Will optimizing a prompt negatively affect output quality?

No, the skill is specifically designed to maintain or improve accuracy by refining the prompt to focus on essential instructions and clear directives.

What triggers this skill in Claude Code?

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

Is it compatible with all LLM providers?

Yes, while the skill runs within Claude Code, the resulting optimized prompts are generally applicable and effective for any major LLM provider.

Can I use this for complex system prompts?

Yes, it is particularly effective for long system prompts where token savings accumulate significantly over thousands of requests.

How does this skill reduce LLM costs?

It identifies and removes redundant instructions and wordy phrasing, lowering the total input token count sent to the model API.

AI Prompt Engineering & Optimization

Name: AI Prompt Engineering & Optimization
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Optimizes LLM prompts to minimize token usage, reduce operational costs, and enhance model response quality through automated refinement.

About

This skill empowers Claude to act as a professional prompt engineer, analyzing existing LLM instructions for redundancy, verbosity, and structural inefficiencies. By streamlining phrasing and focusing on high-impact directives, it significantly reduces token overhead and improves response latency without sacrificing output accuracy. It is an essential tool for developers and AI engineers looking to scale LLM applications cost-effectively while maintaining high performance and clarity across various model architectures.

Key Features

Detailed impact explanations for suggested changes
1 GitHub stars
Integration with prompt-architect and LLM expert workflows
Token usage reduction and cost optimization
Automated prompt rewriting for clarity and precision
Redundancy and verbosity analysis

Use Cases

Standardizing prompt quality and efficiency across engineering teams
Scaling high-volume LLM applications with lower API costs
Improving response speed and latency for real-time AI features

About

Key Features

Detailed impact explanations for suggested changes
1 GitHub stars
Integration with prompt-architect and LLM expert workflows
Token usage reduction and cost optimization
Automated prompt rewriting for clarity and precision
Redundancy and verbosity analysis

Use Cases

Standardizing prompt quality and efficiency across engineering teams
Scaling high-volume LLM applications with lower API costs
Improving response speed and latency for real-time AI features