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Scientific Brainstorming

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LeonChaoX
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.

Overview

PublisherLeonChaoX
Repositoryqinyan-academic-skills
Skill namescientific-brainstorming
Stars
944
Forks
83
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LeonChaoX on GitHub. Read the source before you install it.

Installation

Install the Scientific Brainstorming AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LeonChaoX/qinyan-academic-skills/tree/main/skills/04-研究方法与科学思维/scientific-brainstorming
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Scientific Brainstorming in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Scientific Brainstorming on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Scientific Brainstorming is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Scientific Brainstorming AI 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.

Why use Scientific Brainstorming on TypingMind?

Because you install it once and use it with any model. Scientific Brainstorming is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Scientific Brainstorming in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LeonChaoX/qinyan-academic-skills/tree/main/skills/04-研究方法与科学思维/scientific-brainstorming. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Scientific Brainstorming?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Scientific Brainstorming?

As many as you like. As long as a model supports skills, you can use Scientific Brainstorming with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Scientific Brainstorming AI skill free?

Yes. It is published on GitHub by LeonChaoX under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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