reasoning-tools

Guide selection between code-reasoning and shannon-thinking MCP tools by problem type.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/DxTa/dotfiles --skill reasoning-tools
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: reasoning-tools
Source: https://github.com/DxTa/dotfiles/tree/main/opencode/skills/core/reasoning-tools
Command: npx skills add https://github.com/DxTa/dotfiles --skill reasoning-tools

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you select the most effective AI reasoning tool (code-reasoning vs. shannon-thinking) for your specific problem-solving needs, ensuring efficient and accurate outcomes.

Core Features & Use Cases

  • Decision Framework: Provides a clear, question-based guide to choosing between iterative code-focused thinking and structured, analytical thinking.
  • Comparative Analysis: Offers a quick reference table highlighting the strengths of each tool for various scenarios like debugging, system design, and root cause analysis.
  • Use Case: When faced with a complex system design challenge, this Skill guides you to use shannon-thinking for its structured approach to constraints and modeling, rather than code-reasoning which is better suited for iterative debugging.

Quick Start

Use the reasoning-tools skill to decide whether to use code-reasoning or shannon-thinking for debugging a complex API.

Frequently Asked Questions about reasoning-tools

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

FAQPage Schema
When should I use structured analytical thinking versus iterative code reasoning for debugging?

Use iterative code-reasoning for complex debugging tasks like API tracing, and apply structured analytical thinking when you need formal system design and root cause analysis. This decision framework guides you to the correct AI paradigm based on your specific problem type.

How do I choose the right AI reasoning tool for system design challenges?

To choose the right AI reasoning tool for system design, use a decision framework to evaluate problem constraints. It directs you to structured analytical thinking for modeling constraints, rather than iterative code reasoning which targets iterative debugging scenarios.

What is the difference between code-reasoning and shannon-thinking AI tools?

Code-reasoning focuses on iterative code-focused thinking for tasks like debugging, while shannon-thinking provides structured, analytical thinking for formal system design. A comparative analysis table highlights their distinct strengths for various problem-solving scenarios.

Can I use this decision framework for both root cause analysis and complex API debugging?

Yes, this decision framework supports both root cause analysis and complex API debugging. It provides a clear question-based guide to route root cause analysis to structured analytical thinking and API debugging to iterative code reasoning.

Does this reasoning tool selector require any specific dependencies or environments?

No, this reasoning tool selector requires no external dependencies. It operates as a standalone decision framework, utilizing comparative tables and practical examples to guide your AI paradigm selection without environmental setup.