method-designer

Convert research idea units into structured method designs and experiment matrices.

5|Updated Apr 13, 2026
One-click install
npx skills add https://github.com/caozx1110/ResearchLab --skill method-designer
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: method-designer
Source: https://github.com/caozx1110/ResearchLab/tree/main/skills/method-designer
Command: npx skills add https://github.com/caozx1110/ResearchLab --skill method-designer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill addresses the gap between conceptual research ideas and actionable implementation plans by enforcing a rigorous, multi-stage design process that prevents premature execution.

Core Features & Use Cases

  • Method Handoff: Converts abstract research ideas into concrete method notes, interface contracts, and experiment matrices.
  • Resource-Aware Planning: Automatically scales experiment matrices based on declared GPU and compute capacity.
  • Verification Gate: Ensures all methodological claims are backed by evidence and requires explicit user confirmation before advancing to implementation.

Quick Start

Use the method-designer skill to prepare a new method design for the selected idea in the current program.

Frequently Asked Questions about method-designer

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

FAQPage Schema
How do I convert research ideas into structured experiment designs?

Converting research ideas into structured experiment designs involves transforming abstract idea units into concrete method notes, interface contracts, and experiment matrices using a multi-stage prepare, verify, and confirm workflow.

What is the best way to prevent premature execution in research methodology design?

The best way to prevent premature execution in research methodology design is enforcing a rigorous verification gate that validates all methodological claims with evidence and requires explicit user confirmation before advancing to implementation.

How does resource-aware planning work for scaling experiment matrices?

Resource-aware planning scales experiment matrices by automatically adjusting the design scope based on declared GPU and compute capacity within a program-scoped research workspace to align repository selection with available resources.

Do I need to declare GPU and compute capacity before designing an experiment matrix?

Yes, you need to declare GPU and compute capacity before designing an experiment matrix because the method designer automatically scales the experiment matrices based on these declared compute resources and user-defined constraints.

Can I use this methodology design tool for repository selection alignment?

Yes, this methodology design tool supports repository selection alignment by operating within a program-scoped research workspace to align repository selection with available compute resources and user-defined constraints.

Why does method design require explicit user confirmation before implementation?

Method design requires explicit user confirmation before implementation to act as a verification gate ensuring all methodological claims are backed by evidence, thereby maintaining research integrity and preventing premature execution.