Will optimizing a prompt reduce the quality of the output?

No, the skill is specifically designed to maintain or improve instruction clarity and intent while using more efficient and direct phrasing.

Does it support complex prompt architectures?

Yes, it integrates with tools like prompt-architect to handle sophisticated multi-step logic and advanced engineering patterns.

What are the trigger phrases for this skill?

You can activate it using terms like 'optimize prompt', 'reduce LLM cost', 'improve prompt performance', or 'rewrite prompt'.

How does this skill reduce LLM costs?

It identifies and removes redundant words and phrases from your prompts, lowering the total token count sent to the API without losing meaning.

Can this be used with any LLM?

Yes, while designed as a Claude Code Skill, the resulting optimized prompts are generally more efficient for any large language model.

Prompt Optimizer

Name: Prompt Optimizer
Author: BbgnsurfTech

byBbgnsurfTech

•

Ciencia de Datos y ML

Optimizes Large Language Model prompts to minimize token usage, reduce operational costs, and enhance response quality.

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The Prompt Optimizer skill streamlines your interactions with AI models by refining prompts for maximum efficiency. It analyzes input text to identify redundancies and verbosity, rewriting instructions into concise, high-performance formats that maintain intent while lowering token overhead. This is particularly valuable for developers looking to scale LLM usage, reduce API latency, and improve the precision of AI-generated responses through expert prompt engineering and strategic phrasing.

Características Principales

Automated prompt rewriting for clarity and directness
Response speed optimization via streamlined instructions
3 GitHub stars
Cost-saving analysis for high-volume LLM API usage
Integration with prompt architecture frameworks
Token count reduction through redundancy elimination

Casos de Uso

Reducing operational costs for production-grade AI applications
Refining complex summarization tasks for higher accuracy
Optimizing long-form context prompts to fit within specific token limits

Acerca de

Características Principales

Automated prompt rewriting for clarity and directness
Response speed optimization via streamlined instructions
3 GitHub stars
Cost-saving analysis for high-volume LLM API usage
Integration with prompt architecture frameworks
Token count reduction through redundancy elimination

Casos de Uso

Reducing operational costs for production-grade AI applications
Refining complex summarization tasks for higher accuracy
Optimizing long-form context prompts to fit within specific token limits