--- title: "scientific-research-workflows" description: "Nine local-first scientific research Skills for ideation, hypotheses, study design, statistics, uncertainty, writing, peer review, and scholarly evaluation." canonical: https://agentpluginsdirectory.com/plugins/scientific-research-workflows last-updated: 2026-10-21 --- # scientific-research-workflows Nine local-first scientific research Skills for ideation, hypotheses, study design, statistics, uncertainty, writing, peer review, and scholarly evaluation. - Slug: scientific-research-workflows - Publisher: K-Dense Inc.; MiniMax Code packaging by hetaoBackend - Repository: https://github.com/hetaoBackend/MiniMax-Code-Plugins - Manifest: plugins/hetaoBackend/scientific-research-workflows/plugin.json - Version: 1.0.0 - License: MIT - Category (editorial): research - Skills: 9 (experimental-design, hypothesis-generation, peer-review, scholar-evaluation, scientific-brainstorming, scientific-writing, statistical-analysis, statistical-power, uncertainty-and-units) - MCP servers: 0 - Stars: 11 - Repository created: 2026-08-14 - Repository last pushed: 2026-09-07 - Publisher type: User - Listing: https://agentpluginsdirectory.com/plugins/scientific-research-workflows - Schema: https://agent-plugins.org/schemas/1.0.0/plugin.schema.json ## What scientific-research-workflows does, in the publisher's words This MiniMax Code Plugin packages nine complementary, local-first Skills from K-Dense Scientific Agent Skills. It helps researchers move from an early idea to a defensible study plan, analysis, manuscript, and review without installing the upstream repository's unrelated, network-dependent, or separately-licensed Skills. The Skills, references, assets, and scripts are sourced from upstream commit 1dd0fccf46fc3c9855c4a0c313a0c57fe4319883. To satisfy this repository's hosted-package validator, the scientific-writing templates use the [[DRAFT:...]] label for unfinished fields; its local linter and scaffold helper were updated consistently, with no change to the marker's meaning. The bundle deliberately contains only Skills that declare the MIT license. The upstream repository contains other Skills with different terms; those are not included here. Expected result: MiniMax Code separates hypotheses from established evidence, identifies the experimental unit and likely batch/site confounders, proposes a reproducible blocked design, asks for the effect-size inputs needed for power, and creates an analysis plan with explicit uncertainty and reporting boundaries. From the project README, punctuation lightly normalized. Full text: https://raw.githubusercontent.com/hetaoBackend/MiniMax-Code-Plugins/HEAD/plugins/hetaoBackend/scientific-research-workflows/README.md ## Skills - experimental-design: Design experiments and studies BEFORE data is collected, choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratifi… - hypothesis-generation: Formulate evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Use when turning observations or preliminary findings into transparent, testable research pl… - peer-review: Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments. Use for authorized review of scientific manuscripts, protocols, preprints, or research proposals; reporting-guideline selection; claim, evidence checks; methods, statistics, reproducibility, ethics, figu… - scholar-evaluation: Provide qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions. - 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, stu… - scientific-writing: Draft, revise, and audit scientific manuscripts or reports with explicit evidence provenance, reporting-guideline coverage, authorship accountability, confidentiality controls, and local consistency checks. Use for manuscript sections, references, declarations, tables, figures, or submission prepar… - statistical-analysis: Guided statistical analysis for research data: test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions,… - statistical-power: Sample-size and statistical power calculations for planning studies. Use whenever someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-re… - uncertainty-and-units: Track physical units and propagate measurement uncertainty in scientific calculations using pint and uncertainties. Use for unit conversion and dimensional checking, GUM uncertainty budgets, Type A and Type B evaluation, coverage factors and expanded uncertainty, Monte Carlo propagation, significan… Descriptions come from the frontmatter of each SKILL.md, punctuation lightly normalized.