Will optimizing a prompt decrease its accuracy?

No, the skill is designed to maintain or improve instruction clarity while reducing length, ensuring that the LLM still understands the core objective clearly.

Does this integrate with other Claude Code tools?

Yes, it integrates with the prompt-architect agent to leverage advanced engineering techniques and the llm-integration-expert for API-specific optimizations.

Can I use this for complex engineering prompts?

Yes, it is particularly effective at distilling complex, multi-step engineering instructions into concise, high-performance prompts that result in more focused outputs.

How does this skill reduce LLM costs?

It analyzes prompts to remove redundant words and verbosity, resulting in fewer tokens sent to the API, which directly lowers per-request costs.

Prompt Optimization Adapter

Name: Prompt Optimization Adapter
Author: jeremylongshore

byjeremylongshore

•

883

データサイエンスとML

Optimizes LLM prompts to reduce token usage, lower costs, and enhance response performance through intelligent rewriting.

概要

This skill empowers Claude to refine and streamline prompts for Large Language Models (LLMs), identifying redundancies and verbosity to minimize token counts without sacrificing quality. By transforming complex instructions into concise, effective language, it helps developers reduce operational costs, increase inference speed, and improve the overall clarity of AI outputs across various applications and workflows.

主な機能

Response speed acceleration
High-performance prompt rewriting
883 GitHub stars
Intelligent prompt analysis for redundancy removal
Iterative refinement suggestions
Token usage reduction and cost optimization

ユースケース

Reducing API costs for high-volume LLM applications
Refining complex instructions for better summarization and reasoning
Improving response latency by minimizing input context

概要

主な機能

Response speed acceleration
High-performance prompt rewriting
883 GitHub stars
Intelligent prompt analysis for redundancy removal
Iterative refinement suggestions
Token usage reduction and cost optimization

ユースケース

Reducing API costs for high-volume LLM applications
Refining complex instructions for better summarization and reasoning
Improving response latency by minimizing input context