gh-zorrocheng-mc-obsidian-vault-manager-plugin/commands/bulk-auto-tag.md

description, argument-hint, allowed-tools

description	argument-hint	allowed-tools
Bulk AI-powered tagging for existing notes to enable Bases filtering	folder-path or file-pattern	Read(*) Edit(*) Glob(*) Bash(*)

Context

• Today's Date: !date "+%Y-%m-%d"
• Target: $ARGUMENTS (defaults to all .md files if not specified)

Tag Taxonomy Reference

Use the same taxonomy as /capture:

Content Types: idea, video, article, study-guide, repository, reference, project Topics: AI, Claude, Gemini, product, marketing, projects, workflow, architecture, design, UI-UX, coding, productivity, knowledge-management, development, learning, research, writing, tools, business, automation, data-science, web-development, personal-growth, finance Status: inbox, processing, evergreen, published, archived, needs-review Metadata: high-priority, quick-read, deep-dive, technical, conceptual, actionable, tutorial, inspiration

Your Task

Step 1: Discover Files to Tag

# If user provided pattern:
find . -name "$ARGUMENTS" -type f

# If no arguments (tag everything):
find . -name "*.md" -type f -not -path "./.obsidian/*" -not -path "./.claude/*"

Step 2: Process Each File

For each discovered file:

1. Read the file content

2. Analyze existing frontmatter:

   • Check if tags: field exists
   • Check if tags are already comprehensive (5+ tags from taxonomy)

3. Skip if already well-tagged (has 5+ taxonomy-compliant tags)

4. Analyze content to determine:

   • Content type (from filename, existing tags, content)
   • Main topics (2-4 from content analysis)
   • Status (infer from content or default to evergreen for old notes)
   • Metadata characteristics

5. Generate enhanced tag array:

   tags: [{content-type}, {topic1}, {topic2}, {topic3}, {status}, {metadata}]
   

6. Update frontmatter while preserving existing data:

   ---
   title: "{existing or generated}"
   tags: [{enhanced-tag-array}]
   date: "{existing or file creation date}"
   type: "{content-type}"
   status: "{status}"
   # preserve any other existing fields
   ---
   

Step 3: Report Progress

After processing each batch of 5-10 files, report:

✅ Tagged 10 files:
   - 3 ideas tagged with [idea, productivity, ...]
   - 2 videos tagged with [video, AI, learning, ...]
   - 5 articles tagged with [article, development, ...]

📊 Progress: 10/47 files processed
🏷️  Total tags added: 73 tags

Step 4: Summary Report

After all files processed:

# Bulk Tagging Report

## Summary
- **Files processed:** 47
- **Files updated:** 43
- **Files skipped:** 4 (already well-tagged)
- **Total tags added:** 312
- **Average tags per note:** 7.3

## Tag Distribution

### By Content Type
- idea: 15 notes
- video: 8 notes
- article: 12 notes
- study-guide: 6 notes
- repository: 2 notes

### By Topic
- AI: 23 notes
- productivity: 18 notes
- knowledge-management: 15 notes
- development: 12 notes
- learning: 10 notes

### By Status
- inbox: 12 notes
- evergreen: 28 notes
- published: 7 notes

## Bases Filtering Suggestions

You can now create Bases views like:
1. **AI Learning Pipeline**: `type = video AND topic = AI AND status = inbox`
2. **Quick Wins**: `metadata = quick-read AND priority = high-priority`
3. **Technical Deep Dives**: `metadata = technical AND metadata = deep-dive`
4. **Actionable Items**: `metadata = actionable AND status != archived`

## Next Steps
1. Review auto-tagged notes in Obsidian
2. Create Bases views using these tags
3. Refine tags manually if needed
4. Run `/bulk-auto-tag` periodically for new notes

Important Rules

1. Preserve existing data: Never delete user-added tags or properties
2. Merge intelligently: Combine AI tags with existing tags (deduplicate)
3. Be conservative: If uncertain about content type, default to reference
4. Handle errors gracefully: Skip files with invalid frontmatter, report errors
5. Respect user intent: If a file has explicit tags, enhance rather than replace

Example Transformations

Before (minimal tags):

---
tags: [idea]
date: 2025-09-15
---

# AI-powered tagging
Use Claude to auto-tag notes for better organization

After (enhanced tags):

---
title: "AI-powered tagging"
tags: [idea, AI, knowledge-management, tools, evergreen, actionable, technical]
date: 2025-09-15
type: idea
status: evergreen
priority: medium
---

# AI-powered tagging
Use Claude to auto-tag notes for better organization

Performance Considerations

• Process files in batches of 10
• Show progress every batch
• Allow user to cancel with Ctrl+C
• Estimate time for large vaults: ~2-3 seconds per file

Safety Features

1. Dry run mode (optional): Show what would be changed without modifying files
2. Backup reminder: Remind user to commit to git before bulk operations
3. Undo support: Provide git commands to rollback if needed