scientific-brainstorming
Structured ideation methods: SCAMPER, Six Thinking Hats, Morphological Analysis, TRIZ, Biomimicry, plus more. Decision framework for picking methods by challenge type (stuck, improving, systematic exploration, contradiction)
Install / Use
npx skills add jaechang-hits/SciAgent-Skills --skill scientific-brainstormingInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Education & ResearchSupported Platforms
Tags
Our assessment of scientific-brainstorming
scientific-brainstorming scores 87/100 on our quality scale, 253rd of 437 Education & Research skills we index.
Its SKILL.md is 28 KB long, well organised into 22 sections with 1 code example: a thorough specification that gives an agent plenty to work with.
It has 367 GitHub stars, a meaningful sign that others use it.
Maintenance, license and trust
- The repository was last updated 37 days ago, so scientific-brainstorming is actively maintained.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-10-05. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
scientific-brainstorming compared with similar skills
All 4 of these similar skills score higher than scientific-brainstorming; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| scientific-brainstorming (this skill)by jaechang-hits | 87 | 367 | 37d ago | SKILL.md |
| last30days-skillby mvanhorn | 100 | 63.5k | today | CLAUDE.md |
| algorithmic-artby anthropics | 100 | 177.9k | 12d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 12d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 130.2k | 13d ago | SKILL.md |
Frequently asked questions
- How do I install scientific-brainstorming?
- Run
npx skills add jaechang-hits/SciAgent-Skills --skill scientific-brainstorming. The install tabs above show the steps for each supported agent. - Which AI agents does scientific-brainstorming work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is scientific-brainstorming safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is scientific-brainstorming still maintained?
- The repository was last updated 37 days ago, so scientific-brainstorming is actively maintained.
Skill content
View source on GitHubname: "scientific-brainstorming" description: "Structured ideation methods: SCAMPER, Six Thinking Hats, Morphological Analysis, TRIZ, Biomimicry, plus more. Decision framework for picking methods by challenge type (stuck, improving, systematic exploration, contradiction). Use when generating research ideas or exploring interdisciplinary connections." license: "CC-BY-4.0"
Scientific Brainstorming
Overview
Scientific brainstorming is a structured ideation process for generating, connecting, and evaluating research ideas. Unlike casual brainstorming, scientific brainstorming applies formal methodologies (SCAMPER, TRIZ, Morphological Analysis, etc.) matched to the specific creative challenge. The process moves through divergent exploration, connection-making, critical evaluation, and synthesis to produce actionable research directions with testable hypotheses.
Key Concepts
Core Principles
Five principles guide effective scientific brainstorming sessions:
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Collaborative: Brainstorming works best as dialogue, not monologue. Build on each other's ideas rather than presenting finished thoughts. Use "Yes, and..." framing to extend ideas before evaluating them. In AI-assisted sessions, the scientist contributes domain expertise and the AI contributes breadth and pattern-matching across disciplines.
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Curious: Approach the problem space with genuine curiosity. Ask "what if" and "why not" before "why." Suspend expertise-driven assumptions temporarily to allow unexpected connections. Experts often dismiss novel directions because they conflict with established mental models -- curiosity counteracts this.
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Domain-Aware: Ground brainstorming in real scientific constraints. Ideas must eventually connect to testable hypotheses, available methods, and feasible experiments. Domain knowledge channels creativity productively. Pure creativity without domain grounding produces ideas that cannot be tested; pure domain expertise without creativity produces incremental work.
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Structured: Use formal ideation methods rather than unguided free association. Structure prevents cognitive fixation (repeatedly returning to the same idea space) and ensures systematic coverage of the possibility space. Unstructured brainstorming sessions typically explore less than 20% of the available idea space.
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Challenging: Actively seek ideas that feel uncomfortable or counterintuitive. The most productive brainstorming sessions push past obvious solutions into territory that requires deeper analysis. If every idea generated feels reasonable and safe, the session is not pushing hard enough.
Brainstorming Methods Catalog
Nine structured methodologies, each suited to different creative challenges:
| Method | When to Use | Key Technique | Scientific Example | |--------|-------------|---------------|-------------------| | SCAMPER | Improving or extending an existing method/system | Systematically apply 7 operators (Substitute, Combine, Adapt, Modify, Put to use, Eliminate, Reverse) to the current approach | Substitute fluorescence for radioactive labeling in an assay; Combine two biomarker panels into a multiplex panel | | Six Thinking Hats | Need multiple perspectives on a research question | Assign structured roles: White (data/facts), Red (intuition/feelings), Black (critical/risks), Yellow (benefits/optimism), Green (creative alternatives), Blue (process management) | Evaluate a proposed clinical trial: White examines prior data, Black identifies ethical risks, Green suggests novel endpoints | | Morphological Analysis | Exploring all combinations within a design space | Define dimensions of the problem, list options per dimension, systematically explore combinations | Drug delivery: dimensions = carrier (liposome, nanoparticle, hydrogel) x targeting (passive, active, magnetic) x release (pH, thermal, enzymatic) | | TRIZ | Resolving technical contradictions | Identify the contradiction (improving X worsens Y), apply inventive principles, envision the ideal final result | Increasing drug potency (desired) increases toxicity (undesired) -- apply separation principle: target only affected tissue | | Biomimicry | Seeking nature-inspired solutions | Define function, biologize the question, discover natural models, abstract the principle, apply to problem | "How does nature filter particles?" leads to studying kidney nephrons for microfluidic filter design | | Provocation (Po) | Breaking out of fixed thinking patterns | State an impossible or absurd premise ("Po: cells never divide"), then extract useful principles from the provocation | "Po: proteins fold instantly" -- what if we engineered ultrafast folding domains? Leads to intrinsically disordered protein research | | Random Input | Need fresh connections when stuck in a rut | Select a random stimulus (word, image, object from nature), force connections to the research problem | Random word "bridge" + enzyme kinetics = bridging molecules that connect substrate to enzyme active site | | Reverse Assumptions | Questioning fundamental assumptions | List all assumptions about the problem, flip each one, explore consequences of each reversal | Assumption: "higher purity improves results" -- reverse: what if impurities are functional? Leads to studying beneficial contaminants | | Future Backwards | Envisioning long-term research directions | Start from a solved future state, work backwards to identify necessary intermediate steps and breakthroughs | "Cancer is cured in 2050" -- what needed to happen in 2040? 2030? What research today enables the 2030 milestone? |
SCAMPER Operators in Detail
The seven SCAMPER operators applied to scientific contexts:
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Substitute: Replace one component with another. What material, reagent, model organism, or technique could replace the current one? Example: replace mouse models with organoids; substitute CRISPR for siRNA knockdown.
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Combine: Merge two approaches, techniques, or datasets. What happens if you combine two assays into one? Two datasets from different modalities? Example: combine proteomics and metabolomics in a single sample preparation.
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Adapt: Borrow a technique from another field. What methods from physics, engineering, or computer science could solve this biological problem? Example: adapt semiconductor lithography for tissue engineering scaffolds.
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Modify: Change the scale, frequency, intensity, or duration. What if you ran the experiment 10x faster, at 100x concentration, or at a different temperature? Example: single-molecule resolution instead of bulk measurement.
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Put to other use: Apply an existing tool or finding to a different purpose. What other questions could this dataset answer? What other diseases could this drug treat? Example: repurpose failed drug candidates for new indications.
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Eliminate: Remove a step, component, or constraint. What if you eliminated the purification step? The control group? The assumption of linearity? Example: label-free detection instead of fluorescent tagging.
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Reverse: Invert the order, direction, or perspective. What if you worked backwards from the output? Reversed the cause-effect relationship? Example: start from the phenotype and work backwards to the genotype.
TRIZ Core Concepts
TRIZ (Theory of Inventive Problem Solving) provides three key concepts for scientific brainstorming:
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Technical Contradiction: Improving one parameter worsens another. Example: increasing drug selectivity (desired) reduces potency (undesired). TRIZ provides 40 inventive principles organized in a contradiction matrix to resolve such trade-offs systematically rather than by trial and error.
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Ideal Final Result (IFR): Envision the perfect solution where the desired function is achieved with zero cost, zero harm, and zero complexity. Working backwards from the IFR reveals which constraints are real and which are assumed. Example: the ideal drug delivers itself precisely to the target, requires no administration, and has no side effects -- what existing technology gets closest?
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Inventive Principles: The most commonly applicable principles in scientific research include:
- Segmentation: divide a system into independent parts
- Extraction: separate an interfering component or property
- Local Quality: vary conditions locally rather than globally
- Asymmetry: introduce useful asymmetry into a symmetric system
- Nesting: place one system inside another (e.g., nanoparticle within liposome)
- Prior Action: perform required changes in advance (e.g., pre-functionalization)
- Dynamization: allow a system to change to achieve optimal conditions at each stage
Biomimicry Process Steps
The Biomimicry design spiral follows five steps:
- Define: What function do you need? Frame the problem as a verb, not a noun. "How does nature filter?" not "How does nature make a filter?"
- Biologize: Translate the function into biological terms. "What organisms need to separate particles from fluid?"
- Discover: Search for organisms that have solved this problem. Use resources like AskNature.org or consult biological literature.
- Abstract: Extract the design principle from the biological model. Not "copy the kidney" but "use countercurrent flow with selective permeability membranes."
- Apply: Translate the abstracted principle back to the research problem. Design the experiment or technology using the biological principle as inspiration.
Method Categories
Brainstorming methods serve three distinct cognitive functions. Effective sessions use methods from multiple categories:
- Divergent methods expand the idea space: SCAMPER, Provocation, Random Input, Future Backwards. Use these when you need more options or feel stuck with too few ideas. These methods deliberately break existing thought patterns.
- Connecting methods find relationships between ideas: Morphological Analysis, Biomimicry, Reverse Assumptions. Use these when you have elements that might relate but have not identified how. These methods build structure across disparate concepts.
- Convergent methods evaluate and select: Six Thinking Hats, TRIZ. Use these when you have many ideas and need to identify which are most promising or how to resolve contradictions. These methods apply systematic judgment.
A complete brainstorming session should use at least one method from each category, typically in the order: divergent first, connecting second, convergent third.
Method Combinations
Certain methods pair well for deeper exploration. The key principle is to combine methods from different cognitive categories (divergent + connecting, or connecting + convergent):
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SCAMPER + Six Hats: Generate modifications with SCAMPER (divergent), then evaluate each modification from six perspectives (convergent). Particularly effective for experimental protocol refinement.
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Morphological Analysis + TRIZ: Map the design space with Morphological Analysis (connecting), then use TRIZ (convergent) to resolve contradictions that emerge in promising but conflicting combinations.
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Biomimicry + Provocation: Use Biomimicry (connecting) to find natural solutions, then apply Provocation (divergent) to push beyond biological constraints into engineered solutions that nature has not explored.
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Reverse Assumptions + Future Backwards: Challenge current assumptions first (connecting), then project forward from those reversed assumptions to envision alternative research trajectories (divergent).
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Random Input + Morphological Analysis: Use Random Input (divergent) to discover a new dimension for the morphological matrix (connecting) that was not previously considered. This often reveals blind spots in the design space.
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Three-method sequence (recommended for full sessions): Sta
Truncated for display — read the full file on GitHub.
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From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
