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blog-cannibalization

@infrasity-labs · 收录于 1 周前

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".

适合你,如果管理多个博客页面,担心它们互相竞争同一搜索意图。

/ 下载安装
blog-cannibalization.skill双击,或拖进 Claude 桌面版 / Cowork,即完成安装↓ .skill↓ .zip
用别的 agent?下载 .zip 解压,把文件夹放进它的技能目录
Claude Code~/.claude/skills/(项目级 .claude/skills/)
Codex CLI~/.codex/skills/
Cursor自动读取上面两处目录
其他工具见其文档的「skills」目录;两个下载是同一份文件,只是名字不同
/ 通过 npx 安装 校验哈希
npx oh-my-skill add infrasity-labs/dev-gtm-claude-skills/blog-cannibalization
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- infrasity-labs/dev-gtm-claude-skills/blog-cannibalization
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify infrasity-labs/dev-gtm-claude-skills/blog-cannibalization
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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怎么用

技能原文 SKILL.md作者撰写 · MIT · 02cfefb

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:

  1. Tokenize title and H1 into 1-gram, 2-gram, and 3-gram phrases
  2. Score each phrase by frequency across title + H2s + first paragraph
  3. Select the top-scoring 2-3 word phrase as the primary keyword
  4. Record secondary keywords from H2 headings
Step 3: Cluster by Similarity

Group posts into clusters using these matching rules (in priority order):

  1. Exact match - identical primary keyword across 2+ posts
  2. Stem match - same root word (e.g., "optimize" vs "optimization")
  3. Semantic overlap - Claude determines that two keywords target the same search intent (e.g., "best CRM software" vs "top CRM tools 2026")
  4. Subset match - one keyword contains another (e.g., "email marketing" vs "email marketing for startups")
Step 4: Score and Flag

For each cluster with 2+ posts, assess severity and generate a recommendation.

Step 5: Output Report

Display the results table and per-cluster recommendations.

API Mode Workflow (DataForSEO)

Requires the --api flag. Uses WebFetch to call DataForSEO endpoints.

Endpoints Used

Page Intersection - find keywords where multiple URLs rank:

POST https://api.dataforseo.com/v3/dataforseo_labs/google/page_intersection/live
Authorization: Basic <base64(login:password)>

{
  "pages": {
    "1": "https://example.com/post-a",
    "2": "https://example.com/post-b"
  },
  "language_code": "en",
  "location_code": 2840
}

Cost: ~$0.01 per call. Returns overlapping keywords with position, volume, CPC.

Ranked Keywords - get all keywords a single URL ranks for:

POST https://api.dataforseo.com/v3/dataforseo_labs/google/ranked_keywords/live

{
  "target": "https://example.com/post-a",
  "language_code": "en",
  "location_code": 2840
}
API Analysis Steps
  1. Collect all published URLs from the user (or sitemap)
  2. Run Ranked Keywords for each URL to build keyword profiles
  3. Run Page Intersection for URL pairs that share keyword clusters
  4. Calculate severity using the formula below
  5. Output enriched report with search volume and position data
Severity Scoring

Four severity levels based on overlap signals:

| Level | Criteria | Action Urgency | |-------|----------|----------------| | Critical | Same exact keyword, both pages in top 20 | Immediate | | High | Same keyword cluster, one page outranks the other | This week | | Medium | Related keywords with partial SERP overlap | This month | | Low | Semantic similarity but different confirmed intents | Monitor |

Severity Formula (API Mode)
severity_score = overlap_count x avg_search_volume x (1 / position_gap)

Where:

  • overlap_count = number of shared ranking keywords
  • avg_search_volume = mean monthly volume of shared keywords
  • position_gap = absolute difference in average ranking position (min 1)

Higher score = more urgent cannibalization problem.

Severity Heuristic (Local Mode)

Without SERP data, use a simplified scoring:

  • Critical: Exact primary keyword match between posts
  • High: Stem match on primary keyword, or 3+ shared H2 keywords
  • Medium: Semantic overlap on primary keyword
  • Low: Subset match only, or shared secondary keywords
Output Format
Summary Table
| Post A | Post B | Shared Keywords | Severity | Recommendation |
|--------|--------|-----------------|----------|----------------|
| /best-crm-tools | /top-crm-software | best crm, crm tools, crm software | Critical | MERGE |
| /email-tips | /email-marketing-guide | email marketing | High | DIFFERENTIATE |
| /seo-basics | /seo-for-beginners | seo basics, beginner seo | Critical | CANONICAL |
| /react-hooks | /react-state-mgmt | react, state | Low | NO ACTION |
Per-Cluster Detail

For each flagged cluster, provide:

  • Both post titles and URLs
  • Full list of overlapping keywords (with volume if API mode)
  • Which post is stronger (more comprehensive, better structured)
  • Specific recommendation with rationale
Recommendations

Four possible actions for each cannibalization cluster:

MERGE

When both pages are thin or cover the same intent with similar depth.

  • Combine the best content from both into one comprehensive post
  • 301 redirect the weaker URL to the merged post
  • Preserve all internal links pointing to either URL
DIFFERENTIATE

When pages serve different intents but keyword targeting overlaps.

  • Shift the primary keyword of the weaker post to a related long-tail
  • Update the title, H1, and meta description to reflect the new focus
  • Add internal links between the two posts to signal distinct topics
CANONICAL

When one post is clearly the authority and the other is a lesser duplicate.

  • Add rel="canonical" on the weaker page pointing to the authority
  • Consider noindexing the weaker page if it adds no unique value
  • Link from the weaker page to the authority page
NO ACTION

When intent is genuinely different despite surface-level keyword similarity.

  • Document the reasoning for future audits
  • Monitor rankings quarterly for any position changes
  • Re-evaluate if either post drops in rankings
Error Handling
  • No blog files found: If the directory contains no .md, .mdx, or .html files, report "No blog files found in [directory]" and suggest checking the path
  • DataForSEO credentials missing: In API mode, if credentials are not configured, fall back to local mode automatically and notify the user
  • API rate limits: DataForSEO has per-minute rate limits. If a 429 response is received, wait and retry once. If it persists, switch to local mode for remaining URLs
  • WebFetch failures: If a source URL is unreachable, skip it and note "Unable to verify - source unavailable" in the report
  • Single-post directory: If only one blog post exists, report "Cannibalization analysis requires at least 2 posts" and exit gracefully
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