ad-spend-optimizer
Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use…
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
Claude Code
$ npx -y skills add guia-matthieu/clawfu-skills --skill keyword-clusterer --agent claude-code
Codex
$ npx skills add guia-matthieu/clawfu-skills --skill keyword-clusterer -a codex -y
Cursor
$ npx skills add guia-matthieu/clawfu-skills --skill keyword-clusterer -a cursor -y
Gemini CLI
$ npx skills add guia-matthieu/clawfu-skills --skill keyword-clusterer -a gemini-cli -y
GitHub Copilot
$ npx skills add guia-matthieu/clawfu-skills --skill keyword-clusterer -a github-copilot -y
OpenCode
$ npx skills add guia-matthieu/clawfu-skills --skill keyword-clusterer -a opencode -y
Windsurf
$ npx skills add guia-matthieu/clawfu-skills --skill keyword-clusterer -a windsurf -y
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"
> Group keywords by semantic similarity using embeddings - turn a keyword list into an organized content strategy.
| 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 |
pip install scikit-learn sentence-transformers pandas click # For simpler usage without ML: pip install click pandas
python scripts/main.py cluster keywords.csv --n-clusters 10 python scripts/main.py cluster keywords.csv --column keyword --n-clusters 15
python scripts/main.py similar "content marketing" --count 20
python scripts/main.py intent keywords.csv --column keyword
# 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 # ...
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
| 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 |
| Method | Best For | Speed | |--------|----------|-------| | **semantic** | Meaning-based grouping | Slower | | **lexical** | Word overlap grouping | Faster | | **intent** | Search intent categories | Fast |
category: seo-tools subcategory: keyword-research dependencies: [scikit-learn, sentence-transformers, pandas] difficulty: intermediate time_saved: 5+ hours/week
175 expert marketing methodologies for AI agents. Free. Open source. MIT licensed. Dunford on positioning. Schwartz on copywriting. Cialdini on persuasion. Ogilvy on advertising. Hormozi on offers. Voss on negotiation.
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