diff --git a/clean-modular-code/write-pseudocode.md b/clean-modular-code/write-pseudocode.md
index fd5302b..1e987a0 100644
--- a/clean-modular-code/write-pseudocode.md
+++ b/clean-modular-code/write-pseudocode.md
@@ -323,11 +323,13 @@ mean_citations = all_pubs_df["citation_count"].mean()
 
 +++ {"editable": true, "slideshow": {"slide_type": ""}}
 
+:::{todo}
 ## Add Multiple data files to your workflow
 
 Above you begin to think about the steps associated with creating a workflow for a single list of dictionaries.  
 
 Using pseudocode helps you think through your logic clearly, while LLMs can assist by generating Python code based on your structure. This process is especially helpful when working on tasks like processing JOSS CrossRef data, where filtering, extracting, and calculating values are essential steps.
+:::
 
 ```{code-cell} ipython3
 
diff --git a/conf.py b/conf.py
index 3a78a52..d58ae23 100644
--- a/conf.py
+++ b/conf.py
@@ -59,6 +59,7 @@
     "sphinx_favicon",
     "sphinx.ext.todo",
     "sphinx_codeautolink",
+    "sphinx.ext.todo",
 ]
 
 # colon fence for card support in md
diff --git a/index.md b/index.md
index 4efc32a..0d32c3e 100644
--- a/index.md
+++ b/index.md
@@ -84,3 +84,8 @@ Optimize Code <code-workflow-logic/intro>
 
 Share Code <publish-share-code/intro>
 :::
+
+
+:::{todolist}
+
+:::