What is Chain of Thought prompting?

Chain of Thought (CoT) is a technique included in this skill that instructs the AI to reason step-by-step before providing a final answer, which significantly improves performance on logical and complex tasks.

Does this skill help with structured data like JSON?

Yes, it specifically includes techniques like response prefilling and explicit formatting instructions to ensure the AI outputs valid JSON, XML, or other structured formats without unnecessary conversational filler.

How does this skill reduce AI hallucinations?

By implementing techniques like 'giving permission to express uncertainty,' the skill encourages the model to admit when it doesn't have enough data rather than speculating or inventing information.

What is the primary benefit of the Prompt Engineering skill?

It provides a standardized set of rules and techniques that help Claude generate more reliable, accurate, and better-formatted responses from any large language model.

Prompt Engineering Best Practices

Name: Prompt Engineering Best Practices
Author: hanai

byhanai

0•

학습 및 문서화

Optimizes, reviews, and troubleshoots LLM prompts using industry-standard engineering techniques for higher accuracy and better formatting.

This skill provides a comprehensive framework for creating high-performance prompts based on official Anthropic best practices. It enables Claude to apply advanced techniques such as Chain of Thought (CoT) reasoning, response prefilling, and structured context providing to your queries. Whether you are building complex agentic workflows or simply trying to get more consistent JSON output, this skill ensures your prompts are explicit, context-aware, and designed to minimize hallucinations while maximizing output quality.

주요 기능

01Chain of Thought (CoT) implementation for complex analytical tasks

020 GitHub stars

03Ambiguity reduction through explicit constraints and motivational context

04Prompt chaining strategies to break down multi-stage complex tasks

05Response prefilling for strict control over structured output like JSON

06Few-shot prompting patterns to demonstrate specific styles and formats

사용 사례

01Refining development prompts to ensure code output follows specific architectural patterns

02Debugging inconsistent LLM responses by applying uncertainty permissions and explicit formatting

03Converting vague instructions into structured, high-accuracy prompts for data extraction

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hanai/cc-marketplace prompt

For use in Claude.ai and ChatGPT

Download Skill