design-converge

Weigh design options into a criteria matrix with scores and a recommendation.

7|1|Updated Mar 19, 2026

Search Tags:#design #architecture #evaluation #prioritization #options #decision-making #matrix

Ready to run

npx skills add https://github.com/Shiien/Self-Evolved-Research-Framework --skill design-converge

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: design-converge
Source: https://github.com/Shiien/Self-Evolved-Research-Framework/tree/main/skills/design-converge
Command: npx skills add https://github.com/Shiien/Self-Evolved-Research-Framework --skill design-converge

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill converts a set of concrete design options into a weighted criteria matrix (novelty / feasibility / alignment / risk) with a recommendation, key risk, and first concrete step. Triggers when the user picks a direction from research-explore, asks "how should we implement X?", or weighs algorithms/architectures.

Core Features & Use Cases

Build a criteria matrix with weights for novelty, feasibility, alignment, and risk.
Generate a top recommendation with rationale, key risk, and a concrete first step.
Apply to architecture or algorithm decisions, or design-path selections.

Quick Start

Use this skill to weigh 2-3 concrete options and obtain a recommended path with next steps.

Frequently Asked Questions about design-converge

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

FAQPage Schema

How do I evaluate architecture options and make a design decision?▼

Evaluating architecture options involves building a weighted criteria matrix that scores concrete design paths on novelty, feasibility, alignment, and risk to output a recommendation and first concrete step.

What is a criteria matrix for design decision-making?▼

A criteria matrix for design decision-making is a scoring model that weighs concrete options against novelty, feasibility, alignment, and risk, producing a top recommendation, key risk, and a first concrete step.

How to compare algorithms and prioritize design paths?▼

Comparing algorithms requires clarifying the decision scope and applying weighted scores for feasibility and risk, which outputs a prioritized design path with a rationale and an actionable first step.

Can I use this for selecting an implementation direction from research?▼

Yes, you can use this process when you select a research direction or ask how to implement a feature, converting 2-3 concrete options into a decision-ready matrix with a recommended path.

What are the limitations of using a weighted matrix for design evaluation?▼

The limitation of a weighted matrix is that it operates strictly on 2-3 concrete options provided, meaning it cannot generate new alternatives or evaluate paths outside the defined decision scope.