opus-reasoning-approach

Enforce a six-phase reasoning protocol for complex problem-solving tasks.

Updated Mar 1, 2026
One-click install
npx skills add https://github.com/Plutonian-coder/AI-hms --skill opus-reasoning-approach
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: opus-reasoning-approach
Source: https://github.com/Plutonian-coder/AI-hms/tree/main
Command: npx skills add https://github.com/Plutonian-coder/AI-hms --skill opus-reasoning-approach

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates shallow, first-instinct responses to complex problems by enforcing a structured six-phase reasoning protocol that ensures all constraints are identified, multiple approaches are evaluated, and answers are self-reviewed before delivery.

Core Features & Use Cases

  • Six-Phase Protocol: Decompose the problem, survey knowledge, generate multiple approaches, evaluate depth-first, construct layered explanations, and self-review before responding.
  • Adversarial Self-Skepticism: Actively stress-tests solutions against edge cases, second-order effects, and potential flaws.
  • Use Case: When designing a new system architecture, this Skill ensures you generate 2-4 distinct architectural approaches, evaluate trade-offs like correctness and maintainability, and present a bottom-line-up-front recommendation with concrete next steps.

Quick Start

Use the opus-reasoning-approach skill to analyze the hostel allocation algorithm requirements and recommend the optimal concurrency control strategy.

Frequently Asked Questions about opus-reasoning-approach

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

FAQPage Schema
How do I structure deep reasoning for complex software architecture design?

Structured reasoning for architecture design requires a six-phase protocol: decompose requirements, survey knowledge, generate multiple approaches, evaluate depth-first, construct layered explanations, and self-review before responding.

What is the best way to evaluate multiple approaches for multi-part coding problems?

Evaluating approaches for multi-part coding problems involves generating 2-4 distinct solutions, adversarially stress-testing them against edge cases, and comparing trade-offs like correctness and maintainability.

Why does my AI assistant give shallow responses to ambiguous instruction scenarios?

Shallow responses to ambiguous instruction scenarios occur when first-instinct answers bypass structured reasoning; enforcing an adversarial self-skepticism phase actively stress-tests solutions against edge cases before delivery.

How to analyze ethical dilemmas using a structured decision-making process?

Analyzing ethical dilemmas using structured decision-making requires identifying all constraints, surveying knowledge, evaluating multiple generated approaches depth-first, and executing a self-review checkpoint before finalizing the nuanced response.

When should I not use first-instinct responses for strategy tasks?

First-instinct responses should not be used for strategy tasks when facing complex problem-solving scenarios, as ambiguous constraints require generating multiple approaches and evaluating trade-offs through a layered explanation protocol.