Andrew Yim
a3ff965b87
12 new skills: Keyword Strategy, SERP Analysis, Position Tracking, Link Building, Content Strategy, E-Commerce SEO, KPI Framework, International SEO, AI Visibility, Knowledge Graph, Competitor Intel, and Crawl Budget. ~20K lines of Python across 25 domain scripts. Updated skill 11 pipeline table and repo CLAUDE.md. Enhanced skill 18 local SEO workflow from jamie.clinic audit. Note: Skill 26 hreflang_validator.py pending (content filter block). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
140 lines
4.6 KiB
Markdown
140 lines
4.6 KiB
Markdown
# CLAUDE.md
|
|
|
|
## Overview
|
|
|
|
Knowledge Graph and Entity SEO tool for analyzing brand entity presence in Google Knowledge Graph, Knowledge Panels, People Also Ask (PAA), and FAQ rich results. Checks entity attribute completeness, Wikipedia/Wikidata presence, and Korean equivalents (Naver knowledge iN, Naver encyclopedia). Uses WebSearch and WebFetch for data collection, Ahrefs serp-overview for SERP feature detection.
|
|
|
|
## Quick Start
|
|
|
|
```bash
|
|
pip install -r scripts/requirements.txt
|
|
|
|
# Knowledge Graph analysis
|
|
python scripts/knowledge_graph_analyzer.py --entity "Samsung Electronics" --json
|
|
|
|
# Entity SEO audit
|
|
python scripts/entity_auditor.py --url https://example.com --entity "Brand Name" --json
|
|
```
|
|
|
|
## Scripts
|
|
|
|
| Script | Purpose | Key Output |
|
|
|--------|---------|------------|
|
|
| `knowledge_graph_analyzer.py` | Analyze Knowledge Panel and entity presence | KP detection, entity attributes, Wikipedia/Wikidata status |
|
|
| `entity_auditor.py` | Audit entity SEO signals and PAA/FAQ presence | PAA monitoring, FAQ schema tracking, entity completeness |
|
|
| `base_client.py` | Shared utilities | RateLimiter, ConfigManager, BaseAsyncClient |
|
|
|
|
## Knowledge Graph Analyzer
|
|
|
|
```bash
|
|
# Analyze entity in Knowledge Graph
|
|
python scripts/knowledge_graph_analyzer.py --entity "Samsung Electronics" --json
|
|
|
|
# Check with Korean name
|
|
python scripts/knowledge_graph_analyzer.py --entity "삼성전자" --language ko --json
|
|
|
|
# Include Wikipedia/Wikidata check
|
|
python scripts/knowledge_graph_analyzer.py --entity "Samsung" --wiki --json
|
|
```
|
|
|
|
**Capabilities**:
|
|
- Knowledge Panel detection via Google search
|
|
- Entity attribute extraction (name, description, logo, type, social profiles, website)
|
|
- Entity attribute completeness scoring
|
|
- Wikipedia article presence check
|
|
- Wikidata entity presence check (QID lookup)
|
|
- Naver encyclopedia (네이버 백과사전) presence
|
|
- Naver knowledge iN (지식iN) presence
|
|
- Knowledge Panel comparison with competitors
|
|
|
|
## Entity Auditor
|
|
|
|
```bash
|
|
# Full entity SEO audit
|
|
python scripts/entity_auditor.py --url https://example.com --entity "Brand Name" --json
|
|
|
|
# PAA monitoring for brand keywords
|
|
python scripts/entity_auditor.py --url https://example.com --entity "Brand Name" --paa --json
|
|
|
|
# FAQ rich result tracking
|
|
python scripts/entity_auditor.py --url https://example.com --entity "Brand Name" --faq --json
|
|
```
|
|
|
|
**Capabilities**:
|
|
- People Also Ask (PAA) monitoring for brand-related queries
|
|
- FAQ schema presence tracking (FAQPage schema -> SERP appearance)
|
|
- Entity markup audit (Organization, Person, LocalBusiness schema on website)
|
|
- Social profile linking validation (sameAs in schema)
|
|
- Brand SERP analysis (what appears when you search the brand name)
|
|
- Entity consistency across web properties
|
|
- Korean entity optimization (Korean Knowledge Panel, Naver profiles)
|
|
|
|
## Data Sources
|
|
|
|
| Source | Purpose |
|
|
|--------|---------|
|
|
| WebSearch | Search for entity/brand to detect Knowledge Panel |
|
|
| WebFetch | Fetch Wikipedia, Wikidata, Naver pages |
|
|
| Ahrefs `serp-overview` | SERP feature detection for entity keywords |
|
|
|
|
## Output Format
|
|
|
|
```json
|
|
{
|
|
"entity": "Samsung Electronics",
|
|
"knowledge_panel": {
|
|
"detected": true,
|
|
"attributes": {
|
|
"name": "Samsung Electronics",
|
|
"type": "Corporation",
|
|
"description": "...",
|
|
"logo": true,
|
|
"website": true,
|
|
"social_profiles": ["twitter", "facebook", "linkedin"]
|
|
},
|
|
"completeness_score": 85
|
|
},
|
|
"wikipedia": {"present": true, "url": "..."},
|
|
"wikidata": {"present": true, "qid": "Q20710"},
|
|
"naver_encyclopedia": {"present": true, "url": "..."},
|
|
"naver_knowledge_in": {"present": true, "entries": 15},
|
|
"paa_questions": [...],
|
|
"faq_rich_results": [...],
|
|
"entity_schema_on_site": {
|
|
"organization": true,
|
|
"same_as_links": 5,
|
|
"completeness": 78
|
|
},
|
|
"score": 75,
|
|
"timestamp": "2025-01-01T00:00:00"
|
|
}
|
|
```
|
|
|
|
## Notion Output (Required)
|
|
|
|
**IMPORTANT**: All audit reports MUST be saved to the OurDigital SEO Audit Log database.
|
|
|
|
### Database Configuration
|
|
|
|
| Field | Value |
|
|
|-------|-------|
|
|
| Database ID | `2c8581e5-8a1e-8035-880b-e38cefc2f3ef` |
|
|
| URL | https://www.notion.so/dintelligence/2c8581e58a1e8035880be38cefc2f3ef |
|
|
|
|
### Required Properties
|
|
|
|
| Property | Type | Description |
|
|
|----------|------|-------------|
|
|
| Issue | Title | Report title (Korean + date) |
|
|
| Site | URL | Entity website URL |
|
|
| Category | Select | Knowledge Graph & Entity SEO |
|
|
| Priority | Select | Based on entity completeness |
|
|
| Found Date | Date | Audit date (YYYY-MM-DD) |
|
|
| Audit ID | Rich Text | Format: KG-YYYYMMDD-NNN |
|
|
|
|
### Language Guidelines
|
|
|
|
- Report content in Korean (한국어)
|
|
- Keep technical English terms as-is (e.g., Knowledge Panel, Knowledge Graph, PAA)
|
|
- URLs and code remain unchanged
|