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scientific-brainstorming skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.

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Install the scientific-brainstorming skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/scientific-brainstorming ~/.claude/skills/scientific-brainstorming
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

Scientific Brainstorming

Purpose and boundaries

Use this skill to create, organize, challenge, and transparently prioritize candidate research directions. Treat every output as a proposal, not a finding. Creativity methods can alter participation and idea yield, but no method universally improves originality, usefulness, or scientific validity. The evidence base and its limits are summarized in references/sources.md.

Keep these activities separate:

designs, analyses, and independent scrutiny; brainstorming cannot validate a hypothesis.

  • Ideation creates questions, mechanisms, alternatives, or study concepts.
  • Evidence assessment checks what reliable literature and data support.
  • Hypothesis validation requires observations, predictions, suitable

the relevant authorized reviewers. A brainstorm is never approval.

  • Ethics, biosafety, dual-use, regulatory, and institutional review require

context. Do not turn research ideas into diagnosis or treatment guidance.

  • Clinical advice requires qualified clinicians and patient-specific

For an observation-led testable hypothesis, hand off to hypothesis-generation. For study architecture, use experimental-design; for sample size, statistical-power; for existing evidence, literature-review; and for analysis, statistical-analysis.

Operating rules

evidence, or decision**. Never blur these categories.

  1. Label claims as idea, assumption, prediction, **located

AI-generated ideas. Face-to-face turn-taking can block production, and examples can anchor later output.

  1. Generate independently before exposing participants to other people's or

Consensus is not truth and vote counts are not effect sizes.

  1. Preserve minority views, negative evidence, uncertainty, and abstentions.

unpublished information.

  1. Record provenance without exposing confidential, personal, controlled, or

reasons, ranges, and disagreement visible.

  1. Define evaluation criteria and directions before scoring. Keep raw ratings,

then deliberately reopen ideation. This reduces early anchoring without mistaking an incomplete search for a research gap.

  1. Search the literature after an initial independent round when practical,

qualitative judgment, uncertainty, feasibility, and ethics gates remain controlling.

  1. Do not automatically select a “winner.” Scores are traceable decision aids;

Reproducible workflow

1. Scope the session

Write one focal question and record:

pathogens, controlled technologies, or environmental release could be implicated.

  • purpose, audience, decision owner, and time horizon;
  • in-scope and out-of-scope topics;
  • constraints that are real, assumed, negotiable, or unknown;
  • current knowledge, unresolved observations, and prohibited outputs;
  • whether human participants, animals, clinical care, sensitive data,

If the request seeks patient-specific care, evasion of oversight, harmful optimization, or operationally enabling dual-use details, stop ideation and route to the appropriate professional or institutional process.

2. Diversify perspectives deliberately

Invite relevant methodological, domain, implementation, statistical, safety, ethics, stakeholder, and lived-experience perspectives. Diversity is not a guarantee of creativity: explain whose perspective is represented, missing, or structurally disadvantaged. Use accessible participation modes and pseudonymous participant IDs where appropriate.

The facilitator should disclose conflicts, avoid offering a preferred answer first, prevent senior members from dominating, and ask leaders to contribute after the independent round.

3. Generate independently

Give everyone the same neutral prompt, constraints, and fixed time window. Participants write ideas privately and in parallel before discussion. For each idea, capture:

disconfirming evidence;

  • a stable ID and one-sentence statement;
  • contributor ID(s) and stage (independent, discussion, or post-check);
  • origin (human, AI-assisted, literature-inspired, mixed, or other);
  • assumptions, predicted observations, uncertainties, and possible

for AI assistance.

  • source identifiers for literature-inspired ideas and tool/purpose disclosure

Do not show example solutions before this round unless examples are necessary; if they are, record them as potential anchors.

4. Share without immediate evaluation

Use round-robin or pooled silent sharing. Clarify wording without advocacy. Permit a private or anonymous channel. Ask each participant what is missing, what contradicts the dominant framing, and which idea became less obvious after hearing the group.

5. Cluster structurally

Group ideas by an explicit relation such as shared outcome, mechanism, population, scale, or method. Keep original IDs and text. Record merges and splits. Similar wording is not proof of semantic equivalence; retain distinct ideas when their assumptions, intervention, population, or predictions differ. See references/facilitation_workflows.md.

6. Define transparent criteria

Before rating, define each criterion, direction, scale anchors, evidence needed, conflicts, and explicit weights. Common dimensions include:

  • potential information gain and discriminating predictions;
  • relevance to the scoped question;
  • originality relative to the checked literature, not merely to the room;
  • feasibility, resources, and reversibility;
  • methodological rigor and vulnerability to bias;
  • ethics, safety, equity, dual-use, and regulatory burden;
  • value if the result is null or contradicts the favored mechanism.

Use ranges or confidence labels where assessors are uncertain. Do not hide vetoes inside an averaged score. See references/idea_evaluation.md.

7. Run adversarial review

Assign a reviewer who did not originate each shortlisted idea. Ask:

could dominate?

  • What observation would make this idea wrong or uninformative?
  • Which alternative explanation fits the same predicted result?
  • What hidden dependency, measurement failure, confounder, or selection effect

attractive technology driving preference?

  • Are authority, anchoring, group loyalty, publication incentives, or an

enable misuse?

  • Could this cause harm, worsen inequity, expose sensitive information, or

Record the response, mitigation, residual uncertainty, and whether the idea was revised—not just pass/fail.

8. Check literature and evidence

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