Add experimental badge to function that are

Author: cderv

docs/blog/posts/2025-xx-xx-R-package-release-1.5/index.qmd

@@ -119,7 +119,7 @@ These helper functions wrap content in HTML spans with `data-qmd-base64` attribu
 
 ### Extract R Code from Quarto Documents
 
-The new `qmd_to_r_script()` function provides an alternative to `knitr::purl()` that leverages `quarto inspect` for code extraction:
+The new `qmd_to_r_script()` <a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='https://img.shields.io/badge/lifecycle-experimental-orange.svg' alt='Experimental lifecycle'></a> function provides an alternative to `knitr::purl()` that leverages `quarto inspect` for code extraction:
 
 ```r
 # Extract R code from a Quarto document
@@ -129,7 +129,7 @@ qmd_to_r_script("analysis.qmd")  # Creates "analysis.R"
 qmd_to_r_script("analysis.qmd", script = "extracted-code.R")
 ```
 
-This function uses Quarto's static document analysis rather than R evaluation, making it faster and safer for simple code extraction. It preserves cells options as spin-style headers, commenting cells with `eval: false`, and ignoring content having `purl: false`. For documents using advanced knitr features like `child=` chunks or `knitr::read_chunk()`, `knitr::purl()` remains the recommended approach as it handles these through actual document processing.
+This function uses Quarto's static document analysis rather than R evaluation, making it faster and safer for simple code extraction. It preserves cells options, commenting cells with `eval: false`, and ignoring content having `purl: false`. For documents using advanced knitr features like `child=` chunks or `knitr::read_chunk()`, `knitr::purl()` remains the recommended approach as it handles these through actual document processing.
 
 ### Prepare R Scripts for Quarto Rendering
 
@@ -170,8 +170,7 @@ data_file <- project_path("data", "analysis.csv")
 # Explicitly find the project root (searches for _quarto.yml)
 root <- find_project_root()
 ```
-
-`project_path()` intelligently handles different execution contexts:
+`project_path()` <a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='https://img.shields.io/badge/lifecycle-experimental-orange.svg' alt='Experimental lifecycle'></a> intelligently handles different execution contexts:
 - During `quarto render`, it uses `QUARTO_PROJECT_ROOT` or `QUARTO_PROJECT_DIR` environment variables
 - In interactive sessions, it automatically detects the project root by searching for `_quarto.yml`.
 - Falls back to the current working directory with a warning if no project is found.