM prompts to minimize token usage, reduce costs, and maximize output quality.

About

This skill empowers Claude to analyze and rewrite prompts for peak efficiency, reducing operational costs and latency by eliminating redundancy and verbosity. By applying advanced prompt engineering techniques, it ensures instructions are clear and direct, helping developers get the most out of Large Language Models while staying within budget and performance targets. It is particularly useful for productionizing AI pipelines where token overhead and response speed are critical factors.

Key Features

0 GitHub stars
Latency improvement through concise instruction sets
Integration with prompt-architect for advanced engineering
Automated redundancy analysis and removal
Prompt rewriting for enhanced clarity and directness
Token usage reduction for significant cost savings

Use Cases

Enhancing output quality by providing clearer, more direct instructions
Improving response speed in latency-sensitive AI applications
Scaling LLM applications while minimizing high API costs

About

Key Features

0 GitHub stars
Latency improvement through concise instruction sets
Integration with prompt-architect for advanced engineering
Automated redundancy analysis and removal
Prompt rewriting for enhanced clarity and directness
Token usage reduction for significant cost savings

Use Cases

Enhancing output quality by providing clearer, more direct instructions
Improving response speed in latency-sensitive AI applications
Scaling LLM applications while minimizing high API costs