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Lint and Debug Workflow

User goal

Linting helps instructors detect common authoring problems before conversion. This is useful in teaching teams where several people maintain source lessons.

Minimal example

library(tutorizeR)

lint <- lint_source("lesson.qmd", strict = FALSE)
print(lint)

Strict conversion

library(tutorizeR)

report <- tutorize(
  input = "lesson.qmd",
  output_dir = tempdir(),
  assessment = "both",
  lint_strict = TRUE,
  overwrite = TRUE
)

Realistic installed example

library(tutorizeR)

example_dir <- system.file("examples", "example_course_module", package = "tutorizeR")
source_file <- file.path(example_dir, "lesson-source.qmd")
question_bank <- load_question_bank(file.path(example_dir, "question-bank"))

lint <- lint_source(
  input = source_file,
  question_bank = question_bank,
  strict = FALSE
)

print(lint)

Conversion reports

write_tutorize_report(
  report,
  file.path(tempdir(), "conversion-report.json"),
  format = "json"
)

Reports are useful in continuous integration and course release workflows because they record the generated output path, number of exercises, MCQs, warnings, and lint summary.

Limits

Linting can detect structural issues, but it cannot judge whether a lesson is pedagogically effective. Generated reports should support instructor review, not replace it.

Reproducibility checklist

stopifnot(inherits(lint, "tutorize_lint_report"))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.