From agent-almanac
Creates R package vignettes using R Markdown or Quarto. Covers setup, YAML configuration, code chunk options, building, testing, and CRAN requirements.
How this skill is triggered — by the user, by Claude, or both
Slash command
/agent-almanac:write-vignetteThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Create long-form documentation vignettes for R packages.
Create long-form documentation vignettes for R packages.
usethis::use_vignette("getting-started", title = "Getting Started with packagename")
Expected: vignettes/getting-started.Rmd created with YAML frontmatter. knitr and rmarkdown added to DESCRIPTION Suggests field. The vignettes/ directory exists.
On failure: If usethis::use_vignette() fails, verify the working directory is the package root (contains DESCRIPTION). If knitr is not installed, run install.packages("knitr") first. For manual creation, create the vignettes/ directory and file by hand, ensuring the YAML frontmatter includes all three %\Vignette* entries.
---
title: "Getting Started with packagename"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Getting Started with packagename}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
## Introduction
Brief overview of what the package does and who it's for.
## Installation
```r
install.packages("packagename")
library(packagename)
Walk through the primary workflow:
# Load example data
data <- example_data()
# Process
result <- main_function(data, option = "default")
# Inspect
summary(result)
Cover optional or advanced functionality.
Summarize and point to other vignettes or resources.
**Expected:** The vignette Rmd file contains Introduction, Installation, Basic Usage, Advanced Features, and Conclusion sections. Code examples use the package's exported functions and produce visible output.
**On failure:** If examples fail to run, verify the package is installed with `devtools::install()`. Ensure examples use the package name in `library()` calls (not `devtools::load_all()`). For functions requiring external resources, use `eval=FALSE` to show code without execution.
### Step 3: Configure Code Chunks
Use chunk options for different purposes:
```r
# Standard evaluated chunk
{r example-basic}
result <- compute_something(1:10)
result
# Show code but don't run (for illustrative purposes)
{r api-example, eval=FALSE}
connect_to_api(key = "your_key_here")
# Run but hide code (show only output)
{r hidden-setup, echo=FALSE}
library(packagename)
# Set global options
{r setup, include=FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 7,
fig.height = 5
)
Expected: A setup chunk with include=FALSE sets global options (collapse, comment, fig.width, fig.height). Chunks are configured appropriately: eval=FALSE for illustrative code, echo=FALSE for hidden setup, and standard chunks for interactive examples.
On failure: If chunk options are not taking effect, verify the syntax uses {r chunk-name, option=value} format (comma-separated, no quotes around logical values). Check that the setup chunk runs first by placing it at the top of the document.
For vignettes that need network access or optional packages:
{r check-available, include=FALSE}
has_suggested <- requireNamespace("optionalpkg", quietly = TRUE)
{r use-suggested, eval=has_suggested}
optionalpkg::special_function()
For long-running computations, pre-compute and save results:
# Save pre-computed results to vignettes/
saveRDS(expensive_result, "vignettes/precomputed.rds")
# Load in vignette
{r load-precomputed}
result <- readRDS("precomputed.rds")
Expected: External dependencies are handled gracefully: optional packages are conditionally loaded with requireNamespace(), network-dependent code uses eval=FALSE or tryCatch(), and expensive computations use pre-computed .rds files.
On failure: If the vignette fails on CRAN due to unavailable optional packages, wrap those sections with a conditional variable (e.g., eval=has_suggested). For pre-computed results, ensure the .rds file is included in the vignettes/ directory and referenced with a relative path.
# Build single vignette
devtools::build_vignettes()
# Build and check (catches vignette issues)
devtools::check()
Expected: Vignette builds without errors. HTML output is readable.
On failure:
RSTUDIO_PANDOC in .Renvirondevtools::install() firstdevtools::check()
Vignette-related checks: builds correctly, doesn't take too long, no errors.
Expected: devtools::check() passes with no vignette-related errors or warnings. The vignette builds within CRAN time limits (typically under 60 seconds).
On failure: If the vignette causes check failures, common fixes include: adding missing Suggests packages to DESCRIPTION, reducing build time with eval=FALSE on slow chunks, and ensuring VignetteIndexEntry matches the title. Run devtools::build_vignettes() separately to isolate vignette-specific errors.
devtools::build_vignettes()devtools::check() passes with no vignette warningsvignette(package = "pkg")vignette YAML block: All three %\Vignette* lines are requiredeval=FALSE for expensive operationsRSTUDIO_PANDOC environment variable is setlibrary(packagename) not devtools::load_all() in vignetteswrite-roxygen-docs - function-level docs complement vignette tutorialsbuild-pkgdown-site - vignettes appear as articles on pkgdown sitesubmit-to-cran - CRAN has specific vignette requirementscreate-quarto-report - Quarto as an alternative to R Markdown vignettesnpx claudepluginhub pjt222/agent-almanacBuild and deploy a pkgdown documentation site for an R package to GitHub Pages. Covers _pkgdown.yml configuration, theming, article organization, reference index customization, and deployment methods. Use when creating a documentation site for a new or existing package, customizing layout or navigation, fixing 404 errors on a deployed site, or migrating between branch-based and GitHub Actions deployment methods.
Structures complex domain knowledge into self-contained Markdown packages with multiple reading paths, time budgets, reader roles, and verification tracking for research topics.
Provides behavioral guidelines to reduce common LLM coding mistakes, focusing on simplicity, surgical changes, assumption surfacing, and verifiable success criteria.