reason

Decompose complex reasoning tasks recursively and reconstruct modular solutions.

7|3|Updated Jan 15, 2026

Search Tags:#decomposition #problem-solving #structure #reasoning #metacognition #recursive

Ready to run

npx skills add https://github.com/Zpankz/mcp-skillset --skill reason-zpankz

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: reason
Source: https://github.com/Zpankz/mcp-skillset/tree/main/reason
Command: npx skills add https://github.com/Zpankz/mcp-skillset --skill reason-zpankz

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Clear, structured reasoning via recursive decomposition and modular reconstruction to break complex questions into manageable parts and produce interpretable results.

Core Features & Use Cases

Recursive decomposition to break problems into self-similar subproblems.
Branching, reduction, grounding, and emitting to synthesize robust, traceable answers.
Supports meta-cognitive guidance and explicit premise-tracing for explainable outcomes.

Quick Start

Describe a complex problem and apply recursive decomposition to produce a structured solution with trace.

Frequently Asked Questions about reason

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

FAQPage Schema

How do I break down complex problems using recursive decomposition?▼

Recursive decomposition breaks complex problems into self-similar subproblems, solving each manageable part before synthesizing a final, traceable answer through modular reconstruction.

What is structured reasoning and when do I need it for problem-solving?▼

Structured reasoning imposes a deterministic workflow to explore multiple solution paths and synthesize robust answers. It is needed when problems require explicit premise-tracing and interpretable outcomes.

How do I apply modular reconstruction to synthesize a traceable answer?▼

Modular reconstruction synthesizes a traceable answer by applying branching, reduction, and grounding steps to subproblems, ensuring deterministic reasoning and explainable outcomes across domains.

Does this problem-solving approach support meta-cognitive guidance?▼

Yes, the reasoning process supports meta-cognitive guidance by imposing structured workflow steps and explicit premise-tracing, enabling deterministic reasoning and fully traceable outputs.

Can I use external resources to support deterministic reasoning?▼

Yes, deterministic reasoning supports optional resources via scripts, references, and assets, allowing you to ground subproblems and produce structured solutions with traceable outputs.

What is the best way to ensure interpretable results for multi-domain questions?▼

The best way to ensure interpretable results is applying recursive decomposition to break questions into manageable parts, then synthesizing a final answer through modular reconstruction.