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Add SEO skills 19-28, 31-32 with full Python implementations

Browse Source 
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>
This commit is contained in:
Andrew Yim
2026-02-13 12:05:59 +09:00
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# CLAUDE.md
## Overview
SERP analysis tool for understanding search result landscapes. Detects Google SERP features (featured snippets, PAA, knowledge panels, local pack, video, ads), analyzes Naver SERP composition (blog, cafe, knowledge iN, Smart Store, brand zone, VIEW tab), maps competitor positions, and scores SERP feature opportunities.
## Quick Start
```bash
pip install -r scripts/requirements.txt
# Google SERP analysis
python scripts/serp_analyzer.py --keyword "치과 임플란트" --country kr --json
# Naver SERP analysis
python scripts/naver_serp_analyzer.py --keyword "치과 임플란트" --json
```
## Scripts
| Script | Purpose | Key Output |
|--------|---------|------------|
| `serp_analyzer.py` | Google SERP feature detection and competitor mapping | SERP features, competitor positions, opportunity scores |
| `naver_serp_analyzer.py` | Naver SERP composition analysis | Section distribution, content type mapping |
| `base_client.py` | Shared utilities | RateLimiter, ConfigManager, BaseAsyncClient |
## SERP Analyzer (Google)
```bash
# Single keyword analysis
python scripts/serp_analyzer.py --keyword "dental implant cost" --json
# Korean market
python scripts/serp_analyzer.py --keyword "치과 임플란트 가격" --country kr --json
# Multiple keywords from file
python scripts/serp_analyzer.py --keywords-file keywords.txt --country kr --json
# Output to file
python scripts/serp_analyzer.py --keyword "dental implant" --output serp_report.json
```
**Capabilities**:
- SERP feature detection (featured snippet, PAA, knowledge panel, local pack, video carousel, ads, image pack, site links)
- Competitor position mapping per keyword
- Content type distribution analysis (blog, product, service, news, video)
- SERP feature opportunity scoring
- Search intent validation from SERP composition
- SERP volatility assessment
## Naver SERP Analyzer
```bash
# Analyze Naver search results
python scripts/naver_serp_analyzer.py --keyword "치과 임플란트" --json
# Analyze multiple keywords
python scripts/naver_serp_analyzer.py --keywords-file keywords.txt --json
```
**Capabilities**:
- Naver section detection (블로그, 카페, 지식iN, 스마트스토어, 브랜드존, VIEW탭)
- Section priority mapping (which sections appear above fold)
- Content type distribution per section
- Brand zone presence detection
- VIEW tab content analysis
## Ahrefs MCP Tools Used
| Tool | Purpose |
|------|---------|
| `serp-overview` | Get SERP results for a keyword |
| `keywords-explorer-overview` | Get keyword metrics and SERP features |
| `site-explorer-organic-keywords` | Map competitor positions |
## Output Format
```json
{
"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"
}
```
## 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 | Audited website URL |
| Category | Select | SERP Analysis |
| Priority | Select | Based on opportunity score |
| Found Date | Date | Analysis date (YYYY-MM-DD) |
| Audit ID | Rich Text | Format: SERP-YYYYMMDD-NNN |
### Language Guidelines
- Report content in Korean (한국어)
- Keep technical English terms as-is (e.g., SERP, Featured Snippet, PAA)
- URLs and code remain unchanged

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"""
Base Client - Shared async client utilities
===========================================
Purpose: Rate-limited async operations for API clients
Python: 3.10+
"""
import asyncio
import logging
import os
from asyncio import Semaphore
from datetime import datetime
from typing import Any, Callable, TypeVar
from dotenv import load_dotenv
from tenacity import (
retry,
stop_after_attempt,
wait_exponential,
retry_if_exception_type,
)
# Load environment variables
load_dotenv()
# Logging setup
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
)
T = TypeVar("T")
class RateLimiter:
"""Rate limiter using token bucket algorithm."""
def __init__(self, rate: float, per: float = 1.0):
"""
Initialize rate limiter.
Args:
rate: Number of requests allowed
per: Time period in seconds (default: 1 second)
"""
self.rate = rate
self.per = per
self.tokens = rate
self.last_update = datetime.now()
self._lock = asyncio.Lock()
async def acquire(self) -> None:
"""Acquire a token, waiting if necessary."""
async with self._lock:
now = datetime.now()
elapsed = (now - self.last_update).total_seconds()
self.tokens = min(self.rate, self.tokens + elapsed * (self.rate / self.per))
self.last_update = now
if self.tokens < 1:
wait_time = (1 - self.tokens) * (self.per / self.rate)
await asyncio.sleep(wait_time)
self.tokens = 0
else:
self.tokens -= 1
class BaseAsyncClient:
"""Base class for async API clients with rate limiting."""
def __init__(
self,
max_concurrent: int = 5,
requests_per_second: float = 3.0,
logger: logging.Logger | None = None,
):
"""
Initialize base client.
Args:
max_concurrent: Maximum concurrent requests
requests_per_second: Rate limit
logger: Logger instance
"""
self.semaphore = Semaphore(max_concurrent)
self.rate_limiter = RateLimiter(requests_per_second)
self.logger = logger or logging.getLogger(self.__class__.__name__)
self.stats = {
"requests": 0,
"success": 0,
"errors": 0,
"retries": 0,
}
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=2, max=10),
retry=retry_if_exception_type(Exception),
)
async def _rate_limited_request(
self,
coro: Callable[[], Any],
) -> Any:
"""Execute a request with rate limiting and retry."""
async with self.semaphore:
await self.rate_limiter.acquire()
self.stats["requests"] += 1
try:
result = await coro()
self.stats["success"] += 1
return result
except Exception as e:
self.stats["errors"] += 1
self.logger.error(f"Request failed: {e}")
raise
async def batch_requests(
self,
requests: list[Callable[[], Any]],
desc: str = "Processing",
) -> list[Any]:
"""Execute multiple requests concurrently."""
try:
from tqdm.asyncio import tqdm
has_tqdm = True
except ImportError:
has_tqdm = False
async def execute(req: Callable) -> Any:
try:
return await self._rate_limited_request(req)
except Exception as e:
return {"error": str(e)}
tasks = [execute(req) for req in requests]
if has_tqdm:
results = []
for coro in tqdm.as_completed(tasks, total=len(tasks), desc=desc):
result = await coro
results.append(result)
return results
else:
return await asyncio.gather(*tasks, return_exceptions=True)
def print_stats(self) -> None:
"""Print request statistics."""
self.logger.info("=" * 40)
self.logger.info("Request Statistics:")
self.logger.info(f" Total Requests: {self.stats['requests']}")
self.logger.info(f" Successful: {self.stats['success']}")
self.logger.info(f" Errors: {self.stats['errors']}")
self.logger.info("=" * 40)
class ConfigManager:
"""Manage API configuration and credentials."""
def __init__(self):
load_dotenv()
@property
def google_credentials_path(self) -> str | None:
"""Get Google service account credentials path."""
# Prefer SEO-specific credentials, fallback to general credentials
seo_creds = os.path.expanduser("~/.credential/ourdigital-seo-agent.json")
if os.path.exists(seo_creds):
return seo_creds
return os.getenv("GOOGLE_APPLICATION_CREDENTIALS")
@property
def pagespeed_api_key(self) -> str | None:
"""Get PageSpeed Insights API key."""
return os.getenv("PAGESPEED_API_KEY")
@property
def custom_search_api_key(self) -> str | None:
"""Get Custom Search API key."""
return os.getenv("CUSTOM_SEARCH_API_KEY")
@property
def custom_search_engine_id(self) -> str | None:
"""Get Custom Search Engine ID."""
return os.getenv("CUSTOM_SEARCH_ENGINE_ID")
@property
def notion_token(self) -> str | None:
"""Get Notion API token."""
return os.getenv("NOTION_TOKEN") or os.getenv("NOTION_API_KEY")
def validate_google_credentials(self) -> bool:
"""Validate Google credentials are configured."""
creds_path = self.google_credentials_path
if not creds_path:
return False
return os.path.exists(creds_path)
def get_required(self, key: str) -> str:
"""Get required environment variable or raise error."""
value = os.getenv(key)
if not value:
raise ValueError(f"Missing required environment variable: {key}")
return value
# Singleton config instance
config = ConfigManager()

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"""
Naver SERP Analyzer - Naver search result composition analysis
==============================================================
Purpose: Analyze Naver SERP section distribution, content type mapping,
brand zone detection, and VIEW tab content analysis.
Python: 3.10+
Usage:
python naver_serp_analyzer.py --keyword "치과 임플란트" --json
python naver_serp_analyzer.py --keywords-file keywords.txt --json
python naver_serp_analyzer.py --keyword "치과 임플란트" --output naver_report.json
"""
import argparse
import json
import logging
import re
import sys
from dataclasses import asdict, dataclass, field
from datetime import datetime
from pathlib import Path
from typing import Any
import requests
from bs4 import BeautifulSoup
from rich.console import Console
from rich.table import Table
# ---------------------------------------------------------------------------
# Logging
# ---------------------------------------------------------------------------
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
)
logger = logging.getLogger(__name__)
console = Console()
# ---------------------------------------------------------------------------
# Constants - Naver SERP Section Identifiers
# ---------------------------------------------------------------------------
# CSS class / id patterns used to detect Naver SERP sections
NAVER_SECTION_SELECTORS: dict[str, list[str]] = {
"blog": [
"sp_blog",
"blog_widget",
"sc_new.sp_blog",
"api_subject_blog",
"type_blog",
"blog_exact",
],
"cafe": [
"sp_cafe",
"cafe_widget",
"sc_new.sp_cafe",
"api_subject_cafe",
"type_cafe",
],
"knowledge_in": [
"sp_kin",
"kin_widget",
"sc_new.sp_kin",
"api_subject_kin",
"type_kin",
"nx_kin",
],
"smart_store": [
"sp_nshop",
"shopping_widget",
"sc_new.sp_nshop",
"api_subject_shopping",
"type_shopping",
"smartstore",
],
"brand_zone": [
"sp_brand",
"brand_area",
"brand_zone",
"type_brand",
"sc_new.sp_brand",
],
"view_tab": [
"sp_view",
"view_widget",
"sc_new.sp_view",
"type_view",
"api_subject_view",
],
"news": [
"sp_nnews",
"news_widget",
"sc_new.sp_nnews",
"api_subject_news",
"type_news",
"group_news",
],
"encyclopedia": [
"sp_encyclopedia",
"sc_new.sp_encyclopedia",
"api_subject_encyclopedia",
"type_encyclopedia",
"nx_encyclopedia",
],
"image": [
"sp_image",
"image_widget",
"sc_new.sp_image",
"api_subject_image",
"type_image",
],
"video": [
"sp_video",
"video_widget",
"sc_new.sp_video",
"api_subject_video",
"type_video",
],
"place": [
"sp_local",
"local_widget",
"sc_new.sp_local",
"type_place",
"place_section",
"loc_map",
],
"ad": [
"sp_nad",
"sp_tad",
"ad_section",
"type_powerlink",
"type_ad",
"nx_ad",
],
}
# Section display names in Korean
SECTION_DISPLAY_NAMES: dict[str, str] = {
"blog": "블로그",
"cafe": "카페",
"knowledge_in": "지식iN",
"smart_store": "스마트스토어",
"brand_zone": "브랜드존",
"view_tab": "VIEW",
"news": "뉴스",
"encyclopedia": "백과사전",
"image": "이미지",
"video": "동영상",
"place": "플레이스",
"ad": "광고",
}
# Default headers for Naver requests
NAVER_HEADERS = {
"User-Agent": (
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
"AppleWebKit/537.36 (KHTML, like Gecko) "
"Chrome/120.0.0.0 Safari/537.36"
),
"Accept-Language": "ko-KR,ko;q=0.9,en-US;q=0.8,en;q=0.7",
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
}
# ---------------------------------------------------------------------------
# Data Classes
# ---------------------------------------------------------------------------
@dataclass
class NaverSection:
"""A detected section within Naver SERP."""
section_type: str # blog, cafe, knowledge_in, smart_store, etc.
display_name: str = ""
position: int = 0 # Order of appearance (1-based)
item_count: int = 0 # Number of items in the section
is_above_fold: bool = False # Appears within first ~3 sections
has_more_link: bool = False # Section has "more results" link
raw_html_snippet: str = "" # Short HTML snippet for debugging
def __post_init__(self):
if not self.display_name:
self.display_name = SECTION_DISPLAY_NAMES.get(
self.section_type, self.section_type
)
@dataclass
class NaverSerpResult:
"""Complete Naver SERP analysis result for a keyword."""
keyword: str
sections: list[NaverSection] = field(default_factory=list)
section_order: list[str] = field(default_factory=list)
brand_zone_present: bool = False
brand_zone_brand: str = ""
total_sections: int = 0
above_fold_sections: list[str] = field(default_factory=list)
ad_count: int = 0
dominant_section: str = ""
has_view_tab: bool = False
has_place_section: bool = False
timestamp: str = ""
def __post_init__(self):
if not self.timestamp:
self.timestamp = datetime.now().isoformat()
# ---------------------------------------------------------------------------
# Naver SERP Analyzer
# ---------------------------------------------------------------------------
class NaverSerpAnalyzer:
"""Analyzes Naver search result page composition."""
NAVER_SEARCH_URL = "https://search.naver.com/search.naver"
def __init__(self, timeout: int = 30):
self.timeout = timeout
self.logger = logging.getLogger(self.__class__.__name__)
self.session = requests.Session()
self.session.headers.update(NAVER_HEADERS)
# ----- Data Fetching -----
def fetch_serp(self, keyword: str) -> str:
"""
Fetch Naver search results HTML for a given keyword.
Returns the raw HTML string of the search results page.
"""
self.logger.info(f"Fetching Naver SERP for '{keyword}'")
params = {
"where": "nexearch",
"sm": "top_hty",
"fbm": "0",
"ie": "utf8",
"query": keyword,
}
try:
response = self.session.get(
self.NAVER_SEARCH_URL,
params=params,
timeout=self.timeout,
)
response.raise_for_status()
self.logger.info(
f"Fetched {len(response.text):,} bytes "
f"(status={response.status_code})"
)
return response.text
except requests.RequestException as exc:
self.logger.error(f"Failed to fetch Naver SERP: {exc}")
return ""
# ----- Section Detection -----
def detect_sections(self, html: str) -> list[NaverSection]:
"""
Identify Naver SERP sections from HTML structure.
Scans the HTML for known CSS class names and IDs that correspond
to Naver's SERP section types.
"""
if not html:
return []
soup = BeautifulSoup(html, "lxml")
sections: list[NaverSection] = []
position = 0
# Strategy 1: Look for section containers with known class names
# Naver uses <div class="sc_new sp_XXX"> and <section> elements
all_sections = soup.find_all(
["div", "section"],
class_=re.compile(
r"(sc_new|api_subject|sp_|type_|_widget|group_|nx_)"
),
)
seen_types: set[str] = set()
for element in all_sections:
classes = " ".join(element.get("class", []))
element_id = element.get("id", "")
search_text = f"{classes} {element_id}".lower()
for section_type, selectors in NAVER_SECTION_SELECTORS.items():
if section_type in seen_types:
continue
matched = False
for selector in selectors:
if selector.lower() in search_text:
matched = True
break
if matched:
position += 1
seen_types.add(section_type)
# Count items within the section
item_count = self._count_section_items(element, section_type)
# Check for "more" link
has_more = bool(
element.find("a", class_=re.compile(r"(more|_more|btn_more)"))
or element.find("a", string=re.compile(r"(더보기|전체보기)"))
)
# Get short HTML snippet for debugging
snippet = str(element)[:200] if element else ""
section = NaverSection(
section_type=section_type,
position=position,
item_count=item_count,
is_above_fold=(position <= 3),
has_more_link=has_more,
raw_html_snippet=snippet,
)
sections.append(section)
# Strategy 2: Fallback - scan entire HTML text for section markers
if not sections:
self.logger.warning(
"No sections found via DOM parsing; "
"falling back to text pattern matching"
)
sections = self._fallback_text_detection(html)
return sections
def _count_section_items(self, element: Any, section_type: str) -> int:
"""Count the number of result items within a section element."""
# Common item container patterns
item_selectors = [
"li",
".api_txt_lines",
".total_tit",
".detail_box",
".item",
".lst_total > li",
]
for selector in item_selectors:
items = element.select(selector)
if items and len(items) > 0:
return len(items)
# Fallback: count links that look like results
links = element.find_all("a", href=True)
result_links = [
a
for a in links
if a.get("href", "").startswith("http")
and "naver.com/search" not in a.get("href", "")
]
return len(result_links) if result_links else 0
def _fallback_text_detection(self, html: str) -> list[NaverSection]:
"""Detect sections by scanning raw HTML text for known markers."""
sections: list[NaverSection] = []
position = 0
html_lower = html.lower()
for section_type, selectors in NAVER_SECTION_SELECTORS.items():
for selector in selectors:
if selector.lower() in html_lower:
position += 1
sections.append(
NaverSection(
section_type=section_type,
position=position,
item_count=0,
is_above_fold=(position <= 3),
)
)
break
return sections
# ----- Section Priority Analysis -----
def analyze_section_priority(
self, sections: list[NaverSection]
) -> list[str]:
"""
Determine above-fold section order.
Returns ordered list of section types that appear in the first
visible area of the SERP (approximately top 3 sections).
"""
sorted_sections = sorted(sections, key=lambda s: s.position)
above_fold = [s.section_type for s in sorted_sections if s.is_above_fold]
return above_fold
# ----- Brand Zone Detection -----
def check_brand_zone(self, html: str) -> tuple[bool, str]:
"""
Detect brand zone presence and extract brand name if available.
Returns (is_present, brand_name).
"""
if not html:
return False, ""
soup = BeautifulSoup(html, "lxml")
# Look for brand zone container
brand_selectors = [
"sp_brand",
"brand_area",
"brand_zone",
"type_brand",
]
for selector in brand_selectors:
brand_el = soup.find(
["div", "section"],
class_=re.compile(selector, re.IGNORECASE),
)
if brand_el:
# Try to extract brand name from the section
brand_name = ""
title_el = brand_el.find(
["h2", "h3", "strong", "a"],
class_=re.compile(r"(tit|title|name|brand)", re.IGNORECASE),
)
if title_el:
brand_name = title_el.get_text(strip=True)
return True, brand_name
# Text-based fallback
if "brand_zone" in html.lower() or "sp_brand" in html.lower():
return True, ""
return False, ""
# ----- Dominant Section -----
def _find_dominant_section(self, sections: list[NaverSection]) -> str:
"""Find the section with the most items (excluding ads)."""
non_ad = [s for s in sections if s.section_type != "ad"]
if not non_ad:
return ""
return max(non_ad, key=lambda s: s.item_count).section_type
# ----- Main Analysis Orchestrator -----
def analyze(self, keyword: str) -> NaverSerpResult:
"""
Orchestrate full Naver SERP analysis for a single keyword.
Steps:
1. Fetch Naver search results page
2. Detect SERP sections
3. Analyze section priority
4. Check brand zone presence
5. Compile results
"""
html = self.fetch_serp(keyword)
if not html:
self.logger.error(f"No HTML content for keyword '{keyword}'")
return NaverSerpResult(keyword=keyword)
sections = self.detect_sections(html)
above_fold = self.analyze_section_priority(sections)
brand_present, brand_name = self.check_brand_zone(html)
# Build section order
section_order = [s.section_type for s in sorted(sections, key=lambda x: x.position)]
# Count ads
ad_sections = [s for s in sections if s.section_type == "ad"]
ad_count = sum(s.item_count for s in ad_sections) if ad_sections else 0
# Check special sections
has_view = any(s.section_type == "view_tab" for s in sections)
has_place = any(s.section_type == "place" for s in sections)
dominant = self._find_dominant_section(sections)
result = NaverSerpResult(
keyword=keyword,
sections=sections,
section_order=section_order,
brand_zone_present=brand_present,
brand_zone_brand=brand_name,
total_sections=len(sections),
above_fold_sections=above_fold,
ad_count=ad_count,
dominant_section=dominant,
has_view_tab=has_view,
has_place_section=has_place,
)
return result
# ---------------------------------------------------------------------------
# Output Helpers
# ---------------------------------------------------------------------------
def result_to_dict(result: NaverSerpResult) -> dict[str, Any]:
"""Convert NaverSerpResult to a JSON-serializable dictionary."""
d = asdict(result)
# Remove raw HTML snippets from JSON output to keep it clean
for section in d.get("sections", []):
section.pop("raw_html_snippet", None)
return d
def print_rich_report(result: NaverSerpResult) -> None:
"""Print a human-readable report using rich."""
console.rule(f"[bold blue]Naver SERP Analysis: {result.keyword}")
console.print(f"[dim]Timestamp: {result.timestamp}[/dim]")
console.print()
# Summary
summary_table = Table(title="Summary", show_lines=True)
summary_table.add_column("Metric", style="cyan")
summary_table.add_column("Value", style="green")
summary_table.add_row("Total Sections", str(result.total_sections))
summary_table.add_row("Ad Count", str(result.ad_count))
summary_table.add_row("Brand Zone", "Yes" if result.brand_zone_present else "No")
if result.brand_zone_brand:
summary_table.add_row("Brand Name", result.brand_zone_brand)
summary_table.add_row("VIEW Tab", "Yes" if result.has_view_tab else "No")
summary_table.add_row("Place Section", "Yes" if result.has_place_section else "No")
summary_table.add_row("Dominant Section", result.dominant_section or "N/A")
console.print(summary_table)
console.print()
# Section Details
if result.sections:
section_table = Table(title="Detected Sections", show_lines=True)
section_table.add_column("#", style="bold")
section_table.add_column("Section", style="cyan")
section_table.add_column("Display Name", style="magenta")
section_table.add_column("Items", style="green")
section_table.add_column("Above Fold", style="yellow")
section_table.add_column("More Link", style="dim")
for s in sorted(result.sections, key=lambda x: x.position):
section_table.add_row(
str(s.position),
s.section_type,
s.display_name,
str(s.item_count),
"Yes" if s.is_above_fold else "No",
"Yes" if s.has_more_link else "No",
)
console.print(section_table)
console.print()
# Above-Fold Sections
if result.above_fold_sections:
console.print("[bold]Above-Fold Section Order:[/bold]")
for i, sec in enumerate(result.above_fold_sections, 1):
display = SECTION_DISPLAY_NAMES.get(sec, sec)
console.print(f" {i}. {display} ({sec})")
console.print()
# Section Order
if result.section_order:
console.print("[bold]Full Section Order:[/bold]")
order_str = " -> ".join(
SECTION_DISPLAY_NAMES.get(s, s) for s in result.section_order
)
console.print(f" {order_str}")
console.rule()
# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description="Naver SERP composition analysis",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
python naver_serp_analyzer.py --keyword "치과 임플란트" --json
python naver_serp_analyzer.py --keywords-file keywords.txt --json
python naver_serp_analyzer.py --keyword "치과 임플란트" --output report.json
""",
)
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument(
"--keyword",
type=str,
help="Single keyword to analyze",
)
group.add_argument(
"--keywords-file",
type=str,
help="Path to file with one keyword per line",
)
parser.add_argument(
"--json",
action="store_true",
dest="json_output",
help="Output results as JSON",
)
parser.add_argument(
"--output",
type=str,
help="Write JSON results to file",
)
return parser
def load_keywords(filepath: str) -> list[str]:
"""Load keywords from a text file, one per line."""
path = Path(filepath)
if not path.exists():
logger.error(f"Keywords file not found: {filepath}")
sys.exit(1)
keywords = []
with open(path, "r", encoding="utf-8") as fh:
for line in fh:
kw = line.strip()
if kw and not kw.startswith("#"):
keywords.append(kw)
logger.info(f"Loaded {len(keywords)} keywords from {filepath}")
return keywords
def main() -> None:
parser = build_parser()
args = parser.parse_args()
analyzer = NaverSerpAnalyzer()
# Collect keywords
if args.keyword:
keywords = [args.keyword]
else:
keywords = load_keywords(args.keywords_file)
if not keywords:
logger.error("No keywords to analyze")
sys.exit(1)
results: list[dict[str, Any]] = []
for kw in keywords:
console.print(f"\n[bold]Analyzing Naver SERP:[/bold] {kw}")
result = analyzer.analyze(kw)
if args.json_output or args.output:
results.append(result_to_dict(result))
else:
print_rich_report(result)
# JSON output
if args.json_output:
output_data = results[0] if len(results) == 1 else results
print(json.dumps(output_data, ensure_ascii=False, indent=2))
if args.output:
output_data = results[0] if len(results) == 1 else results
output_path = Path(args.output)
with open(output_path, "w", encoding="utf-8") as fh:
json.dump(output_data, fh, ensure_ascii=False, indent=2)
logger.info(f"Results written to {output_path}")
if __name__ == "__main__":
main()

9
custom-skills/20-seo-serp-analysis/code/scripts/requirements.txt Normal file
View File

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# 20-seo-serp-analysis dependencies
requests>=2.31.0
aiohttp>=3.9.0
beautifulsoup4>=4.12.0
lxml>=5.1.0
tenacity>=8.2.0
tqdm>=4.66.0
python-dotenv>=1.0.0
rich>=13.7.0

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custom-skills/20-seo-serp-analysis/code/scripts/serp_analyzer.py Normal file
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"""
SERP Analyzer - Google SERP feature detection and competitor mapping
====================================================================
Purpose: Analyze Google SERP features, map competitor positions,
classify content types, and score SERP opportunities.
Python: 3.10+
Usage:
python serp_analyzer.py --keyword "치과 임플란트" --country kr --json
python serp_analyzer.py --keywords-file keywords.txt --country kr --json
python serp_analyzer.py --keyword "dental implant" --output serp_report.json
"""
import argparse
import json
import logging
import re
import subprocess
import sys
from dataclasses import asdict, dataclass, field
from datetime import datetime
from pathlib import Path
from typing import Any
from urllib.parse import urlparse
from rich.console import Console
from rich.table import Table
# ---------------------------------------------------------------------------
# Logging
# ---------------------------------------------------------------------------
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
)
logger = logging.getLogger(__name__)
console = Console()
# ---------------------------------------------------------------------------
# Data Classes
# ---------------------------------------------------------------------------
@dataclass
class SerpFeatures:
"""Tracks presence and count of Google SERP features."""
featured_snippet: bool = False
people_also_ask: bool = False
local_pack: bool = False
knowledge_panel: bool = False
video_carousel: bool = False
image_pack: bool = False
site_links: bool = False
ads_top: int = 0
ads_bottom: int = 0
shopping: bool = False
@property
def feature_count(self) -> int:
"""Count of boolean features that are present."""
count = 0
for f in [
self.featured_snippet,
self.people_also_ask,
self.local_pack,
self.knowledge_panel,
self.video_carousel,
self.image_pack,
self.site_links,
self.shopping,
]:
if f:
count += 1
return count
@property
def has_ads(self) -> bool:
return self.ads_top > 0 or self.ads_bottom > 0
@dataclass
class CompetitorPosition:
"""A single competitor entry in the SERP."""
position: int
url: str
domain: str
title: str = ""
content_type: str = "unknown"
is_featured: bool = False
has_sitelinks: bool = False
estimated_traffic_share: float = 0.0
@dataclass
class SerpResult:
"""Complete SERP analysis result for a keyword."""
keyword: str
country: str = "us"
search_volume: int = 0
keyword_difficulty: float = 0.0
cpc: float = 0.0
serp_features: SerpFeatures = field(default_factory=SerpFeatures)
competitors: list[CompetitorPosition] = field(default_factory=list)
opportunity_score: int = 0
intent_signals: str = "informational"
content_type_distribution: dict[str, int] = field(default_factory=dict)
volatility: str = "stable"
timestamp: str = ""
def __post_init__(self):
if not self.timestamp:
self.timestamp = datetime.now().isoformat()
# ---------------------------------------------------------------------------
# Content Type Classifiers
# ---------------------------------------------------------------------------
# URL path patterns that hint at content type
URL_CONTENT_PATTERNS: dict[str, list[str]] = {
"blog": [
r"/blog/",
r"/post/",
r"/article/",
r"/news/",
r"/magazine/",
r"/journal/",
r"/column/",
r"/story/",
r"\d{4}/\d{2}/",
],
"product": [
r"/product/",
r"/item/",
r"/shop/",
r"/store/",
r"/buy/",
r"/p/",
r"/goods/",
r"/catalog/",
],
"service": [
r"/service",
r"/solution",
r"/treatment",
r"/procedure",
r"/pricing",
r"/consultation",
],
"news": [
r"/news/",
r"/press/",
r"/media/",
r"/release/",
r"news\.",
r"press\.",
],
"video": [
r"youtube\.com/watch",
r"youtu\.be/",
r"vimeo\.com/",
r"/video/",
r"/watch/",
],
"forum": [
r"/forum/",
r"/community/",
r"/discuss",
r"/thread/",
r"/question/",
r"/answers/",
],
"wiki": [
r"wikipedia\.org",
r"/wiki/",
r"namu\.wiki",
],
}
# Title keywords that hint at content type
TITLE_CONTENT_PATTERNS: dict[str, list[str]] = {
"blog": ["블로그", "후기", "리뷰", "review", "guide", "가이드", "팁", "tips"],
"product": ["구매", "가격", "buy", "price", "shop", "할인", "sale", "최저가"],
"service": ["상담", "치료", "진료", "병원", "클리닉", "clinic", "treatment"],
"news": ["뉴스", "속보", "보도", "news", "기사", "report"],
"video": ["영상", "동영상", "video", "youtube"],
"comparison": ["비교", "vs", "versus", "compare", "차이", "best"],
}
# CTR distribution by position (approximate click-through rates)
CTR_BY_POSITION: dict[int, float] = {
1: 0.316,
2: 0.158,
3: 0.110,
4: 0.080,
5: 0.062,
6: 0.049,
7: 0.040,
8: 0.034,
9: 0.029,
10: 0.025,
}
# ---------------------------------------------------------------------------
# SERP Analyzer
# ---------------------------------------------------------------------------
class SerpAnalyzer:
"""Analyzes Google SERP features, competitor positions, and opportunities."""
def __init__(self):
self.logger = logging.getLogger(self.__class__.__name__)
# ----- Data Fetching -----
def get_serp_data(self, keyword: str, country: str = "us") -> dict[str, Any]:
"""
Fetch SERP data via Ahrefs serp-overview MCP tool.
Uses subprocess to invoke the Ahrefs MCP tool. Falls back to a
structured placeholder when the MCP tool is unavailable (e.g., in
standalone / CI environments).
"""
self.logger.info(f"Fetching SERP data for '{keyword}' (country={country})")
try:
# Attempt MCP tool call via subprocess
cmd = [
"claude",
"mcp",
"call",
"ahrefs",
"serp-overview",
json.dumps({"keyword": keyword, "country": country}),
]
result = subprocess.run(
cmd,
capture_output=True,
text=True,
timeout=60,
)
if result.returncode == 0 and result.stdout.strip():
data = json.loads(result.stdout)
self.logger.info("Successfully fetched SERP data via MCP")
return data
except (subprocess.TimeoutExpired, FileNotFoundError, json.JSONDecodeError) as exc:
self.logger.warning(f"MCP call unavailable ({exc}), using keyword metrics fallback")
# Fallback: try Ahrefs keywords-explorer-overview
try:
cmd_kw = [
"claude",
"mcp",
"call",
"ahrefs",
"keywords-explorer-overview",
json.dumps({"keyword": keyword, "country": country}),
]
result_kw = subprocess.run(
cmd_kw,
capture_output=True,
text=True,
timeout=60,
)
if result_kw.returncode == 0 and result_kw.stdout.strip():
data = json.loads(result_kw.stdout)
self.logger.info("Fetched keyword overview via MCP")
return data
except (subprocess.TimeoutExpired, FileNotFoundError, json.JSONDecodeError) as exc:
self.logger.warning(f"Keywords-explorer MCP also unavailable ({exc})")
# Return empty structure when no MCP tools available
self.logger.warning(
"No MCP data source available. Run inside Claude Desktop "
"or provide data via --input flag."
)
return {
"keyword": keyword,
"country": country,
"serp": [],
"serp_features": {},
"metrics": {},
}
# ----- Feature Detection -----
def detect_features(self, serp_data: dict[str, Any]) -> SerpFeatures:
"""
Identify SERP features from Ahrefs response data.
Handles both the structured 'serp_features' dict returned by
keywords-explorer-overview and the raw SERP items list from
serp-overview.
"""
features = SerpFeatures()
# -- Method 1: structured serp_features from Ahrefs --
sf = serp_data.get("serp_features", {})
if isinstance(sf, dict):
features.featured_snippet = sf.get("featured_snippet", False)
features.people_also_ask = sf.get("people_also_ask", False)
features.local_pack = sf.get("local_pack", False)
features.knowledge_panel = sf.get("knowledge_panel", False) or sf.get(
"knowledge_graph", False
)
features.video_carousel = sf.get("video", False) or sf.get(
"video_carousel", False
)
features.image_pack = sf.get("image_pack", False) or sf.get(
"images", False
)
features.site_links = sf.get("sitelinks", False) or sf.get(
"site_links", False
)
features.shopping = sf.get("shopping_results", False) or sf.get(
"shopping", False
)
features.ads_top = int(sf.get("ads_top", 0) or 0)
features.ads_bottom = int(sf.get("ads_bottom", 0) or 0)
# -- Method 2: infer from raw SERP items list --
serp_items = serp_data.get("serp", [])
if isinstance(serp_items, list):
for item in serp_items:
item_type = str(item.get("type", "")).lower()
if "featured_snippet" in item_type or item.get("is_featured"):
features.featured_snippet = True
if "people_also_ask" in item_type or "paa" in item_type:
features.people_also_ask = True
if "local" in item_type or "map" in item_type:
features.local_pack = True
if "knowledge" in item_type:
features.knowledge_panel = True
if "video" in item_type:
features.video_carousel = True
if "image" in item_type:
features.image_pack = True
if item.get("sitelinks"):
features.site_links = True
if "shopping" in item_type:
features.shopping = True
if "ad" in item_type:
pos = item.get("position", 0)
if pos <= 4:
features.ads_top += 1
else:
features.ads_bottom += 1
return features
# ----- Competitor Mapping -----
def map_competitors(self, serp_data: dict[str, Any]) -> list[CompetitorPosition]:
"""Extract competitor positions and domains from SERP data."""
competitors: list[CompetitorPosition] = []
serp_items = serp_data.get("serp", [])
if not isinstance(serp_items, list):
return competitors
for item in serp_items:
url = item.get("url", "")
if not url:
continue
# Skip ads for organic mapping
item_type = str(item.get("type", "")).lower()
if "ad" in item_type:
continue
parsed = urlparse(url)
domain = parsed.netloc.replace("www.", "")
position = int(item.get("position", len(competitors) + 1))
title = item.get("title", "")
content_type = self.classify_content_type(item)
traffic_share = CTR_BY_POSITION.get(position, 0.01)
comp = CompetitorPosition(
position=position,
url=url,
domain=domain,
title=title,
content_type=content_type,
is_featured=bool(item.get("is_featured")),
has_sitelinks=bool(item.get("sitelinks")),
estimated_traffic_share=round(traffic_share, 4),
)
competitors.append(comp)
# Sort by position
competitors.sort(key=lambda c: c.position)
return competitors
# ----- Content Type Classification -----
def classify_content_type(self, result: dict[str, Any]) -> str:
"""
Classify a SERP result as blog/product/service/news/video/forum/wiki
based on URL patterns and title keywords.
"""
url = result.get("url", "").lower()
title = result.get("title", "").lower()
scores: dict[str, int] = {}
# Score from URL patterns
for ctype, patterns in URL_CONTENT_PATTERNS.items():
for pattern in patterns:
if re.search(pattern, url):
scores[ctype] = scores.get(ctype, 0) + 2
break
# Score from title patterns
for ctype, keywords in TITLE_CONTENT_PATTERNS.items():
for kw in keywords:
if kw.lower() in title:
scores[ctype] = scores.get(ctype, 0) + 1
if not scores:
# Heuristic: if domain is a known authority site
parsed = urlparse(url)
domain = parsed.netloc.lower()
if any(d in domain for d in ["wikipedia", "namu.wiki", "나무위키"]):
return "wiki"
if any(d in domain for d in ["youtube", "vimeo"]):
return "video"
if any(d in domain for d in ["naver.com", "tistory.com", "brunch.co.kr"]):
return "blog"
return "service_page"
# Return highest scoring type
return max(scores, key=scores.get) # type: ignore[arg-type]
# ----- Opportunity Scoring -----
def calculate_opportunity_score(
self,
features: SerpFeatures,
positions: list[CompetitorPosition],
) -> int:
"""
Score SERP opportunity from 0-100.
Higher scores indicate better opportunity to rank or gain features.
Factors (additive):
- Featured snippet available but could be captured +15
- PAA present (related question opportunity) +10
- No knowledge panel (less SERP real-estate taken) +10
- Low ad count (more organic visibility) +10
- Few sitelinks in top results +5
- Content diversity (various domains in top 10) +10
- No video carousel (opportunity to add video) +5
- Top results are blogs (easier to outrank) +10
- Image pack absent (image SEO opportunity) +5
- Shopping absent for commercial keywords +5
- Top positions lacking schema/rich results +5
Penalty factors (subtractive):
- Knowledge panel dominates -15
- Heavy ad presence (4+ top ads) -10
- Single domain dominates top 5 -10
"""
score = 50 # Base score
# -- Positive signals --
if features.featured_snippet:
score += 15
if features.people_also_ask:
score += 10
if not features.knowledge_panel:
score += 10
if features.ads_top <= 1:
score += 10
elif features.ads_top <= 2:
score += 5
if not features.video_carousel:
score += 5
if not features.image_pack:
score += 5
if not features.shopping:
score += 5
# Domain diversity in top 10
if positions:
top10_domains = {p.domain for p in positions[:10]}
if len(top10_domains) >= 8:
score += 10
elif len(top10_domains) >= 5:
score += 5
# Blog-heavy top results (easier to compete)
blog_count = sum(
1 for p in positions[:5] if p.content_type == "blog"
)
if blog_count >= 3:
score += 10
elif blog_count >= 2:
score += 5
# Sitelinks reduce available space
sitelink_count = sum(1 for p in positions[:5] if p.has_sitelinks)
if sitelink_count <= 1:
score += 5
# Single domain dominance penalty
domain_counts: dict[str, int] = {}
for p in positions[:5]:
domain_counts[p.domain] = domain_counts.get(p.domain, 0) + 1
if any(c >= 3 for c in domain_counts.values()):
score -= 10
# -- Negative signals --
if features.knowledge_panel:
score -= 15
if features.ads_top >= 4:
score -= 10
elif features.ads_top >= 3:
score -= 5
# Clamp to 0-100
return max(0, min(100, score))
# ----- Intent Validation -----
def validate_intent(
self,
features: SerpFeatures,
positions: list[CompetitorPosition],
) -> str:
"""
Infer search intent from SERP composition.
Returns one of: informational, navigational, commercial, transactional, local
"""
signals: dict[str, int] = {
"informational": 0,
"navigational": 0,
"commercial": 0,
"transactional": 0,
"local": 0,
}
# Feature-based signals
if features.featured_snippet:
signals["informational"] += 3
if features.people_also_ask:
signals["informational"] += 2
if features.knowledge_panel:
signals["informational"] += 2
signals["navigational"] += 2
if features.local_pack:
signals["local"] += 5
if features.shopping:
signals["transactional"] += 4
if features.has_ads:
signals["commercial"] += 2
signals["transactional"] += 1
if features.ads_top >= 3:
signals["transactional"] += 2
if features.image_pack:
signals["informational"] += 1
if features.video_carousel:
signals["informational"] += 1
# Content type signals from top results
for pos in positions[:10]:
ct = pos.content_type
if ct == "blog":
signals["informational"] += 1
elif ct == "product":
signals["transactional"] += 2
elif ct == "service":
signals["commercial"] += 1
elif ct == "news":
signals["informational"] += 1
elif ct == "video":
signals["informational"] += 1
elif ct == "wiki":
signals["informational"] += 2
elif ct == "forum":
signals["informational"] += 1
elif ct == "comparison":
signals["commercial"] += 2
# Navigational: single domain dominates top 3
if positions:
top3_domains = [p.domain for p in positions[:3]]
if len(set(top3_domains)) == 1:
signals["navigational"] += 5
# Return highest signal
return max(signals, key=signals.get) # type: ignore[arg-type]
# ----- Content Type Distribution -----
def _content_type_distribution(
self, positions: list[CompetitorPosition]
) -> dict[str, int]:
"""Count content types across top organic results."""
dist: dict[str, int] = {}
for p in positions[:10]:
dist[p.content_type] = dist.get(p.content_type, 0) + 1
return dict(sorted(dist.items(), key=lambda x: x[1], reverse=True))
# ----- Volatility Assessment -----
def _assess_volatility(self, serp_data: dict[str, Any]) -> str:
"""
Assess SERP volatility based on available signals.
Returns: stable, moderate, volatile
"""
# Check if Ahrefs provides a volatility/movement score
metrics = serp_data.get("metrics", {})
if isinstance(metrics, dict):
volatility_score = metrics.get("serp_volatility", None)
if volatility_score is not None:
if volatility_score < 3:
return "stable"
elif volatility_score < 7:
return "moderate"
else:
return "volatile"
# Heuristic: if many results have recent dates, SERP is more volatile
serp_items = serp_data.get("serp", [])
if isinstance(serp_items, list) and serp_items:
recent_count = 0
for item in serp_items[:10]:
last_seen = item.get("last_seen", "")
if last_seen:
try:
dt = datetime.fromisoformat(last_seen.replace("Z", "+00:00"))
if (datetime.now(dt.tzinfo) - dt).days < 30:
recent_count += 1
except (ValueError, TypeError):
pass
if recent_count >= 5:
return "volatile"
elif recent_count >= 3:
return "moderate"
return "stable"
# ----- Main Analysis Orchestrator -----
def analyze(self, keyword: str, country: str = "us") -> SerpResult:
"""
Orchestrate full SERP analysis for a single keyword.
Steps:
1. Fetch SERP data from Ahrefs MCP
2. Detect SERP features
3. Map competitor positions
4. Classify content types
5. Calculate opportunity score
6. Validate search intent
7. Assess volatility
"""
serp_data = self.get_serp_data(keyword, country)
features = self.detect_features(serp_data)
positions = self.map_competitors(serp_data)
opportunity = self.calculate_opportunity_score(features, positions)
intent = self.validate_intent(features, positions)
content_dist = self._content_type_distribution(positions)
volatility = self._assess_volatility(serp_data)
# Extract keyword metrics if available
metrics = serp_data.get("metrics", {})
search_volume = int(metrics.get("search_volume", 0) or 0)
keyword_difficulty = float(metrics.get("keyword_difficulty", 0) or 0)
cpc = float(metrics.get("cpc", 0) or 0)
result = SerpResult(
keyword=keyword,
country=country,
search_volume=search_volume,
keyword_difficulty=keyword_difficulty,
cpc=cpc,
serp_features=features,
competitors=positions,
opportunity_score=opportunity,
intent_signals=intent,
content_type_distribution=content_dist,
volatility=volatility,
)
return result
# ---------------------------------------------------------------------------
# Output Helpers
# ---------------------------------------------------------------------------
def result_to_dict(result: SerpResult) -> dict[str, Any]:
"""Convert SerpResult to a JSON-serializable dictionary."""
d = asdict(result)
return d
def print_rich_report(result: SerpResult) -> None:
"""Print a human-readable report using rich."""
console.rule(f"[bold blue]SERP Analysis: {result.keyword}")
console.print(f"[dim]Country: {result.country} | Timestamp: {result.timestamp}[/dim]")
console.print()
# Metrics
if result.search_volume or result.keyword_difficulty:
metrics_table = Table(title="Keyword Metrics", show_lines=True)
metrics_table.add_column("Metric", style="cyan")
metrics_table.add_column("Value", style="green")
metrics_table.add_row("Search Volume", f"{result.search_volume:,}")
metrics_table.add_row("Keyword Difficulty", f"{result.keyword_difficulty:.1f}")
metrics_table.add_row("CPC", f"${result.cpc:.2f}")
console.print(metrics_table)
console.print()
# SERP Features
feat = result.serp_features
feat_table = Table(title="SERP Features", show_lines=True)
feat_table.add_column("Feature", style="cyan")
feat_table.add_column("Present", style="green")
feat_table.add_row("Featured Snippet", _bool_icon(feat.featured_snippet))
feat_table.add_row("People Also Ask", _bool_icon(feat.people_also_ask))
feat_table.add_row("Local Pack", _bool_icon(feat.local_pack))
feat_table.add_row("Knowledge Panel", _bool_icon(feat.knowledge_panel))
feat_table.add_row("Video Carousel", _bool_icon(feat.video_carousel))
feat_table.add_row("Image Pack", _bool_icon(feat.image_pack))
feat_table.add_row("Site Links", _bool_icon(feat.site_links))
feat_table.add_row("Shopping", _bool_icon(feat.shopping))
feat_table.add_row("Ads (top)", str(feat.ads_top))
feat_table.add_row("Ads (bottom)", str(feat.ads_bottom))
console.print(feat_table)
console.print()
# Competitors
if result.competitors:
comp_table = Table(title="Top Competitors", show_lines=True)
comp_table.add_column("#", style="bold")
comp_table.add_column("Domain", style="cyan")
comp_table.add_column("Type", style="magenta")
comp_table.add_column("CTR Share", style="green")
comp_table.add_column("Featured", style="yellow")
for c in result.competitors[:10]:
comp_table.add_row(
str(c.position),
c.domain,
c.content_type,
f"{c.estimated_traffic_share:.1%}",
_bool_icon(c.is_featured),
)
console.print(comp_table)
console.print()
# Content Distribution
if result.content_type_distribution:
dist_table = Table(title="Content Type Distribution (Top 10)", show_lines=True)
dist_table.add_column("Content Type", style="cyan")
dist_table.add_column("Count", style="green")
for ct, count in result.content_type_distribution.items():
dist_table.add_row(ct, str(count))
console.print(dist_table)
console.print()
# Summary
opp_color = "green" if result.opportunity_score >= 60 else (
"yellow" if result.opportunity_score >= 40 else "red"
)
console.print(f"Opportunity Score: [{opp_color}]{result.opportunity_score}/100[/{opp_color}]")
console.print(f"Search Intent: [bold]{result.intent_signals}[/bold]")
console.print(f"SERP Volatility: [bold]{result.volatility}[/bold]")
console.rule()
def _bool_icon(val: bool) -> str:
"""Return Yes/No string for boolean values."""
return "Yes" if val else "No"
# ---------------------------------------------------------------------------
# CLI
# ---------------------------------------------------------------------------
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
description="Google SERP feature detection and competitor mapping",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
python serp_analyzer.py --keyword "치과 임플란트" --country kr --json
python serp_analyzer.py --keywords-file keywords.txt --country kr --output report.json
""",
)
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument(
"--keyword",
type=str,
help="Single keyword to analyze",
)
group.add_argument(
"--keywords-file",
type=str,
help="Path to file with one keyword per line",
)
parser.add_argument(
"--country",
type=str,
default="us",
help="Country code for SERP (default: us)",
)
parser.add_argument(
"--json",
action="store_true",
dest="json_output",
help="Output results as JSON",
)
parser.add_argument(
"--output",
type=str,
help="Write JSON results to file",
)
return parser
def load_keywords(filepath: str) -> list[str]:
"""Load keywords from a text file, one per line."""
path = Path(filepath)
if not path.exists():
logger.error(f"Keywords file not found: {filepath}")
sys.exit(1)
keywords = []
with open(path, "r", encoding="utf-8") as fh:
for line in fh:
kw = line.strip()
if kw and not kw.startswith("#"):
keywords.append(kw)
logger.info(f"Loaded {len(keywords)} keywords from {filepath}")
return keywords
def main() -> None:
parser = build_parser()
args = parser.parse_args()
analyzer = SerpAnalyzer()
# Collect keywords
if args.keyword:
keywords = [args.keyword]
else:
keywords = load_keywords(args.keywords_file)
if not keywords:
logger.error("No keywords to analyze")
sys.exit(1)
results: list[dict[str, Any]] = []
for kw in keywords:
console.print(f"\n[bold]Analyzing:[/bold] {kw}")
result = analyzer.analyze(kw, args.country)
if args.json_output or args.output:
results.append(result_to_dict(result))
else:
print_rich_report(result)
# JSON output
if args.json_output:
output_data = results[0] if len(results) == 1 else results
print(json.dumps(output_data, ensure_ascii=False, indent=2))
if args.output:
output_data = results[0] if len(results) == 1 else results
output_path = Path(args.output)
with open(output_path, "w", encoding="utf-8") as fh:
json.dump(output_data, fh, ensure_ascii=False, indent=2)
logger.info(f"Results written to {output_path}")
if __name__ == "__main__":
main()

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---
name: seo-serp-analysis
description: |
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, VIEW tab), 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, VIEW tab, news, encyclopedia)
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
```json
{
"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

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# Skill metadata (extracted from SKILL.md frontmatter)
name: seo-serp-analysis
description: |
SERP analysis for Google and Naver. Triggers: SERP analysis, search results, featured snippet, SERP features, Naver SERP.
# Optional fields
allowed-tools:
- mcp__ahrefs__*
- mcp__notion__*
- WebSearch
- WebFetch
# triggers: [] # TODO: Extract from description

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# Ahrefs
> TODO: Document tool usage for this skill
## Available Commands
- [ ] List commands
## Configuration
- [ ] Add configuration details
## Examples
- [ ] Add usage examples

15
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# Notion
> TODO: Document tool usage for this skill
## Available Commands
- [ ] List commands
## Configuration
- [ ] Add configuration details
## Examples
- [ ] Add usage examples

15
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# WebSearch
> TODO: Document tool usage for this skill
## Available Commands
- [ ] List commands
## Configuration
- [ ] Add configuration details
## Examples
- [ ] Add usage examples