What frameworks does this skill use?

It leverages several structured methodologies including SCAMPER, Six Thinking Hats, Morphological analysis, and TRIZ principles for inventive problem-solving.

How does this differ from standard hypothesis generation?

Scientific Brainstorming is for early-stage, open-ended exploration when you don't have specific data yet. For formulating testable hypotheses from existing data, use the hypothesis-generation skill.

Can I use this for interdisciplinary research?

Yes, the skill is specifically programmed to draw analogies from diverse fields like biology, physics, and economics to provide fresh perspectives.

Is this suitable for brainstorming experimental designs?

Absolutely. Phase 4 and 5 of the workflow focus on evaluating feasibility and creating concrete next steps for experimental planning and study designs.

Scientific Brainstorming

Name: Scientific Brainstorming
Author: Sologa

bySologa

Data Science & ML

Facilitates creative research ideation and exploratory scientific problem-solving through interdisciplinary analogies and assumption-challenging frameworks.

About

Scientific Brainstorming is a specialized Claude Code skill designed to serve as a high-level research ideation partner for scientists and academics. It guides users through a structured five-phase workflow—Context, Divergent Exploration, Connection Making, Critical Evaluation, and Synthesis—to move from broad curiosity to concrete research directions. By employing techniques like cross-domain analogies, scale shifting, and assumption reversal, it helps researchers identify novel gaps, overcome intellectual blocks, and develop innovative methodological approaches during the early stages of planning.

Key Features

Assumption reversal techniques to challenge existing research paradigms
0 GitHub stars
Scale-shifting analysis from molecular to ecosystem levels
Integrated support for SCAMPER, TRIZ, and Morphological analysis frameworks
Five-phase structured brainstorming workflow for scientific rigor
Cross-domain analogy mapping to bridge disparate scientific fields

Use Cases

Identifying novel research gaps and interdisciplinary project directions
Overcoming creative plateaus in long-term scientific investigations
Developing innovative experimental designs and study plans

About

Key Features

Assumption reversal techniques to challenge existing research paradigms
0 GitHub stars
Scale-shifting analysis from molecular to ecosystem levels
Integrated support for SCAMPER, TRIZ, and Morphological analysis frameworks
Five-phase structured brainstorming workflow for scientific rigor
Cross-domain analogy mapping to bridge disparate scientific fields

Use Cases

Identifying novel research gaps and interdisciplinary project directions
Overcoming creative plateaus in long-term scientific investigations
Developing innovative experimental designs and study plans