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.