Overview

This skill designs and implements chain-of-thought prompting patterns to enable step-by-step reasoning, self-consistency, and tree-of-thought approaches for complex problems. It covers patterns like Zero-shot CoT, Few-shot CoT, Self-Consistency, Tree-of-Thought, and ReAct, and provides configuration options and best practices for robust reasoning outputs.

How This Skill Works

It provides modular CoT patterns and templates, plus verification and output structuring. It includes configuration options such as trigger phrases, step formats, chain length, and voting thresholds, and integrates with prompt-engineering workflows and self-reflection agents using langchain-core.

When to Use It

• When crafting prompts that require multi-step solutions (math, planning, or reasoning tasks) in AI agents
• When you need transparent, auditable reasoning paths for debugging or evaluation
• When experimenting with alternative reasoning strategies like Self-Consistency or Tree-of-Thought
• When building self-reflecting agents that verify their own conclusions
• When integrating reasoning prompts into prompt-engineering workflows and agents

Quick Start

1. Step 1: Choose a CoT pattern (Zero-Shot, Few-Shot, Self-Consistency, Tree-of-Thought, or ReAct) and decide on trigger phrases.
2. Step 2: Define the step format and add verification prompts; set chain length and consistency thresholds.
3. Step 3: Run experiments within a prompt-engineering workflow and iterate based on reasoning quality feedback.

Best Practices

• Use clear reasoning step markers to separate thoughts from conclusions
• Extract the final answer explicitly at the end of reasoning
• Inject verification prompts to test the validity of steps
• Plan for and handle potential reasoning errors or dead ends
• Monitor reasoning quality with consistency checks and voting thresholds

Example Use Cases

• Zero-Shot CoT prompts like 'Let's think step by step' used in math word problems
• Few-Shot CoT with curated examples that illustrate reasoning paths for QA tasks
• Self-Consistency by sampling multiple reasoning paths to select robust answers
• Tree-of-Thought prompts that branch into subproblems before reaching a conclusion
• ReAct-style prompts that interleave reasoning with actions (queries, lookups)

Frequently Asked Questions