Blog Cannibalization
Detect keyword cannibalization across blog posts by extracting primary keywords from...
Install
npx skills add AgriciDaniel/claude-blog --skill blog-cannibalization -g -a claude-code -y- Repository
- AgriciDaniel/claude-blog2.6k
- Category
- Writing
- Updated
- 6d ago
- License
- MIT
- Context cost
- Mediumabout 2,040 tokens
- Runs scripts
- No, instructions only
What it does
Detect keyword cannibalization across blog posts by extracting primary keywords from titles and headings, clustering semantically similar targets, and flagging posts competing for the same search intent. Supports local-only mode (grep-based) and DataForSEO API mode (Page Intersection endpoint at ~$0.01/call). Outputs severity-scored report with merge or differentiate recommendations. Use when user says "cannibalization", "keyword overlap", "competing pages", "duplicate keywords", "cannibalize".
Blog Cannibalization - Keyword Overlap Detection
Detect when multiple blog posts compete for the same search keywords. Two modes: local-only analysis (default) and DataForSEO API mode for SERP-level data.
Two Modes
| Mode | Flag | Cost | Data Source |
|---|---|---|---|
| Local | (default) | Free | File content analysis via Grep/Read |
| API | --api | ~$0.01/call | DataForSEO Page Intersection + Ranked Keywords |
Local mode works without any API keys. API mode requires DataForSEO credentials
set as environment variables: DATAFORSEO_LOGIN and DATAFORSEO_PASSWORD.
Local Mode Workflow
Step 1: Scan Blog Files
Use Glob to find all content files in the target directory:
- Patterns:
**/*.md,**/*.mdx,**/*.html - Skip files in
node_modules/,.git/,drafts/
Step 2: Extract Primary Keywords
For each file, read and extract keyword signals from:
- Title tag or H1 heading (highest weight)
- H2 headings (medium weight)
- First paragraph (supporting signal)
- Meta description if present in frontmatter
Primary keyword extraction method:
- Tokenize title, H1, H2s, meta description, and first paragraph into 1-gram, 2-gram, and 3-gram phrases.
- Normalize deterministically: lowercase, remove locale-aware stop words, lemmatize or stem consistently, preserve product names, and keep intent modifiers such as "best", "pricing", "vs", "review", "template", and year.
- Score sections separately: title/H1 highest, meta description and H2s medium, first paragraph supporting.
- Select the top-scoring 2-3 word phrase as the primary keyword and record secondary keywords from H2 headings.
Step 3: Cluster by Similarity
Group posts into clusters using these matching rules (in priority order):
