Obsidian Auto Tagger

Obsidian Auto Tagger

Add useful tags to untagged Obsidian notes while preserving the vault's existing tag vocabulary.

Use This Skill When

• The user points at an Obsidian vault, folder, or note and asks to add missing tags.
• Notes lack frontmatter, lack a tags field, have an empty tags field, or have no inline tags.
• Existing vault tags should be reused before creating new tags.
• Tags should be written as Obsidian-compatible frontmatter tags entries.

Do Not Use

• The user wants to redesign the entire taxonomy from scratch.
• Notes are generated, cache, plugin, template, attachment, or local skill files.
• Existing tags are already maintained and the user has not requested optimization.
• The note topic is too ambiguous to tag confidently.

Workflow

1. Determine scope. For a single note, read it directly and still inspect nearby vault tags when possible. For a folder or vault, run the bundled scanner first. Run commands from this skill directory or replace scripts/... with the script's absolute path. The script uses Python 3 standard library only.

   python3 scripts/vault_tag_inventory.py scan /path/to/vault --format json
   

2. Inventory the existing taxonomy before assigning tags. Prefer high-frequency, semantically matching tags already present in the vault. Use references/tag-selection.md.

3. Read candidates and recommend tags in small batches. For each untagged note, read the title, frontmatter, headings, and meaningful body text. Choose 1-3 tags from the existing list when possible; use 4 only for notes with distinct retrieval paths. Mark any proposed new tag as new in the recommendation.

4. Present a reviewable suggestion list before editing.

   | File | Suggested Tags | Confidence | Reason |
   | --- | --- | --- | --- |
   | Notes/example.md | ai, research | high | Matches existing tags and note topic |
   

5. Dry-run writes first. Put approved assignments in JSON and validate the exact files that would change:

   python3 scripts/vault_tag_inventory.py apply /path/to/vault assignments.json
   

6. Apply only after the dry run is clean.

   python3 scripts/vault_tag_inventory.py apply /path/to/vault assignments.json --write
   

7. Rescan and summarize. Confirm fewer untagged notes remain and report reused tags, new tags, skipped notes, and any ambiguous cases. Use references/batch-mode.md.

Assignment Format

{
  "assignments": [
    {"path": "Notes/example.md", "tags": ["ai", "research"]}
  ]
}

Paths are relative to the vault root. The apply command is dry-run by default and skips files that already have frontmatter or inline tags unless --force is provided.

Ground Rules

• Preserve existing frontmatter and note content.
• Prefer existing tags over new tags.
• Never remove existing user-authored tags.
• Use concise, reusable tags rather than one-off sentence-like tags.
• Do not infer sensitive or private attributes from personal notes.
• Skip low-confidence notes instead of inventing misleading tags.

Reference Files

• references/tag-selection.md: tag reuse, new-tag criteria, and note-reading heuristics.
• references/batch-mode.md: vault-scale sequencing, dry-run checks, and final reporting.