Bootstrap a local R analysis workspace with standard files, folders, local .claude/skills, and an optional renv layer. Use when starting a new R project or analysis workspace.
Advanced R and R Shiny implementation patterns for reactivity, module testing, data engineering, analyst workbenches, and reusable modeling or reporting helpers. Use for recurring implementation pain points.
Build auditable R data-validation pipelines with pointblank, branching-aware missingness rules, row-level extracts, and testable logic checks. Use for data-quality validators and business or clinical rule screening.
Run end-to-end exploratory and analytical R workflows from raw data to analysis-ready datasets, reproducible pipelines, reports, and model-ready outputs. Use when the user wants the full analysis pipeline scaffolded or executed.
Build advanced R model-helper layers for clinical and statistical workflows, including Bayesian, causal, meta-analytic, SEM, tidymodels, and post-estimation helpers. Use when implementing or hardening reusable modeling code.
Develop and maintain R packages with package-aware scaffolding, testing, checks, changelogs, contributor environments, and release hygiene. Use for R package feature work, fixes, PR prep, and release tasks.
Debug advanced R Shiny apps with a repeatable workflow for reactive loops, stale outputs, dynamic UI, background tasks, maps, tables, plots, module boundaries, and accessibility regressions.
Design and implement production-style R Shiny apps with modules, shared data pipelines, background compute, polished dashboards, maps, tables, figures, and strong accessibility. Use when building or refactoring a Shiny app.