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customaize-agent:thought-based-reasoning

Generate structured chain-of-thought prompts for multi-step reasoning tasks.

Updated Apr 6, 2026
Search Tags:#react#chain-of-thought#self-consistency#tree-of-thoughts#zero-shot-cot#pal#least-to-most
One-click install
npx skills add https://github.com/luicabref97/sushi-jungle-web --skill customaize-agent-thought-based-reasoning-luicabref97
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: customaize-agent:thought-based-reasoning
Source: https://github.com/luicabref97/sushi-jungle-web/tree/main/.agents/skills/customaize-agent-thought-based-reasoning
Command: npx skills add https://github.com/luicabref97/sushi-jungle-web --skill customaize-agent-thought-based-reasoning-luicabref97

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides structured prompting patterns and decision frameworks to improve model performance on complex, multi-step reasoning tasks where simple prompts fail, reducing errors in arithmetic, symbolic manipulation, multi-hop QA, and logical deduction.

Core Features & Use Cases

  • Chain-of-Thought (CoT) & Zero-shot CoT: Elicit intermediate reasoning steps to increase accuracy on math and commonsense problems.
  • Self-Consistency & Sampling: Generate diverse reasoning paths and aggregate results to reduce variance and increase confidence.
  • Tree of Thoughts & Least-to-Most: Explore and backtrack complex solution trees and decompose hard problems into subproblems for stepwise solving.
  • ReAct & PAL: Combine reasoning with external actions (search, lookups) and program execution for grounded, precise computation.
  • Auto-CoT & Reflexion: Automate exemplar generation and iteratively learn from failures to improve subsequent attempts.
  • Use Case Example: Solve multi-step math word problems with PAL for exact calculation, apply Tree of Thoughts for puzzle solving, or use ReAct for multi-hop factual queries that require web lookups.

Quick Start

Ask the agent to solve the problem step by step and explicitly show its intermediate reasoning.

Frequently Asked Questions about customaize-agent:thought-based-reasoning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I use chain-of-thought prompting to improve LLM accuracy on math problems?▼

Chain-of-thought prompting elicits intermediate reasoning steps to increase accuracy on math and commonsense problems by asking the model to solve the task step by step.

What is the best way to decompose complex multi-step reasoning tasks for large language models?▼

Least-to-most decomposition breaks hard problems into subproblems for stepwise solving, while Tree of Thoughts explores and backtracks complex solution trees to improve multi-step reasoning.

Can I use ReAct and PAL prompting patterns for multi-hop question answering and code debugging?▼

ReAct combines reasoning with external actions like web lookups for multi-hop factual queries, while PAL uses program execution for grounded, precise computation in code debugging.

Does self-consistency sampling reduce variance in zero-shot CoT reasoning paths?▼

Self-consistency sampling generates diverse reasoning paths and aggregates results to reduce variance and increase confidence in zero-shot CoT outputs.

How does iterative Reflexion automate exemplar generation for complex logical deduction?▼

Auto-CoT automates exemplar generation, while iterative Reflexion workflows learn from failures to improve subsequent attempts at logical deduction and symbolic manipulation.

When should I not use Tree of Thoughts search for LLM planning tasks?▼

Tree of Thoughts search adds overhead for exploring and backtracking complex solution trees, making it less suitable for simple planning tasks where standard prompting already succeeds.

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