Files
our-claude-skills/custom-skills/20-seo-serp-analysis/desktop/SKILL.md
Andrew Yim d2d0a2d460 Add SEO skills 33-34 and fix bugs in skills 19-34
New skills:
- Skill 33: Site migration planner with redirect mapping and monitoring
- Skill 34: Reporting dashboard with HTML charts and Korean executive reports

Bug fixes (Skill 34 - report_aggregator.py):
- Add audit_type fallback for skill identification (was only using audit_id prefix)
- Extract health scores from nested data dict (technical_score, onpage_score, etc.)
- Support subdomain matching in domain filter (blog.ourdigital.org matches ourdigital.org)
- Skip self-referencing DASH- aggregated reports

Bug fixes (Skill 20 - naver_serp_analyzer.py):
- Remove VIEW tab selectors (removed by Naver in 2026)
- Add new section detectors: books (도서), shortform (숏폼), influencer (인플루언서)

Improvements (Skill 34 - dashboard/executive report):
- Add Korean category labels for Chart.js charts (기술 SEO, 온페이지, etc.)
- Add Korean trend labels (개선 중 ↑, 안정 →, 하락 중 ↓)
- Add English→Korean issue description translation layer (20 common patterns)

Documentation improvements:
- Add Korean triggers to 4 skill descriptions (19, 25, 28, 31)
- Expand Skill 32 SKILL.md from 40→143 lines (was 6/10, added workflow, output format, limitations)
- Add output format examples to Skills 27 and 28 SKILL.md
- Add limitations sections to Skills 27 and 28
- Update README.md, CLAUDE.md, AGENTS.md for skills 33-34

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-02-14 00:01:00 +09:00

4.5 KiB

name, description
name description
seo-serp-analysis SERP analysis for Google and Naver search results. Triggers: SERP analysis, search results, featured snippet, SERP features, Naver SERP, 검색결과 분석, SERP 분석.

SEO SERP Analysis

Purpose

Analyze search engine result page composition for Google and Naver. Detect SERP features (featured snippets, PAA, knowledge panels, local pack, video, ads), map competitor positions, score SERP feature opportunities, and analyze Naver section distribution.

Core Capabilities

  1. Google SERP Feature Detection - Identify featured snippets, PAA, knowledge panels, local pack, video carousel, ads, image pack, site links, shopping
  2. Competitor Position Mapping - Extract domains, positions, content types for top organic results
  3. Opportunity Scoring - Score SERP opportunity (0-100) based on feature landscape and competition
  4. Search Intent Validation - Infer intent (informational, navigational, commercial, transactional, local) from SERP composition
  5. Naver SERP Composition - Detect sections (blog, cafe, knowledge iN, Smart Store, brand zone, books, shortform, influencer), map section priority, analyze brand zone presence

MCP Tool Usage

Ahrefs for SERP Data

mcp__ahrefs__serp-overview: Get SERP results and features for a keyword
mcp__ahrefs__keywords-explorer-overview: Get keyword metrics, volume, difficulty, and SERP feature flags
mcp__ahrefs__site-explorer-organic-keywords: Map competitor keyword positions

Notion for Report Storage

mcp__notion__notion-create-pages: Save analysis report to SEO Audit Log database
mcp__notion__notion-update-page: Update existing report entries

Web Tools for Naver Analysis

WebSearch: Discover Naver search trends
WebFetch: Fetch Naver SERP HTML for section analysis

Workflow

1. Google SERP Analysis

  1. Fetch SERP data via mcp__ahrefs__serp-overview for the target keyword and country
  2. Detect SERP features (featured snippet, PAA, local pack, knowledge panel, video, ads, images, shopping)
  3. Map competitor positions from organic results (domain, URL, title, position)
  4. Classify content type for each result (blog, product, service, news, video)
  5. Calculate opportunity score (0-100) based on feature landscape
  6. Validate search intent from SERP composition
  7. Assess SERP volatility

2. Naver SERP Analysis

  1. Fetch Naver search page for the target keyword
  2. Detect SERP sections (blog, cafe, knowledge iN, Smart Store, brand zone, news, encyclopedia, books, shortform, influencer)
  3. Map section priority (above-fold order)
  4. Check brand zone presence and extract brand name
  5. Count items per section
  6. Identify dominant content section

3. Report Generation

  1. Compile results into structured JSON
  2. Generate Korean-language report
  3. Save to Notion SEO Audit Log database

Output Format

{
  "keyword": "치과 임플란트",
  "country": "kr",
  "serp_features": {
    "featured_snippet": true,
    "people_also_ask": true,
    "local_pack": true,
    "knowledge_panel": false,
    "video_carousel": false,
    "ads_top": 3,
    "ads_bottom": 2
  },
  "competitors": [
    {
      "position": 1,
      "url": "https://example.com/page",
      "domain": "example.com",
      "title": "...",
      "content_type": "service_page"
    }
  ],
  "opportunity_score": 72,
  "intent_signals": "commercial",
  "timestamp": "2025-01-01T00:00:00"
}

Common SERP Features

Feature Impact Opportunity
Featured Snippet High visibility above organic Optimize content format for snippet capture
People Also Ask Related question visibility Create FAQ content targeting PAA
Local Pack Dominates local intent SERPs Optimize Google Business Profile
Knowledge Panel Reduces organic CTR Focus on brand queries and schema
Video Carousel Visual SERP real estate Create video content for keyword
Shopping Transactional intent signal Product feed optimization

Limitations

  • Ahrefs SERP data may have a delay (not real-time)
  • Naver SERP HTML structure changes periodically
  • Brand zone detection depends on HTML class patterns
  • Cannot detect personalized SERP results

Notion Output (Required)

All audit reports MUST be saved to OurDigital SEO Audit Log:

  • Database ID: 2c8581e5-8a1e-8035-880b-e38cefc2f3ef
  • Properties: Issue (title), Site (url), Category, Priority, Found Date, Audit ID
  • Language: Korean with English technical terms
  • Audit ID Format: SERP-YYYYMMDD-NNN