# Reasoning

> Prompting patterns that encourage the model to articulate its specific thought process (Chain of Thought) to improve performance on complex logical, mathematical, or reasoning tasks. Use when user asks to "improve agent reasoning", "add chain-of-thought", "logical reasoning", or mentions inference, deductive reasoning, or structured thinking.

- Skill: `lauraflorentin/reasoning` (Agent Skill)
- Install (CLI): `npx skillmds@latest add lauraflorentin/reasoning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lauraflorentin/reasoning/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: lauraflorentin (https://skillmd.com/u/lauraflorentin)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/lauraflorentin/reasoning

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# Reasoning Techniques

Reasoning techniques (like Chain-of-Thought, Tree-of-Thought) force the LLM to show its work. Large Language Models are statistical, not logical. By making them output a step-by-step reasoning path before the final answer, you allow the model to provide context to itself, significantly reducing logic errors and "hallucinations of calculation".

## When to Use

-   **Math & Logic**: Word problems, puzzles, navigation tasks.
-   **Complex Planning**: "How do I move this couch through this door?"
-   **Legal/Medical reasoning**: Deriving a conclusion from a set of rules and facts.
-   **Debugging**: Asking the model to explain *why* code is failing before fixing it.

## Use Cases

-   **Zero-Shot CoT**: Simply adding "Let's think step by step" to the prompt.
-   **Few-Shot CoT**: Providing examples of [Question -> Reasoning -> Answer] to guide the model.
-   **Self-Consistency**: Generating 5 different Chain-of-Thought paths and picking the answer that appears most frequently (Majority Voting).
-   **Tree of Thoughts**: Exploring multiple possible reasoning branches and backtracking if one leads to a dead end.

## Implementation Pattern

```python
def chain_of_thought_prompt(question):
    prompt = f"""
    Question: {question}
    
    Instruction: Answer the question by reasoning step-by-step. 
    Format your answer as:
    
    Reasoning:
    1. [First Step]
    2. [Second Step]
    ...
    
    Final Answer: [Answer]
    """
    
    return llm.generate(prompt)
```