Expert agents for R data science and biostatistics covering data wrangling, feature engineering, model building, statistical evaluation, clinical trials, genomics, documentation generation, and reproducible reporting using tidyverse and tidymodels. All agents create new code in output folders - never modifying existing user code.
Plugin r-tidy-modelling from marketplace r-tidy-modelling in choxos/TidyRModelling. Commands read from the repo's manifest — 1 month ago.
advanced-adaptive-trialsbayesian-modelingcausal-mediationclinical-trialsdiagnostic-accuracyepidemiology-methodsgenomics-analysishealth-economicsipd-meta-analysismendelian-randomizationmeta-analysismodel-evaluationmodel-tuningnetwork-meta-analysispharmacokineticsr-documentation-patternsreal-world-evidencerecipes-patternsresampling-strategiesroxygen2-pkgdownsurvival-analysistidymodels-review-patternstidymodels-workflow/r-tidy-modelling:r-analysis/r-tidy-modelling:r-clinical-analysis/r-tidy-modelling:r-code-review/r-tidy-modelling:r-doc-generate/r-tidy-modelling:r-model-comparison/r-tidy-modelling:r-project-setup/r-tidy-modelling:r-tutorial-createbiostatisticianExpert biostatistician specializing in clinical trials, survival analysis, epidemiology, genomics, and regulatory-compliant medical statistics. Masters frequentist and Bayesian methods, survival models, mixed effects, meta-analysis, and diagnostic accuracy. Use PROACTIVELY for clinical trial design,
data-wranglerExpert in tidyverse data manipulation specializing in dplyr, tidyr, purrr, and related packages for data transformation, cleaning, and reshaping. Masters complex joins, pivoting, list-columns, and functional programming for data preparation. Use PROACTIVELY for data cleaning, transformation, aggrega
feature-engineerExpert in recipes-based feature engineering for machine learning and statistical modeling. Masters preprocessing transformations, handling missing data, encoding strategies, feature extraction, and domain-specific feature creation. Use PROACTIVELY for data preprocessing, feature creation, handling m
r-code-reviewerExpert R code reviewer specializing in tidyverse style, performance optimization, package development patterns, testing with testthat, TMwR anti-pattern detection, and documentation standards. Conducts comprehensive code audits for quality, maintainability, tidymodels workflow compliance, and best p
r-data-architectStrategic R project architect specializing in enterprise data science architecture, project structure design, workflow orchestration with targets, reproducibility with renv, and technology selection. Masters tidymodels ecosystems, pipeline design, and deployment strategies. Use PROACTIVELY when plan
r-docs-architectTechnical documentation specialist for R projects, creating comprehensive manuals, package documentation with pkgdown, vignettes, README files, and architecture documentation. Masters roxygen2, pkgdown configuration, and technical writing for R codebases. Use PROACTIVELY for generating package docum
r-tutorial-engineerTutorial and educational content creator for R, transforming code into progressive learning experiences with exercises, interactive learnr tutorials, cookbooks, and onboarding guides. Masters instructional design for technical content. Use PROACTIVELY for creating tutorials, learning materials, onbo
reporting-engineerExpert in R reproducible reporting with RMarkdown, Quarto, and Shiny for creating dynamic documents, presentations, dashboards, and interactive applications. Masters parameterized reports, automated pipelines, and multi-format publishing. Use PROACTIVELY for creating reports, documents, presentation
tidymodels-engineerExpert tidymodels practitioner specializing in production-ready machine learning pipelines using parsnip, workflows, tune, and the extended tidymodels ecosystem. Masters model specification, hyperparameter tuning, resampling strategies, and model stacking. Use PROACTIVELY for building ML models, tun
viz-specialistExpert in R data visualization specializing in ggplot2, publication-quality graphics, and interactive visualizations. Masters the grammar of graphics, statistical plots, scientific figures, and domain-specific visualizations for biostatistics and data science. Use PROACTIVELY for creating plots, sta
Generate natively editable PPTX from PDF / DOCX / URL / Markdown — real DrawingML shapes, text boxes, charts, and animations. Note: run `pip install -r requirements.txt` inside the installed plugin directory for the Python post-processing scripts to work.
1 skill
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
26 skills · 4 commands · 3 agents · 1 MCP server
Connect to preclinical research tools and databases (literature search, genomics analysis, target prioritization) to accelerate early-stage life sciences R&D
6 skills · 11 MCP servers
Enterprise / cross-functional Research Operations domain — the managed counterpart to the academic research/ domain. v2.9.0 ships 5 skills: orchestrator (context: fork), clinical-research (protocol synopsis, endpoint selection, sample-size/power for means/proportions/survival, phase-gate feasibility), research-finance (R&D budgeting with F&A split, burn/runway, capitalize-vs-expense routing, portfolio ROI), market-research (TAM/SAM/SOM top-down and bottoms-up, survey sampling with FPC, Kotler segmentation), and product-research (goal-matched study design, method-based saturation, insight synthesis flagging single-source anecdotes). Hard rules: outputs are estimates routed to a named clinical or finance owner, market sizes show method + assumptions, product insights require recurrence across independent participants. 24 stdlib Python tools, 12 reference docs. Distinct from ra-qm-team, finance, research/grants, product-team, and marketing-skill.
6 skills · 7 commands · 2 agents
Nine-skill stack for top-5 economics manuscripts (AER / AER: Insights / AEJ): topic selection, modern causal identification (DiD / IV / RDD / SCM / Bartik), referee-anticipating robustness, Keith-Head-style introductions, AER booktabs tables, AEA Data and Code Availability deposits (openICPSR-ready), submission preflight, and R&R rebuttal letters. Ships Stata / R / Python templates and classic-AER-paper exemplars.
15 skills
Data Science Team — 10 agents: Cast, Feat, Fit, Score, Drift, Vect, Tune, Plot, Clean, Eval
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