Does it work for specific models like GPT-4 or Claude?

It is model-agnostic and optimizes prompts based on general LLM principles that are applicable across all major AI providers and model architectures.

How does this skill reduce LLM costs?

It analyzes and rewrites prompts to be more concise, removing unnecessary words and tokens while keeping the core intent clear, which directly lowers API billing.

Can this skill handle complex system prompts?

Yes, it is built to analyze and simplify complex, multi-layered prompts and system instructions used in professional AI development environments.

Will optimizing my prompt change the quality of the AI's answer?

The skill is designed to maintain or even improve output quality by making instructions more direct and reducing the 'noise' that can confuse models.

AI & ML Prompt Optimizer

Name: AI & ML Prompt Optimizer
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Optimizes Large Language Model (LLM) prompts to minimize token consumption, reduce operational costs, and enhance response quality.

About

The AI & ML Prompt Optimizer skill empowers developers to refine their interactions with Large Language Models by streamlining prompts for maximum efficiency. It automatically identifies redundancies, removes verbosity, and rewrites instructions to be more concise while maintaining or improving the output's accuracy. By significantly reducing token counts, this skill directly lowers API costs and improves response latency, making it an essential utility for building production-grade AI applications and maintaining cost-effective prompt engineering workflows.

Key Features

Token usage analysis and reduction
0 GitHub stars
Cost-saving optimization for high-volume API calls
Direct instruction rewriting for improved clarity
Prompt redundancy and verbosity identification
Performance benchmarking for response speed

Use Cases

Reducing monthly API costs for high-traffic LLM applications
Refining and standardizing complex prompt templates for production
Improving response latency in real-time AI chat interfaces

About

Key Features

Token usage analysis and reduction
0 GitHub stars
Cost-saving optimization for high-volume API calls
Direct instruction rewriting for improved clarity
Prompt redundancy and verbosity identification
Performance benchmarking for response speed

Use Cases

Reducing monthly API costs for high-traffic LLM applications
Refining and standardizing complex prompt templates for production
Improving response latency in real-time AI chat interfaces