Cluster keywords by semantic similarity and intent. Use when: organizing keyword research; creating content pillars; mapping keywords to pages; identifying content gaps; grouping search intent
Scanned 5/27/2026
Install via CLI
openskills install guia-matthieu/clawfu-skills---
name: keyword-clusterer
description: "Cluster keywords by semantic similarity and intent. Use when: organizing keyword research; creating content pillars; mapping keywords to pages; identifying content gaps; grouping search intent"
license: MIT
metadata:
author: ClawFu
version: 1.0.0
mcp-server: "@clawfu/mcp-skills"
---
# Keyword Clusterer
> Group keywords by semantic similarity using embeddings - turn a keyword list into an organized content strategy.
## When to Use This Skill
- **Content planning** - Group keywords into topic clusters
- **Site structure** - Map keywords to pages
- **Intent analysis** - Categorize by search intent
- **Gap analysis** - Find missing keyword themes
- **PPC organization** - Group keywords for ad groups
## What Claude Does vs What You Decide
| Claude Does | You Decide |
|-------------|------------|
| Structures analysis frameworks | Strategic priorities |
| Synthesizes market data | Competitive positioning |
| Identifies opportunities | Resource allocation |
| Creates strategic options | Final strategy selection |
| Suggests implementation approaches | Execution decisions |
## Dependencies
```bash
pip install scikit-learn sentence-transformers pandas click
# For simpler usage without ML:
pip install click pandas
```
## Commands
### Cluster Keywords
```bash
python scripts/main.py cluster keywords.csv --n-clusters 10
python scripts/main.py cluster keywords.csv --column keyword --n-clusters 15
```
### Find Similar
```bash
python scripts/main.py similar "content marketing" --count 20
```
### Analyze Intent
```bash
python scripts/main.py intent keywords.csv --column keyword
```
## Examples
### Example 1: Cluster Keyword Research
```bash
# Input: keywords.csv with 500 keywords
python scripts/main.py cluster keywords.csv --n-clusters 12 --output clustered.csv
# Output:
# Cluster 1 (45 keywords): "content marketing"
# - content marketing strategy
# - content marketing tips
# - how to do content marketing
#
# Cluster 2 (38 keywords): "email marketing"
# - email marketing tools
# - best email marketing software
# - email campaign tips
# ...
```
### Example 2: Categorize by Intent
```bash
python scripts/main.py intent keywords.csv --column keyword
# Output:
# Intent Analysis
# ──────────────────────
# Informational: 234 (47%)
# - how to, what is, guide, tips
# Commercial: 156 (31%)
# - best, top, review, compare
# Transactional: 78 (16%)
# - buy, price, discount, order
# Navigational: 32 (6%)
# - login, contact, brand names
```
## Search Intent Categories
| Intent | Signals | Content Type |
|--------|---------|--------------|
| **Informational** | how, what, why, guide | Blog posts, guides |
| **Commercial** | best, top, review, vs | Comparisons, reviews |
| **Transactional** | buy, price, discount | Product pages |
| **Navigational** | [brand], login, contact | Landing pages |
## Clustering Methods
| Method | Best For | Speed |
|--------|----------|-------|
| **semantic** | Meaning-based grouping | Slower |
| **lexical** | Word overlap grouping | Faster |
| **intent** | Search intent categories | Fast |
## Skill Boundaries
### What This Skill Does Well
- Structuring strategic analysis
- Identifying market opportunities
- Creating strategic frameworks
- Synthesizing competitive data
### What This Skill Cannot Do
- Replace market research
- Guarantee strategic success
- Know proprietary competitor info
- Make executive decisions
## Related Skills
- [content-repurposer](../../automation/content-repurposer/) - Create content for clusters
- [lighthouse-audit](../lighthouse-audit/) - Optimize cluster pages
## Skill Metadata
- **Mode**: centaur
```yaml
category: seo-tools
subcategory: keyword-research
dependencies: [scikit-learn, sentence-transformers, pandas]
difficulty: intermediate
time_saved: 5+ hours/week
```
No comments yet. Be the first to comment!