关于
This skill helps R developers create package vignettes using R Markdown or Quarto, covering setup, configuration, building, and CRAN requirements. It's ideal for adding tutorials, documenting multi-function workflows, or creating user guides beyond standard help pages. The skill provides structured guidance for producing long-form documentation that meets official package submission standards.
快速安装
Claude Code
推荐npx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/write-vignette在 Claude Code 中复制并粘贴此命令以安装该技能
技能文档
Write Vignette
Create long-form documentation vignettes for R packages.
When to Use
- Adding a "Getting Started" tutorial for a package
- Documenting complex workflows that span multiple functions
- Creating domain-specific guides (e.g., statistical methodology)
- CRAN submission requires user-facing documentation beyond function help
Inputs
- Required: R package with functions to document
- Required: Vignette title and topic
- Optional: Format (R Markdown or Quarto, default: R Markdown)
- Optional: Whether the vignette needs external data or APIs
Procedure
Step 1: Create Vignette File
usethis::use_vignette("getting-started", title = "Getting Started with packagename")
Got: vignettes/getting-started.Rmd created with YAML frontmatter. knitr and rmarkdown added to DESCRIPTION Suggests field. The vignettes/ directory exists.
If fail: 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.
Step 2: Write Vignette Content
---
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)
Basic Usage
Walk through the primary workflow:
# Load example data
data <- example_data()
# Process
result <- main_function(data, option = "default")
# Inspect
summary(result)
Advanced Features
Cover optional or advanced functionality.
Conclusion
Summarize and point to other vignettes or resources.
**Got:** 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.
**If fail:** 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
)
Got: 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.
If fail: 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.
Step 4: Handle External Dependencies
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")
Got: 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.
If fail: 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.
Step 5: Build and Test Vignette
# Build single vignette
devtools::build_vignettes()
# Build and check (catches vignette issues)
devtools::check()
Got: Vignette builds without errors. HTML output is readable.
If fail:
- Missing pandoc: Set
RSTUDIO_PANDOCin.Renviron - Package not installed: Run
devtools::install()first - Missing Suggests: Install packages listed in DESCRIPTION Suggests
Step 6: Verify in Package Check
devtools::check()
Vignette-related checks: builds correctly, doesn't take too long, no errors.
Got: devtools::check() passes with no vignette-related errors or warnings. The vignette builds within CRAN time limits (typically under 60 seconds).
If fail: 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.
Validation
- Vignette builds without errors via
devtools::build_vignettes() - All code chunks execute correctly
- VignetteIndexEntry matches the title
-
devtools::check()passes with no vignette warnings - Vignette appears in pkgdown site articles (if applicable)
- Build time is reasonable (< 60 seconds for CRAN)
Pitfalls
- VignetteIndexEntry mismatch: The index entry in YAML must match what you want users to see in
vignette(package = "pkg") - Missing
vignetteYAML block: All three%\Vignette*lines are required - Vignette too slow for CRAN: Pre-compute results or use
eval=FALSEfor expensive operations - Pandoc not found: Ensure
RSTUDIO_PANDOCenvironment variable is set - Self-referencing package: Use
library(packagename)notdevtools::load_all()in vignettes
Related Skills
write-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 vignettes
GitHub 仓库
Frequently asked questions
What is the write-vignette skill?
write-vignette is a Claude Skill by pjt222. Skills package instructions and resources that Claude loads on demand, so Claude can perform write-vignette-related tasks without extra prompting.
How do I install write-vignette?
Use the install commands on this page: add write-vignette to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does write-vignette belong to?
write-vignette is in the Meta category, tagged word, ai, testing, automation and design.
Is write-vignette free to use?
Yes. write-vignette is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.
相关推荐技能
Content Collections 是一个 TypeScript 优先的构建工具,可将本地 Markdown/MDX 文件转换为类型安全的数据集合。它专为构建博客、文档站和内容密集型 Vite+React 应用而设计,提供基于 Zod 的自动模式验证。该工具涵盖从 Vite 插件配置、MDX 编译到生产环境部署的完整工作流。
这个Claude Skill为开发者提供完整的Polymarket预测市场开发支持,涵盖API调用、交易执行和市场数据分析。关键特性包括实时WebSocket数据流,可监控实时交易、订单和市场动态。开发者可用它构建预测市场应用、实施交易策略并集成实时市场预测功能。
该Skill帮助开发者创建OpenCode插件,用于接入命令、文件、LSP等25+种事件。它提供了插件结构、事件API规范和JavaScript/TypeScript实现模式,适合需要拦截操作、扩展功能或自定义事件处理的场景。开发者可通过它快速构建响应式模块来增强OpenCode AI助手的能力。
SGLang是一个专为LLM设计的高性能推理框架,特别适用于需要结构化输出的场景。它通过RadixAttention前缀缓存技术,在处理JSON、正则表达式、工具调用等具有重复前缀的复杂工作流时,能实现极速生成。如果你正在构建智能体或多轮对话系统,并追求远超vLLM的推理性能,SGLang是理想选择。
