Can I use this for models other than Claude?

Yes, while the optimization is performed within Claude Code, the resulting prompts are refined for efficiency across all major LLMs like GPT-4 and Gemini.

How does this skill reduce LLM costs?

It identifies and removes unnecessary tokens and redundant phrasing from your instructions, lowering the price of every API call by minimizing input volume.

Will shortening a prompt affect the quality of the answer?

No, the skill uses advanced rewriting techniques to maintain or improve the clarity and impact of instructions while using fewer words.

Does it provide explanations for the changes made?

Yes, the skill provides the optimized prompt alongside an explanation of the specific changes and the expected impact on token count and performance.

What triggers the prompt optimization process?

The skill activates on phrases like 'optimize prompt', 'reduce LLM cost', or 'improve prompt performance' within the Claude Code environment.

Prompt Optimizer for AI & ML

Name: Prompt Optimizer for AI & ML
Author: BbgnsurfTech

byBbgnsurfTech

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数据科学与机器学习

Refines and compresses LLM prompts to minimize token usage, lower operational costs, and maximize response quality.

关于

The Prompt Optimizer skill, part of the AI-ML Engineering Pack, empowers developers to streamline their interactions with Large Language Models by identifying and removing redundancies. By automatically rewriting prompts into concise, high-impact instructions, it significantly reduces API costs and improves execution speed without compromising the accuracy of the AI's output. It is an essential tool for scaling AI applications where efficiency and performance are critical.

主要功能

Automated token usage reduction through redundancy removal
Smart prompt rewriting for enhanced clarity and directness
Cost-efficiency analysis for high-volume LLM workloads
Performance benchmarking for faster AI response times
Seamless integration with prompt engineering frameworks
1 GitHub stars

使用场景

Reducing monthly API costs for production-scale LLM applications
Shortening complex instructions to better fit within model context windows
Improving the reliability of AI summaries and technical content generation

关于

主要功能

Automated token usage reduction through redundancy removal
Smart prompt rewriting for enhanced clarity and directness
Cost-efficiency analysis for high-volume LLM workloads
Performance benchmarking for faster AI response times
Seamless integration with prompt engineering frameworks
1 GitHub stars

使用场景

Reducing monthly API costs for production-scale LLM applications
Shortening complex instructions to better fit within model context windows
Improving the reliability of AI summaries and technical content generation