Can this skill help generate structured JSON from LLMs?

Yes, it includes specific workflows for implementing structured outputs, JSON mode, and schema validation to ensure reliable data extraction.

Is this skill useful for reducing AI API costs?

Absolutely. It focuses on optimization techniques that improve token efficiency and reduce latency while maintaining high-quality outputs.

What is the Prompt Engineer Claude Code skill?

It is a specialized plugin for Claude Code that provides expert guidance on designing, testing, and optimizing prompts for Large Language Models.

How does this skill assist with prompt testing?

It provides a systematic core workflow that includes defining success criteria, running diverse test cases, and measuring performance with quantitative metrics.

Prompt Engineer

Name: Prompt Engineer
Author: Jeffallan

byJeffallan

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数据科学与机器学习

Designs, optimizes, and evaluates high-performance LLM prompts using advanced techniques like chain-of-thought and few-shot learning.

关于

The Prompt Engineer skill transforms Claude into a specialized consultant for Large Language Model optimization, enabling developers to build robust AI applications through systematic prompt design and evaluation. It provides deep expertise in maximizing model reliability and quality while managing token efficiency and cost. Whether you are implementing complex chain-of-thought reasoning, defining structured JSON outputs, or migrating prompts between model versions, this skill provides the frameworks and best practices necessary to move from experimental chat to production-grade AI integration.

主要功能

Advanced prompt design using chain-of-thought and few-shot learning
Systematic evaluation frameworks for measuring prompt performance
Structured output implementation for JSON mode and function calling
49 GitHub stars
Persona-driven system prompt creation with integrated guardrails
Token optimization strategies to reduce latency and costs

使用场景

Developing production-ready system prompts for new AI-powered features
Debugging and fixing inconsistent or low-quality LLM responses
Building automated test suites to benchmark prompt accuracy across model updates

关于

主要功能

Advanced prompt design using chain-of-thought and few-shot learning
Systematic evaluation frameworks for measuring prompt performance
Structured output implementation for JSON mode and function calling
49 GitHub stars
Persona-driven system prompt creation with integrated guardrails
Token optimization strategies to reduce latency and costs

使用场景

Developing production-ready system prompts for new AI-powered features
Debugging and fixing inconsistent or low-quality LLM responses
Building automated test suites to benchmark prompt accuracy across model updates