moai-foundation-thinking

Applies structured thinking frameworks for ideation, evaluation, and architecture decisions.

Updated Jul 24, 2026
One-click install
npx skills add https://github.com/jjjh7401/AI-Lighting_Console --skill moai-foundation-thinking-jjjh7401
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: moai-foundation-thinking
Source: https://github.com/jjjh7401/AI-Lighting_Console/tree/main/.claude/skills/moai-foundation-thinking
Command: npx skills add https://github.com/jjjh7401/AI-Lighting_Console --skill moai-foundation-thinking-jjjh7401

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Unstructured reasoning leads to premature convergence on weak ideas, missed assumptions, and flawed architecture decisions. This Skill provides repeatable frameworks for generating alternatives, evaluating proposals rigorously, and questioning requirements deeply before committing to a solution. ## Core Features & Use Cases - Critical Evaluation: A 7-step process to restate claims, assess evidence, detect fallacies, expose assumptions, and check contradictions before accepting a proposal. - Diverge-Converge Brainstorming: A 5-phase workflow that generates 20-50 raw ideas, clusters them into themes, and scores candidates with weighted criteria to select 3-5 validated solutions. - Deep Questioning: A 6-layer progressive inquiry method that uncovers root causes, hidden constraints, user needs, and validation risks. - First Principles Reasoning: A 5-phase strategic analysis (assumption audit, decomposition, alternative generation, trade-off analysis, cognitive bias check) for architecture and technology selection. - Use Case: When choosing between state management libraries for a React app, run the First Principles process to audit assumptions, generate options like Redux Toolkit and Zustand, score them with weighted trade-off criteria, and check for anchoring bias before recommending. ## Quick Start Ask the AI to apply the diverge-converge framework to generate and evaluate solution options for your current design problem.

Frequently Asked Questions about moai-foundation-thinking

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

FAQPage Schema
How do I run a structured brainstorming session for a design problem?▼

Use the diverge-converge framework: gather requirements, generate 20-50 ideas without filtering, cluster them into 4-8 themes, score clusters with weighted criteria like feasibility and impact, then document the top 3-5 candidates with validation plans.

What framework should I use for architecture decision making?▼

Use the First Principles five-phase process: audit assumptions, decompose the problem to fundamental truths, generate 5-10 alternatives, score trade-offs across correctness, performance, and maintainability, then check for cognitive biases like anchoring and sunk cost.

How do I evaluate a technical proposal for hidden flaws?▼

Apply the 7-step critical evaluation: restate the claim, assess evidence quality, detect logical fallacies, expose unstated assumptions, generate alternative explanations, check internal contradictions, and verify the burden of proof matches the claim's magnitude.

When should I use deep questioning versus direct implementation?▼

Use deep questioning for features, architecture decisions, and unfamiliar domains where requirements are unclear. Skip it for simple tasks touching 1-2 files. The six layers progress from surface understanding through root cause, constraints, users, solution boundaries, and validation risks.

Does this skill work with the ultrathink keyword for deep reasoning?▼

Yes, the ultrathink keyword triggers extended reasoning on Opus 4.7 and later models, complementing the creative frameworks. Adaptive Thinking allocates reasoning depth automatically based on task complexity, and ultrathink forces deeper reasoning when needed.

What are the limitations of structured thinking frameworks?▼

These frameworks add process overhead unsuited for trivial tasks like quick bug fixes or configuration changes. They also depend on honest input: shallow answers during deep questioning or fewer than three alternatives during divergence undermine the output quality.