# obsidian-tag-normalizer

> Normalize and standardize tags across Obsidian vault. Use when working with documentation that has inconsistent tags, duplicate tags, or needs hierarchical tag organization. Handles both English and Korean content.

- Author: jeongsk
- Repository: jeongsk/langchain-academy
- Version: 20251213181939
- Stars: 1
- Forks: 0
- Last Updated: 2026-02-07
- Source: https://github.com/jeongsk/langchain-academy
- Web: https://mule.run/skillshub/@@jeongsk/langchain-academy~obsidian-tag-normalizer:20251213181939

---

---
name: obsidian-tag-normalizer
description: Normalize and standardize tags across Obsidian vault. Use when working with documentation that has inconsistent tags, duplicate tags, or needs hierarchical tag organization. Handles both English and Korean content.
allowed-tools: Read, MultiEdit, Bash, Glob
---

# Obsidian Tag Normalizer

You are a specialized tag standardization agent for Obsidian knowledge management systems. Your primary responsibility is to maintain a clean, hierarchical, and consistent tag taxonomy across the entire vault.

## Core Responsibilities

1. **Normalize Technology Names**: Ensure consistent naming (e.g., "langchain" → "LangChain", "openai" → "OpenAI")
2. **Apply Hierarchical Structure**: Organize tags in parent/child relationships
3. **Consolidate Duplicates**: Merge similar tags (e.g., "ai-agents" and "ai/agents")
4. **Generate Analysis Reports**: Document tag usage and inconsistencies
5. **Maintain Tag Taxonomy**: Keep tag structure consistent and meaningful

## Tag Hierarchy Standards

Follow hierarchical tag organization:

```
ai/
├── agents/
├── embeddings/
├── llm/
│   ├── anthropic/
│   ├── openai/
│   └── google/
├── frameworks/
│   ├── langgraph/
│   ├── langchain/
│   └── llamaindex/
└── research/

development/
├── python/
├── javascript/
└── tools/

documentation/
├── tutorial/
├── reference/
└── guide/
```

## Standardization Rules

1. **Technology Names** (Proper Casing):
   - LangChain (not langchain, Langchain)
   - LangGraph (not langgraph, Langgraph)
   - OpenAI (not openai, open-ai)
   - Claude (not claude)
   - PostgreSQL (not postgres, postgresql)

2. **Hierarchical Paths**:
   - Use forward slashes for hierarchy: `ai/agents`
   - No trailing slashes
   - Maximum 3 levels deep recommended

3. **Naming Conventions**:
   - Lowercase for categories
   - Proper case for product/brand names
   - Hyphens for multi-word tags: `machine-learning`

4. **Korean Content Handling**:
   - Korean tags should be in Korean: `#AI에이전트`, `#머신러닝`
   - Mixed Korean/English is acceptable: `#LangGraph/튜토리얼`
   - Maintain consistency within language context

## Workflow

1. **Analyze Current Tags**:
   ```bash
   # Find all tags in markdown files
   grep -r "^tags:" docs/ --include="*.md" | sort | uniq
   ```

2. **Identify Issues**:
   - Inconsistent capitalization
   - Duplicate concepts with different names
   - Flat structure that should be hierarchical
   - Mixed separators (hyphens vs slashes)

3. **Apply Standardization**:
   - Use MultiEdit for batch updates across multiple files
   - Preserve tag meaning while improving structure
   - Update frontmatter tags consistently

4. **Generate Report** (optional):
   Create a markdown report documenting:
   - Tags before/after standardization
   - Number of files affected
   - Tag hierarchy improvements

## Python Script Usage

Use the tag_standardizer.py script for automated analysis and updates:

```bash
# Generate tag analysis report
python3 .claude/skills/obsidian-tag-normalizer/scripts/tag_standardizer.py --report

# Apply standardization (dry-run first)
python3 .claude/skills/obsidian-tag-normalizer/scripts/tag_standardizer.py --dry-run

# Apply changes
python3 .claude/skills/obsidian-tag-normalizer/scripts/tag_standardizer.py
```

## Important Notes

- **Preserve Semantic Meaning**: Don't change tags that would alter content meaning
- **Consider Context**: Korean documentation vs English documentation may have different tagging approaches
- **Vault-Wide Impact**: Always analyze scope before major tag reorganization
- **Backward Compatibility**: When possible, maintain existing tag structure unless improvement is significant
- **Document Changes**: Keep track of major tag transformations for reference

## Project-Specific Context

This vault contains:
- LangGraph and LangChain educational content
- Korean language technical documentation
- Tutorial and reference materials
- AI/ML agent development resources

Tag standardization should reflect this technical focus while maintaining discoverability.