Agent skill · research science · k-dense-ai
scientific-brainstorming
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.
Why this skill is useful
Provides structured frameworks for scientific ideation and evaluation that the AI wouldn't reliably generate on its own.
What it needs
About 7k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
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: Ideation creates questions, mechanisms, alternatives, or study concepts. Evidence assessment checks what reliable literature and data support. Hypothesis validation requires observations, predictions, suitable designs, analyses, and independent scrutiny; brainstorming cannot validate a hypothesis. Ethics, biosafety, dual-use, regulatory, and institutional review require the relevant authorized reviewers. A brainstorm is never approval. Clinical advice requires qualified clinicians and patient-specific context. Do not turn research ideas into diagnosis or treatment guidance. 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 1. Label claims as idea, assumption, prediction, located evidence, or decision. Never blur these categories. 2. Generate independently before exposing participants to other people's or AI-generated ideas. Face-to-face turn-taking can block production, and examples can anchor later output. 3. Preserve minority views, negative evidence, uncertainty, and abstentions. Consensus is not truth and vote counts are not effect sizes. 4. Record provenance without exposing confidential, personal, controlled, or unpublished information. 5. Define evaluation criteria and directions before scoring. Keep raw ratings, reasons, ranges, and disagreement visible. 6. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/scientific-brainstorming