idea-creator

Generate, validate, and rank research ideas with GPU budget constraints.

2|Updated Aug 12, 2025
One-click install
npx skills add https://github.com/goupup-ai/miccai25 --skill idea-creator-goupup-ai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: idea-creator
Source: https://github.com/goupup-ai/miccai25/tree/main/ARIS/skills/idea-creator
Command: npx skills add https://github.com/goupup-ai/miccai25 --skill idea-creator-goupup-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Researchers often waste weeks brainstorming vague, unvalidated research ideas that may lack novelty or feasibility, leading to dead-end projects and rejected submissions. This skill automates the full research idea pipeline, from landscape surveying to pilot validation, to surface high-potential, publishable research directions efficiently.

Core Features & Use Cases

  • End-to-end idea workflow: Automates literature surveying, divergent idea generation, budget-based feasibility filtering, cross-model novelty triage, and pilot experiment design.
  • Structured ranking: Ranks ideas by novelty, feasibility, and empirical pilot signal to prioritize the most promising directions for full development.
  • Use case: A researcher working on medical image segmentation can input "improving low-resolution CT scan segmentation" to get 8-12 vetted, ranked research ideas with clear next steps, instead of spending weeks on unproductive brainstorming.

Quick Start

Use the idea-creator skill with your specific research direction, like "low-resource medical image segmentation for rare diseases", to receive a ranked list of vetted, publishable research ideas with pilot validation results and clear execution priorities.

Frequently Asked Questions about idea-creator

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

FAQPage Schema
How do I generate publishable research ideas for machine learning?

To generate publishable research ideas for machine learning, you input a broad research direction to receive 8-12 vetted, ranked ideas. The system automates landscape surveying, novelty validation, and pilot experiment planning to surface high-potential directions efficiently.

What is the best way to validate research novelty for computer vision projects?

The best way to validate research novelty for computer vision projects is using cross-model reviewer triage. This mechanism evaluates generated ideas against existing literature to verify their uniqueness before you invest time in full development or pilot experiments.

Can I use automated pilot experiments for medical imaging with strict GPU budget constraints?

Yes, you can use automated pilot experiments for medical imaging with strict GPU budget constraints. The system explicitly enforces GPU budget limits during pilot run planning to ensure feasibility filtering aligns with your available computational resources.

How do I rank research brainstorming ideas by feasibility and novelty?

You rank research brainstorming ideas by feasibility and novelty through structured ranking. The system evaluates and prioritizes generated ideas based on their novelty, empirical pilot signal, and execution feasibility to highlight the most promising directions.

Does this research workflow tool integrate with research wiki tools for tracking?

Yes, this research workflow tool integrates with research wiki tools for persistent idea tracking. This integration supports spiral learning by maintaining a continuous record of your idea generation, validation, and pilot experiment results across projects.