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

学習とドキュメンテーション

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

概要

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.

主な機能

Chain of Thought (CoT) implementation for complex analytical tasks
0 GitHub stars
Ambiguity reduction through explicit constraints and motivational context
Prompt chaining strategies to break down multi-stage complex tasks
Response prefilling for strict control over structured output like JSON
Few-shot prompting patterns to demonstrate specific styles and formats

ユースケース

Refining development prompts to ensure code output follows specific architectural patterns
Debugging inconsistent LLM responses by applying uncertainty permissions and explicit formatting
Converting vague instructions into structured, high-accuracy prompts for data extraction

概要

主な機能

Chain of Thought (CoT) implementation for complex analytical tasks
0 GitHub stars
Ambiguity reduction through explicit constraints and motivational context
Prompt chaining strategies to break down multi-stage complex tasks
Response prefilling for strict control over structured output like JSON
Few-shot prompting patterns to demonstrate specific styles and formats

ユースケース

Refining development prompts to ensure code output follows specific architectural patterns
Debugging inconsistent LLM responses by applying uncertainty permissions and explicit formatting
Converting vague instructions into structured, high-accuracy prompts for data extraction