# 113 Interactive 660eb858

> Interactive Mermaid visualization showing adding taxonomy to csv workflow

- Skill: `tools-only/113-interactive-660eb858` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/113-interactive-660eb858`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/113-interactive-660eb858/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/tools-only/113-interactive-660eb858

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# Adding Taxonomy to CSV Workflow

<iframe src="main.html" width="100%" height="1200px"></iframe>

**Copy this iframe to your website:**

```html
<iframe src="https://dmccreary.github.io/claude-skills/sims/adding-taxonomy-workflow/main.html" width="100%" height="600px"></iframe>
```

[Run Adding Taxonomy to CSV Workflow in Fullscreen](main.html){ .md-button .md-button--primary }


This interactive Mermaid diagram shows the complete workflow for adding taxonomy categorization to a learning graph CSV file.

## Interactive Diagram

## Process Overview

This workflow demonstrates how to add taxonomy categorization to an existing learning graph CSV file. The process supports both automated (script-based) and manual categorization approaches.

### Key Steps

1. **Identify Natural Categories** - Review concept labels and group by topic, domain, or complexity level
2. **Design Taxonomy Abbreviations** - Create 3-5 letter codes (FOUND, BASIC, ARCH, etc.)
3. **Choose Categorization Method** - Select between automated (add-taxonomy.py) or manual assignment
4. **Review Assignments** - Check that categorization makes logical sense
5. **Validate Distribution** - Run taxonomy-distribution.py to ensure balance
6. **Adjust if Needed** - Refine categories until distribution is balanced (no category > 30%)

### Decision Points

**Automated vs Manual:** The add-taxonomy.py script uses keyword matching for initial suggestions, best for large graphs (150+ concepts). Manual assignment gives more control, recommended for smaller graphs or specialized domains.

**Distribution Check:** A balanced distribution ensures no single taxonomy dominates. Target: no category exceeding 30% of total concepts.