diff --git a/R/adjust-equivocal-zone.R b/R/adjust-equivocal-zone.R
index 34c68ce..1375b7a 100644
--- a/R/adjust-equivocal-zone.R
+++ b/R/adjust-equivocal-zone.R
@@ -5,23 +5,34 @@
 #' value is the size of the buffer around the threshold.
 #' @param threshold A numeric value (between zero and one) or [hardhat::tune()].
 #' @examplesIf rlang::is_installed("modeldata")
-#' library(dplyr)
 #' library(modeldata)
 #'
-#' post_obj <-
+#' head(two_class_example)
+#'
+#' # `predicted` gives hard class predictions based on probabilities
+#' two_class_example %>% count(predicted)
+#'
+#' # when probabilities are within (.25, .75), consider them equivocal
+#' tlr <-
 #'   tailor() %>%
 #'   adjust_equivocal_zone(value = 1 / 4)
 #'
+#' tlr
 #'
-#' post_res <- fit(
-#'   post_obj,
+#' # fit by supplying column names. situate in a modeling workflow
+#' # with `workflows::add_tailor()` to avoid having to do so manually
+#' tlr_fit <- fit(
+#'   tlr,
 #'   two_class_example,
 #'   outcome = c(truth),
 #'   estimate = c(predicted),
 #'   probabilities = c(Class1, Class2)
 #' )
 #'
-#' predict(post_res, two_class_example)
+#' tlr_fit
+#'
+#' # adjust hard class predictions
+#' predict(tlr_fit, two_class_example) %>% count(predicted)
 #' @export
 adjust_equivocal_zone <- function(x, value = 0.1, threshold = 1 / 2) {
   check_tailor(x)
diff --git a/R/adjust-numeric-calibration.R b/R/adjust-numeric-calibration.R
index fff0eeb..ea8bd49 100644
--- a/R/adjust-numeric-calibration.R
+++ b/R/adjust-numeric-calibration.R
@@ -6,26 +6,29 @@
 #' package [probably::cal_estimate_linear()],
 #' [probably::cal_estimate_isotonic()], or
 #' [probably::cal_estimate_isotonic_boot()], respectively.
-#' @examplesIf rlang::is_installed("modeldata")
-#' library(modeldata)
-#' library(probably)
+#' @examples
 #' library(tibble)
 #'
 #' # create example data
 #' set.seed(1)
-#' dat <- tibble(y = rnorm(100), y_pred = y/2 + rnorm(100))
+#' d_potato <- tibble(y = rnorm(100), y_pred = y/2 + rnorm(100))
+#' d_test <- tibble(y = rnorm(100), y_pred = y/2 + rnorm(100))
 #'
-#' dat
+#' d_potato
 #'
 #' # specify calibration
-#' reg_tailor <-
+#' tlr <-
 #'   tailor() %>%
 #'   adjust_numeric_calibration(method = "linear")
 #'
-#' # train tailor
-#' reg_tailor_trained <- fit(reg_tailor, dat, outcome = y, estimate = y_pred)
+#' # train tailor on a subset of data. situate in a modeling workflow with
+#' # `workflows::add_tailor()` to avoid having to specify column names manually
+#' tlr_fit <- fit(tlr, d_potato, outcome = y, estimate = y_pred)
 #'
-#' predict(reg_tailor_trained, dat)
+#' # apply to predictions on another subset of data
+#' d_test
+#'
+#' predict(tlr_fit, d_test)
 #' @export
 adjust_numeric_calibration <- function(x, method = NULL) {
   check_tailor(x, calibration_type = "numeric")
diff --git a/R/adjust-numeric-range.R b/R/adjust-numeric-range.R
index e40b022..c79f08e 100644
--- a/R/adjust-numeric-range.R
+++ b/R/adjust-numeric-range.R
@@ -3,6 +3,26 @@
 #' @param x A [tailor()].
 #' @param upper_limit,lower_limit A numeric value, NA (for no truncation) or
 #' [hardhat::tune()].
+#'
+#' @examplesIf FALSE
+# # TODO: unskip -- fn currently requires estimate to be called `.pred` (#22)
+#' library(tibble)
+#'
+#' # create example data
+#' set.seed(1)
+#' d <- tibble(y = rnorm(100), y_pred = y/2 + rnorm(100))
+#' d
+#'
+#' # specify calibration
+#' tlr <-
+#'   tailor() %>%
+#'   adjust_numeric_range(lower_limit = 1)
+#'
+#' # train tailor by passing column names. situate in a modeling workflow with
+#' # `workflows::add_tailor()` to avoid having to specify column names manually
+#' tlr_fit <- fit(tlr, d, outcome = y, estimate = y_pred)
+#'
+#' predict(tlr_fit, d)
 #' @export
 adjust_numeric_range <- function(x, lower_limit = -Inf, upper_limit = Inf) {
   # remaining input checks are done via probably::bound_prediction
diff --git a/R/adjust-predictions-custom.R b/R/adjust-predictions-custom.R
index 3c63699..d304f9c 100644
--- a/R/adjust-predictions-custom.R
+++ b/R/adjust-predictions-custom.R
@@ -5,24 +5,24 @@
 #' the commands.
 #' @param ... Name-value pairs of expressions. See [dplyr::mutate()].
 #' @examplesIf rlang::is_installed("modeldata")
-#' library(dplyr)
 #' library(modeldata)
 #'
-#' post_obj <-
+#' head(two_class_example)
+#'
+#' tlr <-
 #'   tailor() %>%
 #'   adjust_equivocal_zone() %>%
 #'   adjust_predictions_custom(linear_predictor = binomial()$linkfun(Class2))
 #'
-#'
-#' post_res <- fit(
-#'   post_obj,
+#' tlr_fit <- fit(
+#'   tlr,
 #'   two_class_example,
 #'   outcome = c(truth),
 #'   estimate = c(predicted),
 #'   probabilities = c(Class1, Class2)
 #' )
 #'
-#' predict(post_res, two_class_example)
+#' predict(tlr_fit, two_class_example) %>% head()
 #' @export
 adjust_predictions_custom <- function(x, ..., .pkgs = character(0)) {
   check_tailor(x)
diff --git a/R/adjust-probability-calibration.R b/R/adjust-probability-calibration.R
index d4debc8..7d30ced 100644
--- a/R/adjust-probability-calibration.R
+++ b/R/adjust-probability-calibration.R
@@ -5,6 +5,40 @@
 #' `"beta"`, `"isotonic"`, or `"isotonic_boot"`, corresponding to the
 #' function from the \pkg{probably} package [probably::cal_estimate_logistic()],
 #' [probably::cal_estimate_multinomial()], etc., respectively.
+#'
+# TODO: see #36
+#' @examplesIf FALSE
+# @examplesIf rlang::is_installed("modeldata")
+#' library(modeldata)
+#'
+#' # split example data
+#' set.seed(1)
+#' in_rows <- sample(c(TRUE, FALSE), nrow(two_class_example), replace = TRUE)
+#' d_potato <- two_class_example[in_rows, ]
+#' d_test <- two_class_example[!in_rows, ]
+#'
+#' head(d_potato)
+#'
+#' # specify calibration
+#' tlr <-
+#'   tailor() %>%
+#'   adjust_probability_calibration(method = "logistic")
+#'
+#' # train tailor on a subset of data. situate in a modeling workflow with
+#' # `workflows::add_tailor()` to avoid having to specify column names manually
+#' tlr_fit <- fit(
+#'   tlr,
+#'   d_potato,
+#'   outcome = c(truth),
+#'   estimate = c(predicted),
+#'   probabilities = c(Class1, Class2)
+#' )
+#'
+#' # apply to predictions on another subset of data
+#' head(d_test)
+#'
+#' predict(tlr_fit, d_test)
+#'
 #' @export
 adjust_probability_calibration <- function(x, method = NULL) {
   check_tailor(x, calibration_type = "probability")
diff --git a/R/adjust-probability-threshold.R b/R/adjust-probability-threshold.R
index ba5a333..3cba71d 100644
--- a/R/adjust-probability-threshold.R
+++ b/R/adjust-probability-threshold.R
@@ -3,24 +3,28 @@
 #' @param x A [tailor()].
 #' @param threshold A numeric value (between zero and one) or [hardhat::tune()].
 #' @examplesIf rlang::is_installed("modeldata")
-#' library(dplyr)
 #' library(modeldata)
 #'
-#' post_obj <-
-#'   tailor() %>%
-#'   adjust_probability_threshold(threshold = .1)
+#' # `predicted` gives hard class predictions based on probability threshold .5
+#' head(two_class_example)
 #'
-#' two_class_example %>% count(predicted)
+#' # use a threshold of .1 instead:
+#' tlr <-
+#'   tailor() %>%
+#'   adjust_probability_threshold(.1)
 #'
-#' post_res <- fit(
-#'   post_obj,
+#' # fit by supplying column names. situate in a modeling workflow
+#' # with `workflows::add_tailor()` to avoid having to do so manually
+#' tlr_fit <- fit(
+#'   tlr,
 #'   two_class_example,
 #'   outcome = c(truth),
 #'   estimate = c(predicted),
 #'   probabilities = c(Class1, Class2)
 #' )
 #'
-#' predict(post_res, two_class_example) %>% count(predicted)
+#' # adjust hard class predictions
+#' predict(tlr_fit, two_class_example) %>% head()
 #' @export
 adjust_probability_threshold <- function(x, threshold = 0.5) {
   check_tailor(x)
diff --git a/R/tailor.R b/R/tailor.R
index 180e7b8..8932a47 100644
--- a/R/tailor.R
+++ b/R/tailor.R
@@ -28,7 +28,6 @@
 #' classification, these should be given in the order of the factor levels of
 #' the `estimate`.
 #' @examplesIf rlang::is_installed("modeldata")
-#' library(dplyr)
 #' library(modeldata)
 #'
 #' # `predicted` gives hard class predictions based on probabilities
diff --git a/man/adjust_equivocal_zone.Rd b/man/adjust_equivocal_zone.Rd
index 87d66cb..67a8f57 100644
--- a/man/adjust_equivocal_zone.Rd
+++ b/man/adjust_equivocal_zone.Rd
@@ -19,22 +19,33 @@ Apply an equivocal zone to a binary classification model.
 }
 \examples{
 \dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
-library(dplyr)
 library(modeldata)
 
-post_obj <-
+head(two_class_example)
+
+# `predicted` gives hard class predictions based on probabilities
+two_class_example \%>\% count(predicted)
+
+# when probabilities are within (.25, .75), consider them equivocal
+tlr <-
   tailor() \%>\%
   adjust_equivocal_zone(value = 1 / 4)
 
+tlr
 
-post_res <- fit(
-  post_obj,
+# fit by supplying column names. situate in a modeling workflow
+# with `workflows::add_tailor()` to avoid having to do so manually
+tlr_fit <- fit(
+  tlr,
   two_class_example,
   outcome = c(truth),
   estimate = c(predicted),
   probabilities = c(Class1, Class2)
 )
 
-predict(post_res, two_class_example)
+tlr_fit
+
+# adjust hard class predictions
+predict(tlr_fit, two_class_example) \%>\% count(predicted)
 \dontshow{\}) # examplesIf}
 }
diff --git a/man/adjust_numeric_calibration.Rd b/man/adjust_numeric_calibration.Rd
index 9c79b7a..51aa081 100644
--- a/man/adjust_numeric_calibration.Rd
+++ b/man/adjust_numeric_calibration.Rd
@@ -19,25 +19,26 @@ package \code{\link[probably:cal_estimate_linear]{probably::cal_estimate_linear(
 Re-calibrate numeric predictions
 }
 \examples{
-\dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
-library(modeldata)
-library(probably)
 library(tibble)
 
 # create example data
 set.seed(1)
-dat <- tibble(y = rnorm(100), y_pred = y/2 + rnorm(100))
+d_potato <- tibble(y = rnorm(100), y_pred = y/2 + rnorm(100))
+d_test <- tibble(y = rnorm(100), y_pred = y/2 + rnorm(100))
 
-dat
+d_potato
 
 # specify calibration
-reg_tailor <-
+tlr <-
   tailor() \%>\%
   adjust_numeric_calibration(method = "linear")
 
-# train tailor
-reg_tailor_trained <- fit(reg_tailor, dat, outcome = y, estimate = y_pred)
+# train tailor on a subset of data. situate in a modeling workflow with
+# `workflows::add_tailor()` to avoid having to specify column names manually
+tlr_fit <- fit(tlr, d_potato, outcome = y, estimate = y_pred)
 
-predict(reg_tailor_trained, dat)
-\dontshow{\}) # examplesIf}
+# apply to predictions on another subset of data
+d_test
+
+predict(tlr_fit, d_test)
 }
diff --git a/man/adjust_numeric_range.Rd b/man/adjust_numeric_range.Rd
index 451a7d8..2f8d413 100644
--- a/man/adjust_numeric_range.Rd
+++ b/man/adjust_numeric_range.Rd
@@ -15,3 +15,24 @@ adjust_numeric_range(x, lower_limit = -Inf, upper_limit = Inf)
 \description{
 Truncate the range of numeric predictions
 }
+\examples{
+\dontshow{if (FALSE) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+library(tibble)
+
+# create example data
+set.seed(1)
+d <- tibble(y = rnorm(100), y_pred = y/2 + rnorm(100))
+d
+
+# specify calibration
+tlr <-
+  tailor() \%>\%
+  adjust_numeric_range(lower_limit = 1)
+
+# train tailor by passing column names. situate in a modeling workflow with
+# `workflows::add_tailor()` to avoid having to specify column names manually
+tlr_fit <- fit(tlr, d, outcome = y, estimate = y_pred)
+
+predict(tlr_fit, d)
+\dontshow{\}) # examplesIf}
+}
diff --git a/man/adjust_predictions_custom.Rd b/man/adjust_predictions_custom.Rd
index 85cd7a9..38847bd 100644
--- a/man/adjust_predictions_custom.Rd
+++ b/man/adjust_predictions_custom.Rd
@@ -19,23 +19,23 @@ Change or add variables
 }
 \examples{
 \dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
-library(dplyr)
 library(modeldata)
 
-post_obj <-
+head(two_class_example)
+
+tlr <-
   tailor() \%>\%
   adjust_equivocal_zone() \%>\%
   adjust_predictions_custom(linear_predictor = binomial()$linkfun(Class2))
 
-
-post_res <- fit(
-  post_obj,
+tlr_fit <- fit(
+  tlr,
   two_class_example,
   outcome = c(truth),
   estimate = c(predicted),
   probabilities = c(Class1, Class2)
 )
 
-predict(post_res, two_class_example)
+predict(tlr_fit, two_class_example) \%>\% head()
 \dontshow{\}) # examplesIf}
 }
diff --git a/man/adjust_probability_calibration.Rd b/man/adjust_probability_calibration.Rd
index c4120c5..644de96 100644
--- a/man/adjust_probability_calibration.Rd
+++ b/man/adjust_probability_calibration.Rd
@@ -17,3 +17,36 @@ function from the \pkg{probably} package \code{\link[probably:cal_estimate_logis
 \description{
 Re-calibrate classification probability predictions
 }
+\examples{
+\dontshow{if (FALSE) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+library(modeldata)
+
+# split example data
+set.seed(1)
+in_rows <- sample(c(TRUE, FALSE), nrow(two_class_example), replace = TRUE)
+d_potato <- two_class_example[in_rows, ]
+d_test <- two_class_example[!in_rows, ]
+
+head(d_potato)
+
+# specify calibration
+tlr <-
+  tailor() \%>\%
+  adjust_probability_calibration(method = "logistic")
+
+# train tailor on a subset of data. situate in a modeling workflow with
+# `workflows::add_tailor()` to avoid having to specify column names manually
+tlr_fit <- fit(
+  tlr,
+  d_potato,
+  outcome = c(truth),
+  estimate = c(predicted),
+  probabilities = c(Class1, Class2)
+)
+
+# apply to predictions on another subset of data
+head(d_test)
+
+predict(tlr_fit, d_test)
+\dontshow{\}) # examplesIf}
+}
diff --git a/man/adjust_probability_threshold.Rd b/man/adjust_probability_threshold.Rd
index 14128b1..2da6da3 100644
--- a/man/adjust_probability_threshold.Rd
+++ b/man/adjust_probability_threshold.Rd
@@ -16,23 +16,27 @@ Change the event threshold
 }
 \examples{
 \dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
-library(dplyr)
 library(modeldata)
 
-post_obj <-
-  tailor() \%>\%
-  adjust_probability_threshold(threshold = .1)
+# `predicted` gives hard class predictions based on probability threshold .5
+head(two_class_example)
 
-two_class_example \%>\% count(predicted)
+# use a threshold of .1 instead:
+tlr <-
+  tailor() \%>\%
+  adjust_probability_threshold(.1)
 
-post_res <- fit(
-  post_obj,
+# fit by supplying column names. situate in a modeling workflow
+# with `workflows::add_tailor()` to avoid having to do so manually
+tlr_fit <- fit(
+  tlr,
   two_class_example,
   outcome = c(truth),
   estimate = c(predicted),
   probabilities = c(Class1, Class2)
 )
 
-predict(post_res, two_class_example) \%>\% count(predicted)
+# adjust hard class predictions
+predict(tlr_fit, two_class_example) \%>\% head()
 \dontshow{\}) # examplesIf}
 }
diff --git a/man/tailor.Rd b/man/tailor.Rd
index fb8b01f..0f1158d 100644
--- a/man/tailor.Rd
+++ b/man/tailor.Rd
@@ -40,7 +40,6 @@ of use, situate tailors in model workflows with \code{\link[workflows:add_tailor
 }
 \examples{
 \dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
-library(dplyr)
 library(modeldata)
 
 # `predicted` gives hard class predictions based on probabilities