Does this skill support models other than Claude?

Yes, while it features optimizations for Claude's XML-tag structure, it also includes best practices for GPT-4 and general LLM prompting techniques.

Is this skill useful for reducing hallucinations?

Yes, it includes specific strategies like self-consistency checks, constraint setting, and 'don't know' instructions to significantly improve factual accuracy.

What is the Prompt Engineering Claude Code Skill?

It is a specialized capability that provides expert patterns and guidance for creating, testing, and optimizing prompts for Large Language Models within the Claude Code environment.

How does it improve AI reasoning?

It guides the implementation of Chain-of-Thought (CoT) and 'step-by-step' reasoning techniques, which help the model break down complex logic into manageable segments.

Can it help with structured data output like JSON?

Absolutely. The skill provides specific templates and instructions for forcing LLMs to return valid JSON, XML, or other structured formats reliably for programmatic use.

Prompt Engineering Expert

Name: Prompt Engineering Expert
Author: cameronsjo

bycameronsjo

•

Ciencia de Datos y ML

Provides expert guidance and implementation patterns for crafting highly effective LLM prompts using advanced techniques like chain-of-thought and few-shot learning.

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This skill empowers developers to maximize the performance of AI models by applying proven prompt engineering frameworks directly within their workflow. It offers specialized support for designing, testing, and optimizing prompts for diverse use cases, ranging from complex logical reasoning to structured data extraction and role-based persona creation. By integrating best practices for models like Claude and GPT-4, it helps users troubleshoot poor outputs, reduce hallucinations, and build robust AI-driven features with architectural precision.

Características Principales

Structured output specification using XML tags and JSON schemas
1 GitHub stars
Implementation of Chain-of-Thought (CoT) and multi-step reasoning patterns
Troubleshooting frameworks for common LLM failure modes like hallucinations
Few-shot learning example generation for consistent model behavior
Model-specific optimizations for Anthropic Claude and OpenAI GPT-4

Casos de Uso

Optimizing existing prompt pipelines to improve accuracy and reduce token costs
Designing complex system prompts for autonomous AI agents and chatbots
Building reliable prompt chains for multi-stage data processing and extraction

Acerca de

Características Principales

Structured output specification using XML tags and JSON schemas
1 GitHub stars
Implementation of Chain-of-Thought (CoT) and multi-step reasoning patterns
Troubleshooting frameworks for common LLM failure modes like hallucinations
Few-shot learning example generation for consistent model behavior
Model-specific optimizations for Anthropic Claude and OpenAI GPT-4

Casos de Uso

Optimizing existing prompt pipelines to improve accuracy and reduce token costs
Designing complex system prompts for autonomous AI agents and chatbots
Building reliable prompt chains for multi-stage data processing and extraction