Building, styling, testing, debugging, or observing a Shiny for R app - library(shiny), shinyApp(), runApp(). Index skill: read this, then open the linked reference for the task. Covers reactivity (reactive/observe/req/isolate/bindEvent); modules; async promises and ExtendedTask; bslib layouts, navigation, cards…
The enforcement arm of the handbook rules (handbook/meta/handbook/): the checks that keep the documentation system whole. Run it when a change touches documentation — handbook pages, free-floating .md files, script headers, generated indexes — and periodically as a scheduled sweep.
Scaffold a new strategy (and optionally environment) model in the plant C++/R package, then wire it through the build. Use when asked to add a new model/strategy, create a strategy from an existing one (e.g. clone FF16), add a variant strategy that reuses an existing environment, or "use the scaffolder". Covers…
Migrate code that uses the plant R package up to plant's latest interface. Use when a downstream product (analysis scripts, a dependent package, a notebook) breaks or needs updating after plant changed — e.g. "update X to the latest plant", "plant's interface changed, fix my code", "migrate to the new runscm/Control…
Profile and benchmark the plant C++/R package — find hotspots, measure whether a change or branch helped or regressed, and compare performance across builds. Use when asked to profile, benchmark, time, or optimize plant; to find what is slow; to compare speed between two branches/commits; or to verify a change did not…
Modern tidyverse patterns, style guide, and migration guidance for R development. Use this skill when writing R code, reviewing tidyverse code, updating legacy R code, or enforcing consistent style. Covers native pipe usage, joinby() syntax, .by grouping, pick/across/reframe, filterout/whenany/whenall…
Generates R implementations for Six Sigma / statistical-process-control tasks — control charts (X-bar R, I-MR, P, NP, C, U), process capability (Cp/Cpk/Pp/Ppk), DPMO and sigma level, Pareto and run charts, histograms, box plots, scatter/regression, correlation, hypothesis tests (1- and 2-sample t, proportion…
Provides base R programming guidance covering data structures, data wrangling, statistical modeling, visualization, and I/O — using only packages included in a standard R installation. Use when the user writes or debugs R scripts, works with vectors, matrices, lists, data frames, or factors, applies the apply family…
Guide for creating new dials parameters for hyperparameter tuning. Use when a developer needs to define custom tuning parameters for models, recipes, or workflows, including quantitative parameters (continuous/integer), qualitative parameters (categorical), parameters with transformations, and data-dependent…
Guide for creating new yardstick metrics. Use when a developer needs to extend yardstick with a custom performance metric, including numeric, class, probability, ordered probability, survival (static, dynamic, integrated, linear predictor), and quantile metrics.
R language data analysis and visualization skill. Use when user asks to (1) run R scripts or code, (2) install/update R packages, (3) perform data analysis with R, (4) create visualizations with ggplot2/plotly, (5) statistical analysis, (6) data manipulation with tidyverse/dplyr/data.table. Triggers on keywords like…
Pre-publish council for R data-science analyses. Five voices review a .R / .Rmd / .qmd file for reproducibility (seed / renv / sessionInfo), fairness (ECOA §701 / GDPR Art. 9 protected classes), statistical rigor (p-hacking / multiple comparisons without p.adjust), data provenance (local vs remote sources), and output…