R-Skills

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.

Coordinate the R skill family by routing new topics to the right r-* skills, researching current primary sources, and updating only the skills that need new doctrine. Use when expanding or propagating R skill guidance.

Guide advanced R statistical work with estimand-first method selection, diagnostics, effect sizes, Bayesian, causal, SEM, bootstrap, survival, mixed-effects, count, and missing-data workflows.

Build publication-ready R tables, figures, and reporting artifacts with gtsummary, flextable, gt, officer, Quarto, and domain-specific reporting patterns. Use for polished table, figure, and report outputs.

Bootstrap a local R analysis workspace with standard files, folders, local .codex/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.

Coordinate the R skill family by routing new topics to the right r-* skills, researching current primary sources, and updating only the skills that need new doctrine. Use when expanding or propagating R skill guidance.

Guide advanced R statistical work with estimand-first method selection, diagnostics, effect sizes, Bayesian, causal, SEM, bootstrap, survival, mixed-effects, count, and missing-data workflows.

Build publication-ready R tables, figures, and reporting artifacts with gtsummary, flextable, gt, officer, Quarto, and domain-specific reporting patterns. Use for polished table, figure, and report outputs.