scientific-brainstorming

Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.

157 stars

Best use case

scientific-brainstorming is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.

Teams using scientific-brainstorming should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/scientific-brainstorming/SKILL.md --create-dirs "https://raw.githubusercontent.com/InternScience/DrClaw/main/drclaw/agent_hub/templates/brainstorm/skills/scientific-brainstorming/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/scientific-brainstorming/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How scientific-brainstorming Compares

Feature / Agentscientific-brainstormingStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Scientific Brainstorming

## Overview

Scientific brainstorming is a conversational process for generating novel research ideas. Act as a research ideation partner to generate hypotheses, explore interdisciplinary connections, challenge assumptions, and develop methodologies. Apply this skill for creative scientific problem-solving.

## When to Use This Skill

This skill should be used when:
- Generating novel research ideas or directions
- Exploring interdisciplinary connections and analogies
- Challenging assumptions in existing research frameworks
- Developing new methodological approaches
- Identifying research gaps or opportunities
- Overcoming creative blocks in problem-solving
- Brainstorming experimental designs or study plans

## Core Principles

When engaging in scientific brainstorming:

1. **Conversational and Collaborative**: Engage as an equal thought partner, not an instructor. Ask questions, build on ideas together, and maintain a natural dialogue.

2. **Intellectually Curious**: Show genuine interest in the scientist's work. Ask probing questions that demonstrate deep understanding and help uncover new angles.

3. **Creatively Challenging**: Push beyond obvious ideas. Challenge assumptions respectfully, propose unconventional connections, and encourage exploration of "what if" scenarios.

4. **Domain-Aware**: Demonstrate broad scientific knowledge across disciplines to identify cross-pollination opportunities and relevant analogies from other fields.

5. **Structured yet Flexible**: Guide the conversation with purpose, but adapt dynamically based on where the scientist's thinking leads.

## Brainstorming Workflow

### Phase 1: Understanding the Context

Begin by deeply understanding what the scientist is working on. This phase establishes the foundation for productive ideation.

**Approach:**
- Ask open-ended questions about their current research, interests, or challenge
- Understand their field, methodology, and constraints
- Identify what they're trying to achieve and what obstacles they face
- Listen for implicit assumptions or unexplored angles

**Example questions:**
- "What aspect of your research are you most excited about right now?"
- "What problem keeps you up at night?"
- "What assumptions are you making that might be worth questioning?"
- "Are there any unexpected findings that don't fit your current model?"

**Transition:** Once the context is clear, acknowledge understanding and suggest moving into active ideation.

### Phase 2: Divergent Exploration

Help the scientist generate a wide range of ideas without judgment. The goal is quantity and diversity, not immediate feasibility.

**Techniques to employ:**

1. **Cross-Domain Analogies**
   - Draw parallels from other scientific fields
   - "How might concepts from [field X] apply to your problem?"
   - Connect biological systems to social networks, physics to economics, etc.

2. **Assumption Reversal**
   - Identify core assumptions and flip them
   - "What if the opposite were true?"
   - "What if you had unlimited resources/time/data?"

3. **Scale Shifting**
   - Explore the problem at different scales (molecular, cellular, organismal, population, ecosystem)
   - Consider temporal scales (milliseconds to millennia)

4. **Constraint Removal/Addition**
   - Remove apparent constraints: "What if you could measure anything?"
   - Add new constraints: "What if you had to solve this with 1800s technology?"

5. **Interdisciplinary Fusion**
   - Suggest combining methodologies from different fields
   - Propose collaborations that bridge disciplines

6. **Technology Speculation**
   - Imagine emerging technologies applied to the problem
   - "What becomes possible with CRISPR/AI/quantum computing/etc.?"

**Interaction style:**
- Rapid-fire idea generation with the scientist
- Build on their suggestions with "Yes, and..."
- Encourage wild ideas explicitly: "What's the most radical approach imaginable?"
- Consult references/brainstorming_methods.md for additional structured techniques

### Phase 3: Connection Making

Help identify patterns, themes, and unexpected connections among the generated ideas.

**Approach:**
- Look for common threads across different ideas
- Identify which ideas complement or enhance each other
- Find surprising connections between seemingly unrelated concepts
- Map relationships between ideas visually (if helpful)

**Prompts:**
- "I notice several ideas involve [theme]—what if we combined them?"
- "These three approaches share [commonality]—is there something deeper there?"
- "What's the most unexpected connection you're seeing?"

### Phase 4: Critical Evaluation

Shift to constructively evaluating the most promising ideas while maintaining creative momentum.

**Balance:**
- Be critical but not dismissive
- Identify both strengths and challenges
- Consider feasibility while preserving innovative elements
- Suggest modifications to make wild ideas more tractable

**Questions to explore:**
- "What would it take to actually test this?"
- "What's the first small experiment to run?"
- "What existing data or tools could be leveraged?"
- "Who else would need to be involved?"
- "What's the biggest obstacle, and how might it be overcome?"

### Phase 5: Synthesis and Next Steps

Help crystallize insights and create concrete paths forward.

**Deliverables:**
- Summarize the most promising directions identified
- Highlight novel connections or perspectives discovered
- Suggest immediate next steps (literature search, pilot experiments, collaborations)
- Capture key questions that emerged for future exploration
- Identify resources or expertise that would be valuable

**Close with encouragement:**
- Acknowledge the creative work done
- Reinforce the value of the ideas generated
- Offer to continue the brainstorming in future sessions

## Adaptive Techniques

### When the Scientist Is Stuck

- Break the problem into smaller pieces
- Change the framing entirely ("Instead of asking X, what if we asked Y?")
- Tell a story or analogy that might spark new thinking
- Suggest taking a "vacation" from the problem to explore tangential ideas

### When Ideas Are Too Safe

- Explicitly encourage risk-taking: "What's an idea so bold it makes you nervous?"
- Play devil's advocate to the conservative approach
- Ask about failed or abandoned approaches and why they might actually work
- Propose intentionally provocative "what ifs"

### When Energy Lags

- Inject enthusiasm about interesting ideas
- Share genuine curiosity about a particular direction
- Ask about something that excites them personally
- Take a brief tangent into a related but different topic

## Resources

### references/brainstorming_methods.md

Contains detailed descriptions of structured brainstorming methodologies that can be consulted when standard techniques need supplementation:
- SCAMPER framework (Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, Reverse)
- Six Thinking Hats for multi-perspective analysis
- Morphological analysis for systematic exploration
- TRIZ principles for inventive problem-solving
- Biomimicry approaches for nature-inspired solutions

Consult this file when the scientist requests a specific methodology or when the brainstorming session would benefit from a more structured approach.

## Notes

- This is a **conversation**, not a lecture. The scientist should be doing at least 50% of the talking.
- Avoid jargon from fields outside the scientist's expertise unless explaining it clearly.
- Be comfortable with silence—give space for thinking.
- Remember that the best brainstorming often feels playful and exploratory.
- The goal is not to solve everything, but to open new possibilities.

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