What is the difference between Zero-Shot and Chain-of-Thought?

Zero-shot is for simple, direct tasks. The skill will recommend Chain-of-Thought (CoT) when your task requires multi-step reasoning, as CoT makes the model's logic explicit and reduces errors.

How does this skill improve my prompts?

It uses a structured conversational workflow to analyze your specific goals and applies research-backed techniques like Chain-of-Thought or few-shot examples to increase accuracy and consistency.

Can it help me get consistent JSON output?

Yes, the skill includes specialized patterns for structured output and can guide you in creating few-shot examples that force the model to adhere to specific schemas.

Is this skill suitable for beginners?

Yes, it acts as a conversational consultant, explaining the 'why' behind its recommendations and adjusting its technical depth based on your level of expertise.

Can I use this to troubleshoot prompts that aren't working?

Absolutely. The skill features a diagnostic path specifically designed to analyze failing prompts, identify ambiguity, and apply structural fixes from its best-practices library.

Prompt Engineering Expert

Name: Prompt Engineering Expert
Author: benjaminjackson

bybenjaminjackson

データサイエンスとML

Optimizes and crafts high-performance LLM prompts using research-backed techniques like Chain-of-Thought and Few-Shot learning.

概要

This skill transforms Claude into a specialized prompt engineering consultant that guides users through a structured, four-phase process to create, troubleshoot, and refine AI prompts. By applying academic research and proven implementation patterns, it helps determine the most effective prompting strategy—ranging from simple zero-shot requests to complex Tree-of-Thought reasoning—ensuring your AI interactions are clear, cost-effective, and produce highly accurate, consistent results.

主な機能

Expert guidance on advanced techniques like Chain-of-Thought and Self-Refine
Diagnostic troubleshooting for fixing inconsistent or poor LLM performance
Cost and latency optimization for production-grade prompt deployment
Structured four-phase workflow: Intake, Analysis, Recommendation, and Construction
Extensive pattern library with ready-to-adapt templates for various domains
0 GitHub stars

ユースケース

Learning and applying industry-standard AI engineering best practices
Troubleshooting and refining existing prompts to improve output quality
Building robust prompts for complex reasoning tasks or structured data extraction

概要

主な機能

Expert guidance on advanced techniques like Chain-of-Thought and Self-Refine
Diagnostic troubleshooting for fixing inconsistent or poor LLM performance
Cost and latency optimization for production-grade prompt deployment
Structured four-phase workflow: Intake, Analysis, Recommendation, and Construction
Extensive pattern library with ready-to-adapt templates for various domains
0 GitHub stars

ユースケース

Learning and applying industry-standard AI engineering best practices
Troubleshooting and refining existing prompts to improve output quality
Building robust prompts for complex reasoning tasks or structured data extraction