From ddd240d8658f5ee2f8125632b971d6a471172462 Mon Sep 17 00:00:00 2001
From: Ethan J Lewis <ethan@ejlewis.com>
Date: Sat, 7 Feb 2026 12:30:28 -0600
Subject: [PATCH] add ability to populate slug field

---
 .tag-notes.py.swp | Bin 16384 -> 0 bytes
 README.md         |  12 ++++++++++--
 tag-notes.py      |  29 +++++++++++++++++++++++++----
 3 files changed, 35 insertions(+), 6 deletions(-)
 delete mode 100644 .tag-notes.py.swp

diff --git a/.tag-notes.py.swp b/.tag-notes.py.swp
deleted file mode 100644
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diff --git a/README.md b/README.md
index f067637..b710797 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,19 @@
 # Note Tagging & SEO Automation
 
-Automatically tag your Obsidian notes and add SEO metadata using a local LLM.
+Automatically tag your Obsidian notes, add slugs, and add SEO metadata using a local LLM.
 
-The script requires 4 frontmatter fields named as such:
+The script requires 5 frontmatter fields named as such:
 - tags
 - seo-title
 - seo-description
 - seo-keywords
+- slug
+
+**Note:** The script sends only the first 20,000 characters of the note to the LLM. This should be more than sufficient for most notes, however if you have extremely long notes then you can increase this amount by editing the following line in `tag-notes.py`:
+
+```py
+llm_response = call_llm(None, body[:20000], taxonomy)  # Limit content to first 20000 chars
+```  
 
 ## Setup
 
@@ -41,6 +48,7 @@ The script will:
 - ✓ Add SEO title (clean, non-clickbaity)
 - ✓ Add SEO description (150-160 chars, factual)
 - ✓ Add SEO keywords (generous, 10-15 keywords)
+- ✓ Add slug based off of filename
 - ✓ **Only updates empty fields** - preserves existing values
 - ✓ Updates files directly (no confirmation needed)
 - ✓ **Only touches**: `tags`, `seo-title`, `seo-description`, `seo-keywords`
diff --git a/tag-notes.py b/tag-notes.py
index 996503f..e8f5534 100755
--- a/tag-notes.py
+++ b/tag-notes.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
 Note Tagging and SEO Metadata Script
-Processes markdown notes using a local LLM to add tags and SEO metadata
+Processes markdown notes using a local LLM to add tags, slugs, and SEO metadata
 """
 
 import os
@@ -129,14 +129,36 @@ def process_note(file_path, taxonomy):
         existing_tags = []
     
     needs_tags = not existing_tags
+    needs_slug = not frontmatter.get('slug')
     needs_seo_title = not frontmatter.get('seo-title')
     needs_seo_desc = not frontmatter.get('seo-description')
     needs_seo_keywords = not frontmatter.get('seo-keywords')
     
-    if not (needs_tags or needs_seo_title or needs_seo_desc or needs_seo_keywords):
+    if not (needs_tags or needs_slug or needs_seo_title or needs_seo_desc or needs_seo_keywords):
         print(f"  ✓ All fields already populated, skipping")
         return
     
+    updated = False
+    
+    # Generate slug from filename if needed
+    if needs_slug:
+        # Get filename without extension
+        filename = Path(file_path).stem
+        # Convert to lowercase and replace spaces with hyphens
+        slug = filename.lower().replace(' ', '-')
+        frontmatter['slug'] = slug
+        print(f"  + Added slug: {slug}")
+        updated = True
+    
+    # Only call LLM if we need tags or SEO fields
+    if not (needs_tags or needs_seo_title or needs_seo_desc or needs_seo_keywords):
+        # Only needed slug, we're done
+        new_content = reconstruct_markdown(frontmatter, body)
+        with open(file_path, 'w', encoding='utf-8') as f:
+            f.write(new_content)
+        print(f"  ✓ Updated successfully")
+        return
+    
     # Call LLM
     llm_response = call_llm(None, body[:20000], taxonomy)  # Limit content to first 20000 chars
     
@@ -145,7 +167,6 @@ def process_note(file_path, taxonomy):
         return
     
     # Update frontmatter with new values (only if empty)
-    updated = False
     
     if needs_tags:
         # Combine taxonomy tags and new suggestions
@@ -232,4 +253,4 @@ def main():
     print("\n✓ Processing complete!")
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()