chain-of-thought

Chain-of-Thought (CoT) prompting elicits step-by-step reasoning from LLMs, dramatically improving performance on complex reasoning, math, and logic tasks.

Skills
AI research
0

What it does and how to use it

A skill that teaches Claude Code to do ai research tasks well. It is packaged knowledge the model uses whenever it is relevant.

Install the skill (command above).
In Claude Code, describe what you need - the skill turns on by itself when the context fits.
No commands to memorize: you talk in plain language and the skill does the rest.

Install (Claude Code)

mkdir -p .claude/skills/chain-of-thought && curl -fsSL https://doutorai.pt/data/lib/skills__chain-of-thought.md -o .claude/skills/chain-of-thought/SKILL.md

Paste this command into the terminal, in your project folder, and the component installs automatically.

Content

# Chain-of-Thought Prompting

## Overview

Chain-of-Thought (CoT) prompting elicits step-by-step reasoning from LLMs, dramatically improving performance on complex reasoning, math, and logic tasks.

## Core Techniques

### Zero-Shot CoT
Add a simple trigger phrase to elicit reasoning:

```python
def zero_shot_cot(query):
    return f"""{query}

Let's think step by step:"""

# Example
query = "If a train travels 60 mph for 2.5 hours, how far does it go?"
prompt = zero_shot_cot(query)

# Model output:
# "Let's think step by step:
# 1. Speed = 60 miles per hour
# 2. Time = 2.5 hours
# 3. Distance

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