Does it help with testing prompts?

Absolutely. It can generate multiple prompt variants, design evaluation criteria, and help you test edge cases to measure output consistency.

Can I use this for models other than Claude?

While it features Claude-specific optimization techniques, it also applies general prompt engineering best practices and specific strategies for other models like GPT-4.

How does this skill reduce token usage?

The skill analyzes prompts to remove redundancy, implements hierarchical information structures, and uses compression techniques to ensure the prompt remains effective while using fewer tokens.

Does it support RAG (Retrieval-Augmented Generation)?

Yes, it provides specific strategies for RAG, including context window management, retrieval query optimization, and recommendations for chunk sizes and embedding strategies.

AI Prompt Optimizer

Name: AI Prompt Optimizer
Author: Uniswap

byUniswap

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Data Science & ML

Refines and enhances AI prompts using advanced engineering techniques to improve performance, reduce token costs, and ensure model-specific accuracy.

About

The AI Prompt Optimizer is a specialized utility designed to elevate AI-assisted development by applying professional prompt engineering practices to your workflows. It helps users transform basic instructions into high-performing prompts using techniques like chain-of-thought reasoning, few-shot examples, and role-based instructions. Beyond simple refinement, the skill focuses on technical optimizations such as context window management, token reduction for cost efficiency, and RAG integration strategies, making it an essential tool for developers building production-grade AI applications or complex automated workflows.

Key Features

Advanced prompt engineering using chain-of-thought and few-shot techniques
Model-specific tuning for Claude, GPT-4, and prompt chaining strategies
Performance testing via prompt variants and evaluation criteria design
13 GitHub stars
Context optimization to minimize token usage and lower API costs
RAG integration advice for retrieval query and chunk size optimization

Use Cases

Improving the reliability and accuracy of complex coding or data extraction prompts
Translating and optimizing existing prompts for different LLM versions or providers
Reducing token consumption in high-volume production AI workflows to lower costs

About

Key Features

Advanced prompt engineering using chain-of-thought and few-shot techniques
Model-specific tuning for Claude, GPT-4, and prompt chaining strategies
Performance testing via prompt variants and evaluation criteria design
13 GitHub stars
Context optimization to minimize token usage and lower API costs
RAG integration advice for retrieval query and chunk size optimization

Use Cases

Improving the reliability and accuracy of complex coding or data extraction prompts
Translating and optimizing existing prompts for different LLM versions or providers
Reducing token consumption in high-volume production AI workflows to lower costs