Agent-almanac build-shiny-module

install
source · Clone the upstream repo
git clone https://github.com/pjt222/agent-almanac
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/pjt222/agent-almanac "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/build-shiny-module" ~/.claude/skills/pjt222-agent-almanac-build-shiny-module-150ee0 && rm -rf "$T"
manifest: skills/build-shiny-module/SKILL.md
source content

Build Shiny Module

Create reusable Shiny UI/server module pairs with proper namespace isolation, reactive communication, and composability.

When to Use

  • Extracting a reusable component from a growing Shiny app
  • Building a UI widget that will be used in multiple places
  • Encapsulating complex reactive logic behind a clean interface
  • Composing larger applications from smaller, testable units

Inputs

  • Required: Module purpose and functionality description
  • Required: Input/output contract (what the module receives and returns)
  • Optional: Whether the module nests other modules (default: no)
  • Optional: Framework context (golem, rhino, or vanilla)

Procedure

Step 1: Define the Module Interface

Before writing code, define what the module accepts and returns:

Module: data_filter
Inputs: reactive dataset, column names to filter on
Outputs: reactive filtered dataset
UI: filter controls (selectInput, sliderInput, dateRangeInput)

Expected: Clear contract specifying reactive inputs, reactive outputs, and UI elements.

On failure: If the interface is unclear, the module is probably too broad. Split it into smaller modules with single responsibilities.

Step 2: Create the Module UI Function

#' Data Filter Module UI
#'
#' @param id Module namespace ID
#' @return A tagList of filter controls
#' @export
dataFilterUI <- function(id) {
  ns <- NS(id)
  tagList(
    selectInput(
      ns("column"),
      "Filter column",
      choices = NULL
    ),
    uiOutput(ns("filter_control")),
    actionButton(ns("apply"), "Apply Filter", class = "btn-primary")
  )
}

Key rules:

  • Function name follows
    <name>UI
    convention
  • First argument is always
    id
  • Create
    ns <- NS(id)
    at the top
  • Wrap every
    inputId
    and
    outputId
    with
    ns()
  • Return a
    tagList()
    to allow flexible placement

Expected: UI function that creates namespaced input/output elements.

On failure: If IDs collide when using the module twice, check that every ID is wrapped with

ns()
. Common miss: IDs inside
renderUI()
or
uiOutput()
— these need
ns()
too.

Step 3: Create the Module Server Function

#' Data Filter Module Server
#'
#' @param id Module namespace ID
#' @param data Reactive expression returning a data frame
#' @param columns Character vector of filterable column names
#' @return Reactive expression returning the filtered data frame
#' @export
dataFilterServer <- function(id, data, columns) {
  moduleServer(id, function(input, output, session) {
    ns <- session$ns

    # Update column choices when data changes
    observeEvent(data(), {
      available <- intersect(columns, names(data()))
      updateSelectInput(session, "column", choices = available)
    })

    # Dynamic filter control based on selected column
    output$filter_control <- renderUI({
      req(input$column)
      col_data <- data()[[input$column]]

      if (is.numeric(col_data)) {
        sliderInput(
          ns("value_range"),
          "Range",
          min = min(col_data, na.rm = TRUE),
          max = max(col_data, na.rm = TRUE),
          value = range(col_data, na.rm = TRUE)
        )
      } else {
        selectInput(
          ns("value_select"),
          "Values",
          choices = unique(col_data),
          multiple = TRUE,
          selected = unique(col_data)
        )
      }
    })

    # Return filtered data as a reactive
    filtered <- eventReactive(input$apply, {
      req(input$column)
      col <- input$column
      df <- data()

      if (is.numeric(df[[col]])) {
        req(input$value_range)
        df[df[[col]] >= input$value_range[1] &
           df[[col]] <= input$value_range[2], ]
      } else {
        req(input$value_select)
        df[df[[col]] %in% input$value_select, ]
      }
    }, ignoreNULL = FALSE)

    return(filtered)
  })
}

Key rules:

  • Function name follows
    <name>Server
    convention
  • First argument is always
    id
  • Additional arguments are reactive expressions or static values
  • Use
    moduleServer(id, function(input, output, session) { ... })
  • Use
    session$ns
    for dynamic UI created inside the server
  • Return reactive values explicitly

Expected: Server function that processes inputs and returns reactive output.

On failure: If reactive values don't update, check that inputs from dynamic UI use

session$ns
(not the outer
ns
). If the module returns NULL, ensure
return()
is the last expression inside
moduleServer()
.

Step 4: Wire the Module into the Parent App

# In app_ui.R or ui
ui <- page_sidebar(
  title = "Analysis App",
  sidebar = sidebar(
    dataFilterUI("filter1")
  ),
  card(
    DT::dataTableOutput("table")
  )
)

# In app_server.R or server
server <- function(input, output, session) {
  # Raw data source
  raw_data <- reactive({ mtcars })

  # Call module — capture its return value
  filtered_data <- dataFilterServer(
    "filter1",
    data = raw_data,
    columns = c("cyl", "mpg", "hp", "wt")
  )

  # Use the module's returned reactive
  output$table <- DT::renderDataTable({
    filtered_data()
  })
}

Expected: Module appears in the UI and its returned reactive flows into downstream outputs.

On failure: If the module UI doesn't render, verify the

id
string matches between UI and server calls. If the returned reactive is NULL, check that the server function actually returns a value.

Step 5: Compose Nested Modules (Optional)

For modules that contain other modules:

analysisUI <- function(id) {
  ns <- NS(id)
  tagList(
    dataFilterUI(ns("filter")),
    plotOutput(ns("plot"))
  )
}

analysisServer <- function(id, data) {
  moduleServer(id, function(input, output, session) {
    # Call inner module with namespaced ID
    filtered <- dataFilterServer("filter", data = data, columns = names(data()))

    output$plot <- renderPlot({
      req(filtered())
      plot(filtered())
    })

    return(filtered)
  })
}

Key rule: In the UI, nest with

ns("inner_id")
. In the server, call with just
"inner_id"
moduleServer
handles the namespace chaining.

Expected: Inner module renders correctly within the outer module's namespace.

On failure: If the inner module's UI doesn't appear, you likely forgot

ns()
around the inner module's ID in the outer UI function. If server communication breaks, check that the inner module ID matches (no
ns()
in the server call).

Step 6: Test the Module in Isolation

# Quick test app for the module
if (interactive()) {
  shiny::shinyApp(
    ui = fluidPage(
      dataFilterUI("test"),
      DT::dataTableOutput("result")
    ),
    server = function(input, output, session) {
      data <- reactive(iris)
      filtered <- dataFilterServer("test", data, names(iris))
      output$result <- DT::renderDataTable(filtered())
    }
  )
}

Expected: Module works correctly in the minimal test app.

On failure: If the module fails in isolation but works in the full app (or vice versa), check for implicit dependencies on global variables or parent session state.

Validation

  • Module UI function accepts
    id
    as first argument and uses
    NS(id)
  • Every input/output ID in the UI is wrapped with
    ns()
  • Module server uses
    moduleServer(id, function(input, output, session) { ... })
  • Dynamic UI in server uses
    session$ns
    for IDs
  • Module can be instantiated multiple times without ID collisions
  • Reactive return values are accessible to the parent app
  • Module works in a minimal standalone test app

Common Pitfalls

  • Forgetting
    ns()
    in
    renderUI()
    : Dynamic UI created inside the server must use
    session$ns
    — the outer
    ns
    is not available inside
    moduleServer()
    .
  • Passing non-reactive data: Module arguments that change over time must be reactive expressions. Pass
    reactive(data)
    not
    data
    .
  • ID mismatch: The
    id
    string in the UI call must exactly match the
    id
    in the server call.
  • Not returning reactives: If the module computes something the parent needs, it must
    return()
    a reactive. Forgetting this is a silent bug.
  • Namespace in nested modules: In UI:
    ns("inner_id")
    . In server: just
    "inner_id"
    . Mixing these up causes namespace double-wrapping or missing prefixes.

Related Skills

  • scaffold-shiny-app
    — set up the app structure before adding modules
  • test-shiny-app
    — test modules with testServer() unit tests
  • design-shiny-ui
    — bslib layout and theming for module UIs
  • optimize-shiny-performance
    — cache and async patterns within modules