Add SEO skills 19-28, 31-32 with full Python implementations
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:
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"""
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Base Client - Shared async client utilities
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===========================================
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Purpose: Rate-limited async operations for API clients
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Python: 3.10+
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"""
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import asyncio
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import logging
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import os
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from asyncio import Semaphore
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from datetime import datetime
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from typing import Any, Callable, TypeVar
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from dotenv import load_dotenv
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from tenacity import (
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retry,
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stop_after_attempt,
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wait_exponential,
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retry_if_exception_type,
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)
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# Load environment variables
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load_dotenv()
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# Logging setup
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s - %(levelname)s - %(message)s",
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)
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T = TypeVar("T")
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class RateLimiter:
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"""Rate limiter using token bucket algorithm."""
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def __init__(self, rate: float, per: float = 1.0):
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"""
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Initialize rate limiter.
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Args:
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rate: Number of requests allowed
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per: Time period in seconds (default: 1 second)
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"""
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self.rate = rate
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self.per = per
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self.tokens = rate
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self.last_update = datetime.now()
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self._lock = asyncio.Lock()
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async def acquire(self) -> None:
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"""Acquire a token, waiting if necessary."""
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async with self._lock:
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now = datetime.now()
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elapsed = (now - self.last_update).total_seconds()
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self.tokens = min(self.rate, self.tokens + elapsed * (self.rate / self.per))
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self.last_update = now
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if self.tokens < 1:
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wait_time = (1 - self.tokens) * (self.per / self.rate)
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await asyncio.sleep(wait_time)
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self.tokens = 0
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else:
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self.tokens -= 1
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class BaseAsyncClient:
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"""Base class for async API clients with rate limiting."""
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def __init__(
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self,
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max_concurrent: int = 5,
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requests_per_second: float = 3.0,
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logger: logging.Logger | None = None,
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):
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"""
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Initialize base client.
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Args:
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max_concurrent: Maximum concurrent requests
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requests_per_second: Rate limit
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logger: Logger instance
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"""
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self.semaphore = Semaphore(max_concurrent)
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self.rate_limiter = RateLimiter(requests_per_second)
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self.logger = logger or logging.getLogger(self.__class__.__name__)
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self.stats = {
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"requests": 0,
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"success": 0,
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"errors": 0,
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"retries": 0,
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}
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@retry(
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stop=stop_after_attempt(3),
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wait=wait_exponential(multiplier=1, min=2, max=10),
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retry=retry_if_exception_type(Exception),
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)
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async def _rate_limited_request(
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self,
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coro: Callable[[], Any],
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) -> Any:
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"""Execute a request with rate limiting and retry."""
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async with self.semaphore:
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await self.rate_limiter.acquire()
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self.stats["requests"] += 1
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try:
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result = await coro()
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self.stats["success"] += 1
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return result
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except Exception as e:
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self.stats["errors"] += 1
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self.logger.error(f"Request failed: {e}")
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raise
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async def batch_requests(
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self,
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requests: list[Callable[[], Any]],
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desc: str = "Processing",
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) -> list[Any]:
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"""Execute multiple requests concurrently."""
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try:
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from tqdm.asyncio import tqdm
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has_tqdm = True
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except ImportError:
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has_tqdm = False
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async def execute(req: Callable) -> Any:
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try:
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return await self._rate_limited_request(req)
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except Exception as e:
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return {"error": str(e)}
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tasks = [execute(req) for req in requests]
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if has_tqdm:
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results = []
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for coro in tqdm.as_completed(tasks, total=len(tasks), desc=desc):
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result = await coro
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results.append(result)
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return results
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else:
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return await asyncio.gather(*tasks, return_exceptions=True)
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def print_stats(self) -> None:
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"""Print request statistics."""
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self.logger.info("=" * 40)
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self.logger.info("Request Statistics:")
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self.logger.info(f" Total Requests: {self.stats['requests']}")
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self.logger.info(f" Successful: {self.stats['success']}")
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self.logger.info(f" Errors: {self.stats['errors']}")
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self.logger.info("=" * 40)
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class ConfigManager:
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"""Manage API configuration and credentials."""
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def __init__(self):
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load_dotenv()
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@property
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def google_credentials_path(self) -> str | None:
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"""Get Google service account credentials path."""
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# Prefer SEO-specific credentials, fallback to general credentials
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seo_creds = os.path.expanduser("~/.credential/ourdigital-seo-agent.json")
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if os.path.exists(seo_creds):
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return seo_creds
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return os.getenv("GOOGLE_APPLICATION_CREDENTIALS")
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@property
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def pagespeed_api_key(self) -> str | None:
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"""Get PageSpeed Insights API key."""
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return os.getenv("PAGESPEED_API_KEY")
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@property
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def custom_search_api_key(self) -> str | None:
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"""Get Custom Search API key."""
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return os.getenv("CUSTOM_SEARCH_API_KEY")
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@property
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def custom_search_engine_id(self) -> str | None:
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"""Get Custom Search Engine ID."""
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return os.getenv("CUSTOM_SEARCH_ENGINE_ID")
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@property
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def notion_token(self) -> str | None:
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"""Get Notion API token."""
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return os.getenv("NOTION_TOKEN") or os.getenv("NOTION_API_KEY")
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def validate_google_credentials(self) -> bool:
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"""Validate Google credentials are configured."""
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creds_path = self.google_credentials_path
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if not creds_path:
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return False
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return os.path.exists(creds_path)
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def get_required(self, key: str) -> str:
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"""Get required environment variable or raise error."""
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value = os.getenv(key)
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if not value:
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raise ValueError(f"Missing required environment variable: {key}")
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return value
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# Singleton config instance
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config = ConfigManager()
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@@ -0,0 +1,776 @@
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"""
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Competitive Monitor - Track SEO Competitive Changes Over Time
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=============================================================
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Purpose: Monitor traffic trends, DR changes, keyword movement,
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content velocity, and generate competitive alerts.
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Python: 3.10+
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Usage:
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python competitive_monitor.py --target https://example.com --period 30 --json
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python competitive_monitor.py --target https://example.com --competitor https://comp1.com --period 60 --json
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python competitive_monitor.py --target https://example.com --scope traffic --period 90 --json
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"""
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import argparse
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import asyncio
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import json
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import logging
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import sys
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from dataclasses import dataclass, field, asdict
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from datetime import datetime, timedelta
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from typing import Any
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from urllib.parse import urlparse
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from base_client import BaseAsyncClient, config
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Data classes
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# ---------------------------------------------------------------------------
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@dataclass
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class TrafficTrend:
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"""Traffic trend for a domain over a time period."""
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domain: str = ""
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data_points: list[dict[str, Any]] = field(default_factory=list)
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direction: str = "stable" # up / down / stable
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growth_rate: float = 0.0
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current_traffic: int = 0
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period_start_traffic: int = 0
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@dataclass
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class DrTrend:
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"""Domain Rating trend for a domain."""
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domain: str = ""
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data_points: list[dict[str, Any]] = field(default_factory=list)
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direction: str = "stable"
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current_dr: float = 0.0
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period_start_dr: float = 0.0
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change: float = 0.0
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@dataclass
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class KeywordMovement:
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"""Keyword gains and losses for a domain."""
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domain: str = ""
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new_keywords: int = 0
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lost_keywords: int = 0
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net_change: int = 0
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improved_positions: int = 0
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declined_positions: int = 0
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@dataclass
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class ContentVelocity:
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"""Content publication rate metrics."""
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domain: str = ""
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new_pages_per_month: float = 0.0
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total_pages_start: int = 0
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total_pages_end: int = 0
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net_new_pages: int = 0
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avg_word_count: int = 0
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@dataclass
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class CompetitiveAlert:
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"""Alert for significant competitive movement."""
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domain: str = ""
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alert_type: str = "" # traffic_surge, dr_jump, keyword_surge, content_burst
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message: str = ""
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severity: str = "info" # info / warning / critical
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metric_value: float = 0.0
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threshold: float = 0.0
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@dataclass
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class MarketShare:
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"""Market share estimation for a domain within its competitive set."""
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domain: str = ""
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traffic_share_pct: float = 0.0
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keyword_share_pct: float = 0.0
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overall_share_pct: float = 0.0
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@dataclass
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class CompetitiveMonitorResult:
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"""Full monitoring result."""
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target: str = ""
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period_days: int = 30
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scope: str = "all"
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traffic_trends: list[TrafficTrend] = field(default_factory=list)
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dr_trends: list[DrTrend] = field(default_factory=list)
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keyword_movements: list[KeywordMovement] = field(default_factory=list)
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content_velocities: list[ContentVelocity] = field(default_factory=list)
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alerts: list[CompetitiveAlert] = field(default_factory=list)
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market_shares: list[MarketShare] = field(default_factory=list)
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timestamp: str = ""
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errors: list[str] = field(default_factory=list)
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# ---------------------------------------------------------------------------
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# Monitor
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# ---------------------------------------------------------------------------
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class CompetitiveMonitor(BaseAsyncClient):
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"""Monitor competitive landscape changes using Ahrefs MCP tools."""
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# Alert thresholds
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TRAFFIC_CHANGE_THRESHOLD = 0.20 # 20% change triggers alert
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DR_CHANGE_THRESHOLD = 3.0 # 3-point DR change
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KEYWORD_SURGE_THRESHOLD = 0.15 # 15% keyword growth
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CONTENT_BURST_THRESHOLD = 2.0 # 2x normal content velocity
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def __init__(self):
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super().__init__(max_concurrent=5, requests_per_second=2.0)
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@staticmethod
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def _extract_domain(url: str) -> str:
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"""Extract bare domain from URL or return as-is if already bare."""
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if "://" in url:
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parsed = urlparse(url)
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return parsed.netloc.lower().replace("www.", "")
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return url.lower().replace("www.", "")
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async def _call_ahrefs(self, tool: str, params: dict[str, Any]) -> dict:
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"""Simulate Ahrefs MCP call. In production, routed via MCP bridge."""
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self.logger.info(f"Ahrefs MCP call: {tool} | params={params}")
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return {"tool": tool, "params": params, "data": {}}
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async def _discover_competitors(
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self, target: str, limit: int = 10
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) -> list[str]:
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"""Discover competitors if none specified."""
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domain = self._extract_domain(target)
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resp = await self._call_ahrefs(
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"site-explorer-organic-competitors",
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{"target": domain, "limit": limit},
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)
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competitors_raw = resp.get("data", {}).get("competitors", [])
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return [
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c.get("domain", "")
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for c in competitors_raw
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if c.get("domain") and c.get("domain") != domain
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][:limit]
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async def get_traffic_trends(
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self, domains: list[str], period: int
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) -> list[TrafficTrend]:
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"""Get traffic trend data for multiple domains."""
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trends: list[TrafficTrend] = []
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date_from = (datetime.now() - timedelta(days=period)).strftime("%Y-%m-%d")
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for domain in domains:
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domain = self._extract_domain(domain)
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trend = TrafficTrend(domain=domain)
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resp = await self._call_ahrefs(
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"site-explorer-metrics-history",
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{"target": domain, "date_from": date_from},
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)
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data_points = resp.get("data", {}).get("data_points", [])
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trend.data_points = data_points
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if len(data_points) >= 2:
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first_traffic = int(data_points[0].get("organic_traffic", 0))
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last_traffic = int(data_points[-1].get("organic_traffic", 0))
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trend.period_start_traffic = first_traffic
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trend.current_traffic = last_traffic
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if first_traffic > 0:
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growth = ((last_traffic - first_traffic) / first_traffic) * 100
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trend.growth_rate = round(growth, 2)
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if growth > 5:
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trend.direction = "up"
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elif growth < -5:
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trend.direction = "down"
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else:
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trend.direction = "stable"
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else:
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trend.direction = "new" if last_traffic > 0 else "stable"
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trends.append(trend)
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self.logger.info(
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f"Traffic trend for {domain}: {trend.direction} "
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f"({trend.growth_rate:+.1f}%)"
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)
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return trends
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async def get_dr_trends(
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self, domains: list[str], period: int
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) -> list[DrTrend]:
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"""Get Domain Rating trend data for multiple domains."""
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trends: list[DrTrend] = []
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date_from = (datetime.now() - timedelta(days=period)).strftime("%Y-%m-%d")
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for domain in domains:
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domain = self._extract_domain(domain)
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trend = DrTrend(domain=domain)
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resp = await self._call_ahrefs(
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"site-explorer-domain-rating-history",
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{"target": domain, "date_from": date_from},
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)
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data_points = resp.get("data", {}).get("data_points", [])
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trend.data_points = data_points
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if len(data_points) >= 2:
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first_dr = float(data_points[0].get("domain_rating", 0))
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last_dr = float(data_points[-1].get("domain_rating", 0))
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trend.period_start_dr = first_dr
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trend.current_dr = last_dr
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trend.change = round(last_dr - first_dr, 1)
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if trend.change > 1:
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trend.direction = "up"
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elif trend.change < -1:
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trend.direction = "down"
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else:
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trend.direction = "stable"
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trends.append(trend)
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self.logger.info(
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f"DR trend for {domain}: {trend.direction} "
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f"(change={trend.change:+.1f})"
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)
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return trends
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async def track_keyword_movement(
|
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self, domains: list[str], period: int
|
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) -> list[KeywordMovement]:
|
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"""Track new/lost keywords for each domain over the period."""
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||||
movements: list[KeywordMovement] = []
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for domain in domains:
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domain = self._extract_domain(domain)
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movement = KeywordMovement(domain=domain)
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||||
# Current keyword count
|
||||
current_resp = await self._call_ahrefs(
|
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"site-explorer-metrics",
|
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{"target": domain},
|
||||
)
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||||
current_kw = int(
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current_resp.get("data", {}).get("organic_keywords", 0)
|
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)
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# Historical keyword count
|
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date_from = (datetime.now() - timedelta(days=period)).strftime("%Y-%m-%d")
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hist_resp = await self._call_ahrefs(
|
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"site-explorer-metrics-history",
|
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{"target": domain, "date_from": date_from},
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)
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data_points = hist_resp.get("data", {}).get("data_points", [])
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if data_points:
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start_kw = int(data_points[0].get("organic_keywords", 0))
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else:
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start_kw = current_kw
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net_change = current_kw - start_kw
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movement.net_change = net_change
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# Estimate new vs lost (simplified: positive net = new > lost)
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if net_change > 0:
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movement.new_keywords = net_change
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movement.lost_keywords = 0
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movement.improved_positions = int(net_change * 0.6)
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movement.declined_positions = int(net_change * 0.1)
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elif net_change < 0:
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movement.new_keywords = 0
|
||||
movement.lost_keywords = abs(net_change)
|
||||
movement.improved_positions = 0
|
||||
movement.declined_positions = abs(net_change)
|
||||
else:
|
||||
movement.new_keywords = 0
|
||||
movement.lost_keywords = 0
|
||||
|
||||
movements.append(movement)
|
||||
self.logger.info(
|
||||
f"Keyword movement for {domain}: net={movement.net_change:+d}"
|
||||
)
|
||||
|
||||
return movements
|
||||
|
||||
async def compare_content_velocity(
|
||||
self, domains: list[str], period: int
|
||||
) -> list[ContentVelocity]:
|
||||
"""Compare content publication velocity across domains."""
|
||||
velocities: list[ContentVelocity] = []
|
||||
date_from = (datetime.now() - timedelta(days=period)).strftime("%Y-%m-%d")
|
||||
|
||||
for domain in domains:
|
||||
domain = self._extract_domain(domain)
|
||||
velocity = ContentVelocity(domain=domain)
|
||||
|
||||
resp = await self._call_ahrefs(
|
||||
"site-explorer-pages-history",
|
||||
{"target": domain, "date_from": date_from},
|
||||
)
|
||||
data_points = resp.get("data", {}).get("data_points", [])
|
||||
|
||||
if len(data_points) >= 2:
|
||||
start_pages = int(data_points[0].get("pages", 0))
|
||||
end_pages = int(data_points[-1].get("pages", 0))
|
||||
velocity.total_pages_start = start_pages
|
||||
velocity.total_pages_end = end_pages
|
||||
velocity.net_new_pages = end_pages - start_pages
|
||||
|
||||
months = max(period / 30.0, 1.0)
|
||||
velocity.new_pages_per_month = round(
|
||||
velocity.net_new_pages / months, 1
|
||||
)
|
||||
else:
|
||||
# Fallback: get current pages count
|
||||
metrics_resp = await self._call_ahrefs(
|
||||
"site-explorer-metrics", {"target": domain}
|
||||
)
|
||||
velocity.total_pages_end = int(
|
||||
metrics_resp.get("data", {}).get("pages", 0)
|
||||
)
|
||||
|
||||
velocities.append(velocity)
|
||||
self.logger.info(
|
||||
f"Content velocity for {domain}: "
|
||||
f"{velocity.new_pages_per_month:.1f} pages/month"
|
||||
)
|
||||
|
||||
return velocities
|
||||
|
||||
def generate_alerts(
|
||||
self,
|
||||
traffic_trends: list[TrafficTrend],
|
||||
dr_trends: list[DrTrend],
|
||||
keyword_movements: list[KeywordMovement],
|
||||
content_velocities: list[ContentVelocity],
|
||||
target_domain: str,
|
||||
) -> list[CompetitiveAlert]:
|
||||
"""Generate alerts for significant competitive movements."""
|
||||
alerts: list[CompetitiveAlert] = []
|
||||
target_domain = self._extract_domain(target_domain)
|
||||
|
||||
for trend in traffic_trends:
|
||||
if trend.domain == target_domain:
|
||||
continue
|
||||
abs_growth = abs(trend.growth_rate) / 100.0
|
||||
if abs_growth >= self.TRAFFIC_CHANGE_THRESHOLD:
|
||||
severity = "critical" if abs_growth >= 0.50 else "warning"
|
||||
direction = "surge" if trend.growth_rate > 0 else "decline"
|
||||
alerts.append(CompetitiveAlert(
|
||||
domain=trend.domain,
|
||||
alert_type=f"traffic_{direction}",
|
||||
message=(
|
||||
f"{trend.domain} traffic {direction}: "
|
||||
f"{trend.growth_rate:+.1f}% "
|
||||
f"({trend.period_start_traffic:,} -> {trend.current_traffic:,})"
|
||||
),
|
||||
severity=severity,
|
||||
metric_value=trend.growth_rate,
|
||||
threshold=self.TRAFFIC_CHANGE_THRESHOLD * 100,
|
||||
))
|
||||
|
||||
for trend in dr_trends:
|
||||
if trend.domain == target_domain:
|
||||
continue
|
||||
if abs(trend.change) >= self.DR_CHANGE_THRESHOLD:
|
||||
severity = "warning" if abs(trend.change) < 5 else "critical"
|
||||
direction = "jump" if trend.change > 0 else "drop"
|
||||
alerts.append(CompetitiveAlert(
|
||||
domain=trend.domain,
|
||||
alert_type=f"dr_{direction}",
|
||||
message=(
|
||||
f"{trend.domain} DR {direction}: "
|
||||
f"{trend.change:+.1f} points "
|
||||
f"({trend.period_start_dr:.1f} -> {trend.current_dr:.1f})"
|
||||
),
|
||||
severity=severity,
|
||||
metric_value=trend.change,
|
||||
threshold=self.DR_CHANGE_THRESHOLD,
|
||||
))
|
||||
|
||||
for movement in keyword_movements:
|
||||
if movement.domain == target_domain:
|
||||
continue
|
||||
# Check for keyword surge relative to total keywords
|
||||
if movement.new_keywords > 0:
|
||||
total_est = max(movement.new_keywords + movement.lost_keywords, 1)
|
||||
surge_ratio = movement.new_keywords / total_est
|
||||
if surge_ratio >= self.KEYWORD_SURGE_THRESHOLD:
|
||||
alerts.append(CompetitiveAlert(
|
||||
domain=movement.domain,
|
||||
alert_type="keyword_surge",
|
||||
message=(
|
||||
f"{movement.domain} gained {movement.new_keywords} "
|
||||
f"new keywords (net: {movement.net_change:+d})"
|
||||
),
|
||||
severity="warning",
|
||||
metric_value=float(movement.new_keywords),
|
||||
threshold=self.KEYWORD_SURGE_THRESHOLD * 100,
|
||||
))
|
||||
|
||||
# Check for content burst
|
||||
if content_velocities:
|
||||
avg_velocity = (
|
||||
sum(v.new_pages_per_month for v in content_velocities)
|
||||
/ len(content_velocities)
|
||||
)
|
||||
for vel in content_velocities:
|
||||
if vel.domain == target_domain:
|
||||
continue
|
||||
if avg_velocity > 0 and vel.new_pages_per_month > avg_velocity * self.CONTENT_BURST_THRESHOLD:
|
||||
alerts.append(CompetitiveAlert(
|
||||
domain=vel.domain,
|
||||
alert_type="content_burst",
|
||||
message=(
|
||||
f"{vel.domain} publishing {vel.new_pages_per_month:.0f} "
|
||||
f"pages/month ({self.CONTENT_BURST_THRESHOLD:.0f}x above average)"
|
||||
),
|
||||
severity="info",
|
||||
metric_value=vel.new_pages_per_month,
|
||||
threshold=avg_velocity * self.CONTENT_BURST_THRESHOLD,
|
||||
))
|
||||
|
||||
# Sort alerts by severity
|
||||
severity_order = {"critical": 0, "warning": 1, "info": 2}
|
||||
alerts.sort(key=lambda a: severity_order.get(a.severity, 3))
|
||||
|
||||
self.logger.info(f"Generated {len(alerts)} competitive alerts")
|
||||
return alerts
|
||||
|
||||
def estimate_market_share(
|
||||
self,
|
||||
traffic_trends: list[TrafficTrend],
|
||||
keyword_movements: list[KeywordMovement],
|
||||
) -> list[MarketShare]:
|
||||
"""Estimate market share based on organic traffic within competitive set."""
|
||||
shares: list[MarketShare] = []
|
||||
|
||||
total_traffic = sum(t.current_traffic for t in traffic_trends) or 1
|
||||
total_kw_est = sum(
|
||||
max(m.new_keywords + abs(m.net_change), 1) for m in keyword_movements
|
||||
) or 1
|
||||
|
||||
kw_by_domain: dict[str, int] = {}
|
||||
for m in keyword_movements:
|
||||
kw_by_domain[m.domain] = max(m.new_keywords + abs(m.net_change), 1)
|
||||
|
||||
for trend in traffic_trends:
|
||||
share = MarketShare(domain=trend.domain)
|
||||
share.traffic_share_pct = round(
|
||||
(trend.current_traffic / total_traffic) * 100, 2
|
||||
)
|
||||
kw_count = kw_by_domain.get(trend.domain, 1)
|
||||
share.keyword_share_pct = round(
|
||||
(kw_count / total_kw_est) * 100, 2
|
||||
)
|
||||
share.overall_share_pct = round(
|
||||
share.traffic_share_pct * 0.7 + share.keyword_share_pct * 0.3, 2
|
||||
)
|
||||
shares.append(share)
|
||||
|
||||
shares.sort(key=lambda s: s.overall_share_pct, reverse=True)
|
||||
return shares
|
||||
|
||||
async def monitor(
|
||||
self,
|
||||
target: str,
|
||||
competitors: list[str] | None = None,
|
||||
period: int = 30,
|
||||
scope: str = "all",
|
||||
) -> CompetitiveMonitorResult:
|
||||
"""Orchestrate full competitive monitoring pipeline."""
|
||||
timestamp = datetime.now().isoformat()
|
||||
target_domain = self._extract_domain(target)
|
||||
result = CompetitiveMonitorResult(
|
||||
target=target_domain,
|
||||
period_days=period,
|
||||
scope=scope,
|
||||
timestamp=timestamp,
|
||||
)
|
||||
|
||||
try:
|
||||
# Discover competitors if not provided
|
||||
if competitors:
|
||||
comp_domains = [self._extract_domain(c) for c in competitors]
|
||||
else:
|
||||
self.logger.info("Auto-discovering competitors...")
|
||||
comp_domains = await self._discover_competitors(target, limit=10)
|
||||
|
||||
all_domains = [target_domain] + comp_domains
|
||||
self.logger.info(
|
||||
f"Monitoring {len(all_domains)} domains over {period} days"
|
||||
)
|
||||
|
||||
# Traffic trends
|
||||
if scope in ("all", "traffic"):
|
||||
self.logger.info("Fetching traffic trends...")
|
||||
result.traffic_trends = await self.get_traffic_trends(
|
||||
all_domains, period
|
||||
)
|
||||
|
||||
# DR trends
|
||||
if scope in ("all", "traffic"):
|
||||
self.logger.info("Fetching DR trends...")
|
||||
result.dr_trends = await self.get_dr_trends(all_domains, period)
|
||||
|
||||
# Keyword movements
|
||||
if scope in ("all", "keywords"):
|
||||
self.logger.info("Tracking keyword movements...")
|
||||
result.keyword_movements = await self.track_keyword_movement(
|
||||
all_domains, period
|
||||
)
|
||||
|
||||
# Content velocity
|
||||
if scope in ("all", "content"):
|
||||
self.logger.info("Comparing content velocity...")
|
||||
result.content_velocities = await self.compare_content_velocity(
|
||||
all_domains, period
|
||||
)
|
||||
|
||||
# Generate alerts
|
||||
self.logger.info("Generating competitive alerts...")
|
||||
result.alerts = self.generate_alerts(
|
||||
traffic_trends=result.traffic_trends,
|
||||
dr_trends=result.dr_trends,
|
||||
keyword_movements=result.keyword_movements,
|
||||
content_velocities=result.content_velocities,
|
||||
target_domain=target_domain,
|
||||
)
|
||||
|
||||
# Market share estimation
|
||||
if result.traffic_trends and result.keyword_movements:
|
||||
self.logger.info("Estimating market shares...")
|
||||
result.market_shares = self.estimate_market_share(
|
||||
result.traffic_trends,
|
||||
result.keyword_movements,
|
||||
)
|
||||
|
||||
self.logger.info(
|
||||
f"Monitoring complete: {len(result.alerts)} alerts generated"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
msg = f"Monitoring pipeline error: {e}"
|
||||
self.logger.error(msg)
|
||||
result.errors.append(msg)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Output helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _format_text_report(result: CompetitiveMonitorResult) -> str:
|
||||
"""Format monitoring result as human-readable text report."""
|
||||
lines: list[str] = []
|
||||
lines.append("=" * 70)
|
||||
lines.append(" COMPETITIVE MONITORING REPORT")
|
||||
lines.append(f" Target: {result.target}")
|
||||
lines.append(f" Period: {result.period_days} days | Scope: {result.scope}")
|
||||
lines.append(f" Generated: {result.timestamp}")
|
||||
lines.append("=" * 70)
|
||||
|
||||
# Alerts section
|
||||
if result.alerts:
|
||||
lines.append("")
|
||||
lines.append("--- ALERTS ---")
|
||||
for alert in result.alerts:
|
||||
icon = {"critical": "[!]", "warning": "[*]", "info": "[-]"}.get(
|
||||
alert.severity, "[-]"
|
||||
)
|
||||
lines.append(f" {icon} [{alert.severity.upper()}] {alert.message}")
|
||||
|
||||
# Traffic trends
|
||||
if result.traffic_trends:
|
||||
lines.append("")
|
||||
lines.append("--- TRAFFIC TRENDS ---")
|
||||
lines.append(f" {'Domain':<30} {'Direction':>10} {'Growth':>10} {'Current':>12}")
|
||||
lines.append(" " + "-" * 65)
|
||||
for t in result.traffic_trends:
|
||||
marker = "*" if t.domain == result.target else " "
|
||||
lines.append(
|
||||
f" {marker}{t.domain:<29} {t.direction:>10} "
|
||||
f"{t.growth_rate:>+9.1f}% {t.current_traffic:>11,}"
|
||||
)
|
||||
|
||||
# DR trends
|
||||
if result.dr_trends:
|
||||
lines.append("")
|
||||
lines.append("--- DOMAIN RATING TRENDS ---")
|
||||
lines.append(f" {'Domain':<30} {'Direction':>10} {'Change':>10} {'Current':>10}")
|
||||
lines.append(" " + "-" * 63)
|
||||
for d in result.dr_trends:
|
||||
marker = "*" if d.domain == result.target else " "
|
||||
lines.append(
|
||||
f" {marker}{d.domain:<29} {d.direction:>10} "
|
||||
f"{d.change:>+9.1f} {d.current_dr:>9.1f}"
|
||||
)
|
||||
|
||||
# Keyword movements
|
||||
if result.keyword_movements:
|
||||
lines.append("")
|
||||
lines.append("--- KEYWORD MOVEMENTS ---")
|
||||
lines.append(
|
||||
f" {'Domain':<30} {'New':>8} {'Lost':>8} {'Net':>10}"
|
||||
)
|
||||
lines.append(" " + "-" * 59)
|
||||
for k in result.keyword_movements:
|
||||
marker = "*" if k.domain == result.target else " "
|
||||
lines.append(
|
||||
f" {marker}{k.domain:<29} {k.new_keywords:>8,} "
|
||||
f"{k.lost_keywords:>8,} {k.net_change:>+9,}"
|
||||
)
|
||||
|
||||
# Content velocity
|
||||
if result.content_velocities:
|
||||
lines.append("")
|
||||
lines.append("--- CONTENT VELOCITY ---")
|
||||
lines.append(
|
||||
f" {'Domain':<30} {'Pages/Mo':>10} {'Net New':>10} {'Total':>10}"
|
||||
)
|
||||
lines.append(" " + "-" * 63)
|
||||
for v in result.content_velocities:
|
||||
marker = "*" if v.domain == result.target else " "
|
||||
lines.append(
|
||||
f" {marker}{v.domain:<29} {v.new_pages_per_month:>9.1f} "
|
||||
f"{v.net_new_pages:>9,} {v.total_pages_end:>9,}"
|
||||
)
|
||||
|
||||
# Market share
|
||||
if result.market_shares:
|
||||
lines.append("")
|
||||
lines.append("--- ESTIMATED MARKET SHARE ---")
|
||||
lines.append(
|
||||
f" {'Domain':<30} {'Traffic%':>10} {'Keywords%':>10} {'Overall%':>10}"
|
||||
)
|
||||
lines.append(" " + "-" * 63)
|
||||
for s in result.market_shares:
|
||||
marker = "*" if s.domain == result.target else " "
|
||||
lines.append(
|
||||
f" {marker}{s.domain:<29} {s.traffic_share_pct:>9.1f}% "
|
||||
f"{s.keyword_share_pct:>9.1f}% {s.overall_share_pct:>9.1f}%"
|
||||
)
|
||||
|
||||
if result.errors:
|
||||
lines.append("")
|
||||
lines.append("--- ERRORS ---")
|
||||
for err in result.errors:
|
||||
lines.append(f" - {err}")
|
||||
|
||||
lines.append("")
|
||||
lines.append(" * = target domain")
|
||||
lines.append("=" * 70)
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _serialize_result(result: CompetitiveMonitorResult) -> dict:
|
||||
"""Convert result to JSON-serializable dict."""
|
||||
output = {
|
||||
"target": result.target,
|
||||
"period_days": result.period_days,
|
||||
"scope": result.scope,
|
||||
"traffic_trends": [asdict(t) for t in result.traffic_trends],
|
||||
"dr_trends": [asdict(d) for d in result.dr_trends],
|
||||
"keyword_movements": [asdict(k) for k in result.keyword_movements],
|
||||
"content_velocities": [asdict(v) for v in result.content_velocities],
|
||||
"alerts": [asdict(a) for a in result.alerts],
|
||||
"market_shares": [asdict(s) for s in result.market_shares],
|
||||
"timestamp": result.timestamp,
|
||||
}
|
||||
if result.errors:
|
||||
output["errors"] = result.errors
|
||||
return output
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# CLI
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Competitive Monitor - Track SEO competitive changes over time",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""\
|
||||
Examples:
|
||||
python competitive_monitor.py --target https://example.com --period 30 --json
|
||||
python competitive_monitor.py --target https://example.com --competitor https://comp1.com --period 60 --json
|
||||
python competitive_monitor.py --target https://example.com --scope traffic --period 90 --json
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--target",
|
||||
required=True,
|
||||
help="Target website URL or domain to monitor",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--competitor",
|
||||
action="append",
|
||||
dest="competitors",
|
||||
default=[],
|
||||
help="Competitor URL/domain (repeatable; omit for auto-discovery)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--period",
|
||||
type=int,
|
||||
default=30,
|
||||
help="Monitoring period in days (default: 30)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--scope",
|
||||
choices=["all", "traffic", "keywords", "content"],
|
||||
default="all",
|
||||
help="Monitoring scope (default: all)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--json",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Output in JSON format",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Save output to file path",
|
||||
)
|
||||
return parser.parse_args(argv)
|
||||
|
||||
|
||||
async def async_main(args: argparse.Namespace) -> None:
|
||||
monitor = CompetitiveMonitor()
|
||||
|
||||
result = await monitor.monitor(
|
||||
target=args.target,
|
||||
competitors=args.competitors or None,
|
||||
period=args.period,
|
||||
scope=args.scope,
|
||||
)
|
||||
|
||||
if args.json:
|
||||
output_str = json.dumps(_serialize_result(result), indent=2, ensure_ascii=False)
|
||||
else:
|
||||
output_str = _format_text_report(result)
|
||||
|
||||
if args.output:
|
||||
with open(args.output, "w", encoding="utf-8") as f:
|
||||
f.write(output_str)
|
||||
logger.info(f"Report saved to {args.output}")
|
||||
else:
|
||||
print(output_str)
|
||||
|
||||
monitor.print_stats()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
args = parse_args()
|
||||
asyncio.run(async_main(args))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,719 @@
|
||||
"""
|
||||
Competitor Profiler - SEO Competitive Intelligence
|
||||
===================================================
|
||||
Purpose: Auto-discover competitors, build profile cards, comparison matrices,
|
||||
keyword overlap analysis, and competitive threat scoring.
|
||||
Python: 3.10+
|
||||
|
||||
Usage:
|
||||
python competitor_profiler.py --target https://example.com --json
|
||||
python competitor_profiler.py --target https://example.com --competitor https://comp1.com --json
|
||||
python competitor_profiler.py --target https://example.com --max-competitors 10 --korean-market --json
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import sys
|
||||
from dataclasses import dataclass, field, asdict
|
||||
from datetime import datetime
|
||||
from typing import Any
|
||||
from urllib.parse import urlparse
|
||||
|
||||
from base_client import BaseAsyncClient, config
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Data classes
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@dataclass
|
||||
class CompetitorProfile:
|
||||
"""Full profile card for a single domain."""
|
||||
domain: str
|
||||
domain_rating: float = 0.0
|
||||
organic_traffic: int = 0
|
||||
organic_keywords: int = 0
|
||||
referring_domains: int = 0
|
||||
top_pages_count: int = 0
|
||||
traffic_value_usd: float = 0.0
|
||||
content_volume: int = 0
|
||||
naver_blog_presence: bool = False
|
||||
naver_cafe_presence: bool = False
|
||||
|
||||
|
||||
@dataclass
|
||||
class KeywordOverlap:
|
||||
"""Keyword overlap analysis between target and a competitor."""
|
||||
shared: int = 0
|
||||
unique_target: int = 0
|
||||
unique_competitor: int = 0
|
||||
gap_keywords: int = 0
|
||||
overlap_percentage: float = 0.0
|
||||
|
||||
|
||||
@dataclass
|
||||
class ThreatAssessment:
|
||||
"""Competitive threat score and breakdown for one competitor."""
|
||||
domain: str = ""
|
||||
threat_score: float = 0.0
|
||||
growth_rate: float = 0.0
|
||||
dr_gap: float = 0.0
|
||||
keyword_overlap_pct: float = 0.0
|
||||
traffic_ratio: float = 0.0
|
||||
strengths: list[str] = field(default_factory=list)
|
||||
weaknesses: list[str] = field(default_factory=list)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ComparisonMatrix:
|
||||
"""Multi-dimensional comparison matrix across SEO dimensions."""
|
||||
dimensions: list[str] = field(default_factory=list)
|
||||
target_scores: dict[str, float] = field(default_factory=dict)
|
||||
competitor_scores: dict[str, dict[str, float]] = field(default_factory=dict)
|
||||
|
||||
|
||||
@dataclass
|
||||
class CompetitorProfilingResult:
|
||||
"""Full profiling result with all competitor data."""
|
||||
target: str = ""
|
||||
target_profile: CompetitorProfile | None = None
|
||||
competitors: list[dict[str, Any]] = field(default_factory=list)
|
||||
comparison_matrix: ComparisonMatrix | None = None
|
||||
market_position: str = "unknown"
|
||||
timestamp: str = ""
|
||||
errors: list[str] = field(default_factory=list)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Profiler
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class CompetitorProfiler(BaseAsyncClient):
|
||||
"""Builds competitor profiles using Ahrefs MCP tools."""
|
||||
|
||||
DIMENSIONS = ["traffic", "domain_rating", "keywords", "backlinks", "content"]
|
||||
|
||||
def __init__(self, korean_market: bool = False):
|
||||
super().__init__(max_concurrent=5, requests_per_second=2.0)
|
||||
self.korean_market = korean_market
|
||||
|
||||
@staticmethod
|
||||
def _extract_domain(url: str) -> str:
|
||||
"""Extract bare domain from URL or return as-is if already bare."""
|
||||
if "://" in url:
|
||||
parsed = urlparse(url)
|
||||
return parsed.netloc.lower().replace("www.", "")
|
||||
return url.lower().replace("www.", "")
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Ahrefs MCP wrappers (return dicts; Claude MCP bridge fills these)
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
async def _call_ahrefs(self, tool: str, params: dict[str, Any]) -> dict:
|
||||
"""Simulate Ahrefs MCP call. In production, routed via MCP bridge."""
|
||||
self.logger.info(f"Ahrefs MCP call: {tool} | params={params}")
|
||||
return {"tool": tool, "params": params, "data": {}}
|
||||
|
||||
async def discover_competitors(
|
||||
self, target: str, limit: int = 20
|
||||
) -> list[str]:
|
||||
"""Discover organic competitors via site-explorer-organic-competitors."""
|
||||
domain = self._extract_domain(target)
|
||||
self.logger.info(f"Discovering competitors for {domain} (limit={limit})")
|
||||
|
||||
resp = await self._call_ahrefs(
|
||||
"site-explorer-organic-competitors",
|
||||
{"target": domain, "limit": limit, "country": "kr"},
|
||||
)
|
||||
|
||||
competitors_raw: list[dict] = resp.get("data", {}).get("competitors", [])
|
||||
discovered = []
|
||||
for entry in competitors_raw:
|
||||
comp_domain = entry.get("domain", "")
|
||||
if comp_domain and comp_domain != domain:
|
||||
discovered.append(comp_domain)
|
||||
|
||||
if not discovered:
|
||||
self.logger.warning(
|
||||
"No competitors returned from Ahrefs; "
|
||||
"check that the target domain has organic traffic."
|
||||
)
|
||||
else:
|
||||
self.logger.info(f"Discovered {len(discovered)} competitors")
|
||||
|
||||
return discovered[:limit]
|
||||
|
||||
async def build_profile(self, domain: str) -> CompetitorProfile:
|
||||
"""Build a complete profile card for a single domain."""
|
||||
domain = self._extract_domain(domain)
|
||||
profile = CompetitorProfile(domain=domain)
|
||||
|
||||
# --- Metrics ---
|
||||
metrics_resp = await self._call_ahrefs(
|
||||
"site-explorer-metrics", {"target": domain}
|
||||
)
|
||||
metrics = metrics_resp.get("data", {})
|
||||
profile.organic_traffic = int(metrics.get("organic_traffic", 0))
|
||||
profile.organic_keywords = int(metrics.get("organic_keywords", 0))
|
||||
profile.traffic_value_usd = float(metrics.get("traffic_value", 0.0))
|
||||
|
||||
# --- Domain Rating ---
|
||||
dr_resp = await self._call_ahrefs(
|
||||
"site-explorer-domain-rating", {"target": domain}
|
||||
)
|
||||
dr_data = dr_resp.get("data", {})
|
||||
profile.domain_rating = float(dr_data.get("domain_rating", 0.0))
|
||||
|
||||
# --- Referring Domains ---
|
||||
bl_resp = await self._call_ahrefs(
|
||||
"site-explorer-backlinks-stats", {"target": domain}
|
||||
)
|
||||
bl_data = bl_resp.get("data", {})
|
||||
profile.referring_domains = int(bl_data.get("referring_domains", 0))
|
||||
|
||||
# --- Top Pages ---
|
||||
pages_resp = await self._call_ahrefs(
|
||||
"site-explorer-top-pages", {"target": domain, "limit": 1000}
|
||||
)
|
||||
pages_data = pages_resp.get("data", {})
|
||||
profile.top_pages_count = len(pages_data.get("pages", []))
|
||||
|
||||
# --- Content Volume (pages indexed) ---
|
||||
history_resp = await self._call_ahrefs(
|
||||
"site-explorer-pages-history", {"target": domain}
|
||||
)
|
||||
history_data = history_resp.get("data", {})
|
||||
data_points = history_data.get("data_points", [])
|
||||
if data_points:
|
||||
latest = data_points[-1]
|
||||
profile.content_volume = int(latest.get("pages", 0))
|
||||
|
||||
self.logger.info(
|
||||
f"Profile built for {domain}: DR={profile.domain_rating}, "
|
||||
f"traffic={profile.organic_traffic}, keywords={profile.organic_keywords}"
|
||||
)
|
||||
return profile
|
||||
|
||||
async def analyze_keyword_overlap(
|
||||
self, target: str, competitor: str, limit: int = 1000
|
||||
) -> KeywordOverlap:
|
||||
"""Analyze keyword overlap between target and a single competitor."""
|
||||
target_domain = self._extract_domain(target)
|
||||
comp_domain = self._extract_domain(competitor)
|
||||
|
||||
# Fetch keyword sets for both domains
|
||||
target_resp = await self._call_ahrefs(
|
||||
"site-explorer-organic-keywords",
|
||||
{"target": target_domain, "limit": limit},
|
||||
)
|
||||
comp_resp = await self._call_ahrefs(
|
||||
"site-explorer-organic-keywords",
|
||||
{"target": comp_domain, "limit": limit},
|
||||
)
|
||||
|
||||
target_kws: set[str] = set()
|
||||
for kw in target_resp.get("data", {}).get("keywords", []):
|
||||
keyword = kw.get("keyword", "")
|
||||
if keyword:
|
||||
target_kws.add(keyword.lower())
|
||||
|
||||
comp_kws: set[str] = set()
|
||||
for kw in comp_resp.get("data", {}).get("keywords", []):
|
||||
keyword = kw.get("keyword", "")
|
||||
if keyword:
|
||||
comp_kws.add(keyword.lower())
|
||||
|
||||
shared = target_kws & comp_kws
|
||||
unique_target = target_kws - comp_kws
|
||||
unique_comp = comp_kws - target_kws
|
||||
gap = unique_comp # keywords the competitor ranks for but target does not
|
||||
|
||||
total_union = len(target_kws | comp_kws) or 1
|
||||
overlap_pct = (len(shared) / total_union) * 100.0
|
||||
|
||||
overlap = KeywordOverlap(
|
||||
shared=len(shared),
|
||||
unique_target=len(unique_target),
|
||||
unique_competitor=len(unique_comp),
|
||||
gap_keywords=len(gap),
|
||||
overlap_percentage=round(overlap_pct, 2),
|
||||
)
|
||||
self.logger.info(
|
||||
f"Keyword overlap {target_domain} vs {comp_domain}: "
|
||||
f"shared={overlap.shared}, gap={overlap.gap_keywords}"
|
||||
)
|
||||
return overlap
|
||||
|
||||
def build_comparison_matrix(
|
||||
self,
|
||||
target_profile: CompetitorProfile,
|
||||
competitor_profiles: list[CompetitorProfile],
|
||||
) -> ComparisonMatrix:
|
||||
"""Create a multi-dimensional comparison matrix."""
|
||||
matrix = ComparisonMatrix(dimensions=list(self.DIMENSIONS))
|
||||
|
||||
# Normalize scores to 0-100 scale relative to max in competitive set
|
||||
all_profiles = [target_profile] + competitor_profiles
|
||||
|
||||
def _max_val(attr: str) -> float:
|
||||
return max(getattr(p, attr, 0) for p in all_profiles) or 1
|
||||
|
||||
max_traffic = _max_val("organic_traffic")
|
||||
max_dr = 100.0 # DR is already 0-100
|
||||
max_kw = _max_val("organic_keywords")
|
||||
max_rd = _max_val("referring_domains")
|
||||
max_content = _max_val("content_volume")
|
||||
|
||||
def _norm(profile: CompetitorProfile) -> dict[str, float]:
|
||||
return {
|
||||
"traffic": round((profile.organic_traffic / max_traffic) * 100, 1),
|
||||
"domain_rating": round(profile.domain_rating, 1),
|
||||
"keywords": round((profile.organic_keywords / max_kw) * 100, 1),
|
||||
"backlinks": round((profile.referring_domains / max_rd) * 100, 1),
|
||||
"content": round((profile.content_volume / max_content) * 100, 1)
|
||||
if max_content > 0
|
||||
else 0.0,
|
||||
}
|
||||
|
||||
matrix.target_scores = _norm(target_profile)
|
||||
|
||||
for cp in competitor_profiles:
|
||||
matrix.competitor_scores[cp.domain] = _norm(cp)
|
||||
|
||||
return matrix
|
||||
|
||||
def score_threat(
|
||||
self,
|
||||
target_profile: CompetitorProfile,
|
||||
competitor_profile: CompetitorProfile,
|
||||
overlap: KeywordOverlap,
|
||||
) -> ThreatAssessment:
|
||||
"""Score competitive threat 0-100 based on multiple factors."""
|
||||
assessment = ThreatAssessment(domain=competitor_profile.domain)
|
||||
|
||||
# --- DR gap (positive = competitor stronger) ---
|
||||
dr_gap = competitor_profile.domain_rating - target_profile.domain_rating
|
||||
assessment.dr_gap = round(dr_gap, 1)
|
||||
dr_score = min(max((dr_gap + 30) / 60 * 100, 0), 100) # scale -30..+30 -> 0-100
|
||||
|
||||
# --- Traffic ratio ---
|
||||
target_traffic = max(target_profile.organic_traffic, 1)
|
||||
traffic_ratio = competitor_profile.organic_traffic / target_traffic
|
||||
assessment.traffic_ratio = round(traffic_ratio, 2)
|
||||
traffic_score = min(traffic_ratio * 50, 100) # 2x traffic = 100
|
||||
|
||||
# --- Keyword overlap percentage ---
|
||||
assessment.keyword_overlap_pct = overlap.overlap_percentage
|
||||
overlap_score = min(overlap.overlap_percentage * 2, 100) # 50% overlap = 100
|
||||
|
||||
# --- Gap keywords (competitor ranks, target doesn't) ---
|
||||
total_target_kw = max(overlap.shared + overlap.unique_target, 1)
|
||||
gap_ratio = overlap.gap_keywords / total_target_kw
|
||||
gap_score = min(gap_ratio * 100, 100)
|
||||
|
||||
# --- Weighted threat score ---
|
||||
threat = (
|
||||
dr_score * 0.20
|
||||
+ traffic_score * 0.30
|
||||
+ overlap_score * 0.25
|
||||
+ gap_score * 0.25
|
||||
)
|
||||
assessment.threat_score = round(min(max(threat, 0), 100), 1)
|
||||
|
||||
# --- Identify strengths & weaknesses ---
|
||||
if dr_gap > 5:
|
||||
assessment.strengths.append(f"Higher DR by {dr_gap:.0f} points")
|
||||
elif dr_gap < -5:
|
||||
assessment.weaknesses.append(f"Lower DR by {abs(dr_gap):.0f} points")
|
||||
|
||||
if traffic_ratio > 1.5:
|
||||
assessment.strengths.append(
|
||||
f"Traffic {traffic_ratio:.1f}x higher than target"
|
||||
)
|
||||
elif traffic_ratio < 0.5:
|
||||
assessment.weaknesses.append(
|
||||
f"Traffic only {traffic_ratio:.1f}x of target"
|
||||
)
|
||||
|
||||
if overlap.gap_keywords > overlap.shared:
|
||||
assessment.strengths.append(
|
||||
f"{overlap.gap_keywords} keywords target is missing"
|
||||
)
|
||||
|
||||
if competitor_profile.referring_domains > target_profile.referring_domains * 1.5:
|
||||
assessment.strengths.append("Significantly more referring domains")
|
||||
elif competitor_profile.referring_domains < target_profile.referring_domains * 0.5:
|
||||
assessment.weaknesses.append("Fewer referring domains")
|
||||
|
||||
if competitor_profile.content_volume > target_profile.content_volume * 1.5:
|
||||
assessment.strengths.append("Larger content volume")
|
||||
elif competitor_profile.content_volume < target_profile.content_volume * 0.5:
|
||||
assessment.weaknesses.append("Smaller content library")
|
||||
|
||||
self.logger.info(
|
||||
f"Threat score for {competitor_profile.domain}: "
|
||||
f"{assessment.threat_score}/100"
|
||||
)
|
||||
return assessment
|
||||
|
||||
async def detect_korean_presence(self, domain: str) -> dict[str, bool]:
|
||||
"""Check Naver Blog/Cafe presence for a domain (heuristic)."""
|
||||
domain = self._extract_domain(domain)
|
||||
self.logger.info(f"Checking Korean market presence for {domain}")
|
||||
|
||||
# In production, this would use WebSearch MCP to query Naver
|
||||
# Heuristic: check if domain has .co.kr or .kr TLD,
|
||||
# or has Korean-language top pages
|
||||
is_korean_tld = domain.endswith(".kr") or domain.endswith(".co.kr")
|
||||
|
||||
# Check top pages for Korean content signals
|
||||
pages_resp = await self._call_ahrefs(
|
||||
"site-explorer-organic-keywords",
|
||||
{"target": domain, "limit": 50, "country": "kr"},
|
||||
)
|
||||
kr_keywords = pages_resp.get("data", {}).get("keywords", [])
|
||||
has_kr_keywords = len(kr_keywords) > 0
|
||||
|
||||
return {
|
||||
"naver_blog_presence": is_korean_tld or has_kr_keywords,
|
||||
"naver_cafe_presence": is_korean_tld,
|
||||
"korean_tld": is_korean_tld,
|
||||
"korean_keyword_count": len(kr_keywords),
|
||||
}
|
||||
|
||||
def determine_market_position(
|
||||
self,
|
||||
target_profile: CompetitorProfile,
|
||||
competitor_profiles: list[CompetitorProfile],
|
||||
) -> str:
|
||||
"""Classify target as leader / challenger / follower / niche."""
|
||||
if not competitor_profiles:
|
||||
return "unknown"
|
||||
|
||||
all_profiles = [target_profile] + competitor_profiles
|
||||
all_profiles.sort(key=lambda p: p.organic_traffic, reverse=True)
|
||||
|
||||
target_rank = next(
|
||||
(i for i, p in enumerate(all_profiles) if p.domain == target_profile.domain),
|
||||
len(all_profiles),
|
||||
)
|
||||
|
||||
total = len(all_profiles)
|
||||
percentile = target_rank / total
|
||||
|
||||
# DR comparison
|
||||
avg_competitor_dr = (
|
||||
sum(p.domain_rating for p in competitor_profiles) / len(competitor_profiles)
|
||||
if competitor_profiles
|
||||
else 0
|
||||
)
|
||||
dr_advantage = target_profile.domain_rating - avg_competitor_dr
|
||||
|
||||
# Traffic leader check
|
||||
max_traffic = max(p.organic_traffic for p in all_profiles) or 1
|
||||
traffic_share = target_profile.organic_traffic / max_traffic
|
||||
|
||||
if percentile <= 0.1 and traffic_share >= 0.8:
|
||||
return "leader"
|
||||
elif percentile <= 0.33 or (dr_advantage > 10 and traffic_share > 0.5):
|
||||
return "challenger"
|
||||
elif percentile <= 0.66:
|
||||
return "follower"
|
||||
else:
|
||||
# Check if niche player (high DR but low traffic = niche authority)
|
||||
if target_profile.domain_rating > avg_competitor_dr:
|
||||
return "niche"
|
||||
return "follower"
|
||||
|
||||
async def profile(
|
||||
self,
|
||||
target: str,
|
||||
competitors: list[str] | None = None,
|
||||
max_competitors: int = 10,
|
||||
) -> CompetitorProfilingResult:
|
||||
"""Orchestrate full competitor profiling pipeline."""
|
||||
timestamp = datetime.now().isoformat()
|
||||
result = CompetitorProfilingResult(
|
||||
target=self._extract_domain(target),
|
||||
timestamp=timestamp,
|
||||
)
|
||||
|
||||
try:
|
||||
# Step 1: Build target profile
|
||||
self.logger.info("Step 1/6: Building target profile...")
|
||||
target_profile = await self.build_profile(target)
|
||||
result.target_profile = target_profile
|
||||
|
||||
# Step 2: Discover or validate competitors
|
||||
self.logger.info("Step 2/6: Discovering competitors...")
|
||||
if competitors:
|
||||
comp_domains = [self._extract_domain(c) for c in competitors]
|
||||
else:
|
||||
comp_domains = await self.discover_competitors(
|
||||
target, limit=max_competitors
|
||||
)
|
||||
|
||||
if not comp_domains:
|
||||
result.errors.append("No competitors found or provided.")
|
||||
return result
|
||||
|
||||
comp_domains = comp_domains[:max_competitors]
|
||||
|
||||
# Step 3: Build competitor profiles
|
||||
self.logger.info(
|
||||
f"Step 3/6: Profiling {len(comp_domains)} competitors..."
|
||||
)
|
||||
competitor_profiles: list[CompetitorProfile] = []
|
||||
for domain in comp_domains:
|
||||
try:
|
||||
cp = await self.build_profile(domain)
|
||||
if self.korean_market:
|
||||
kr_presence = await self.detect_korean_presence(domain)
|
||||
cp.naver_blog_presence = kr_presence.get(
|
||||
"naver_blog_presence", False
|
||||
)
|
||||
cp.naver_cafe_presence = kr_presence.get(
|
||||
"naver_cafe_presence", False
|
||||
)
|
||||
competitor_profiles.append(cp)
|
||||
except Exception as e:
|
||||
msg = f"Failed to profile {domain}: {e}"
|
||||
self.logger.error(msg)
|
||||
result.errors.append(msg)
|
||||
|
||||
# Step 4: Keyword overlap analysis
|
||||
self.logger.info("Step 4/6: Analyzing keyword overlaps...")
|
||||
overlaps: dict[str, KeywordOverlap] = {}
|
||||
for cp in competitor_profiles:
|
||||
try:
|
||||
overlap = await self.analyze_keyword_overlap(target, cp.domain)
|
||||
overlaps[cp.domain] = overlap
|
||||
except Exception as e:
|
||||
msg = f"Keyword overlap failed for {cp.domain}: {e}"
|
||||
self.logger.error(msg)
|
||||
result.errors.append(msg)
|
||||
overlaps[cp.domain] = KeywordOverlap()
|
||||
|
||||
# Step 5: Build comparison matrix
|
||||
self.logger.info("Step 5/6: Building comparison matrix...")
|
||||
matrix = self.build_comparison_matrix(target_profile, competitor_profiles)
|
||||
result.comparison_matrix = matrix
|
||||
|
||||
# Step 6: Score threats and assemble output
|
||||
self.logger.info("Step 6/6: Scoring competitive threats...")
|
||||
for cp in competitor_profiles:
|
||||
overlap = overlaps.get(cp.domain, KeywordOverlap())
|
||||
threat = self.score_threat(target_profile, cp, overlap)
|
||||
|
||||
competitor_entry = {
|
||||
"domain": cp.domain,
|
||||
"profile": asdict(cp),
|
||||
"threat_score": threat.threat_score,
|
||||
"threat_detail": asdict(threat),
|
||||
"keyword_overlap": asdict(overlap),
|
||||
}
|
||||
result.competitors.append(competitor_entry)
|
||||
|
||||
# Sort by threat score descending
|
||||
result.competitors.sort(
|
||||
key=lambda c: c.get("threat_score", 0), reverse=True
|
||||
)
|
||||
|
||||
# Determine market position
|
||||
result.market_position = self.determine_market_position(
|
||||
target_profile, competitor_profiles
|
||||
)
|
||||
|
||||
self.logger.info(
|
||||
f"Profiling complete: {len(result.competitors)} competitors analyzed. "
|
||||
f"Market position: {result.market_position}"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
msg = f"Profiling pipeline error: {e}"
|
||||
self.logger.error(msg)
|
||||
result.errors.append(msg)
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Output helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _format_text_report(result: CompetitorProfilingResult) -> str:
|
||||
"""Format profiling result as human-readable text report."""
|
||||
lines: list[str] = []
|
||||
lines.append("=" * 70)
|
||||
lines.append(f" COMPETITOR INTELLIGENCE REPORT")
|
||||
lines.append(f" Target: {result.target}")
|
||||
lines.append(f" Generated: {result.timestamp}")
|
||||
lines.append(f" Market Position: {result.market_position.upper()}")
|
||||
lines.append("=" * 70)
|
||||
|
||||
if result.target_profile:
|
||||
tp = result.target_profile
|
||||
lines.append("")
|
||||
lines.append("--- TARGET PROFILE ---")
|
||||
lines.append(f" Domain Rating: {tp.domain_rating}")
|
||||
lines.append(f" Organic Traffic: {tp.organic_traffic:,}")
|
||||
lines.append(f" Organic Keywords: {tp.organic_keywords:,}")
|
||||
lines.append(f" Referring Domains: {tp.referring_domains:,}")
|
||||
lines.append(f" Top Pages: {tp.top_pages_count:,}")
|
||||
lines.append(f" Content Volume: {tp.content_volume:,}")
|
||||
lines.append(f" Traffic Value: ${tp.traffic_value_usd:,.2f}")
|
||||
|
||||
if result.competitors:
|
||||
lines.append("")
|
||||
lines.append("--- COMPETITORS (sorted by threat score) ---")
|
||||
for i, comp in enumerate(result.competitors, 1):
|
||||
p = comp["profile"]
|
||||
t = comp["threat_detail"]
|
||||
o = comp["keyword_overlap"]
|
||||
lines.append("")
|
||||
lines.append(f" #{i} {comp['domain']}")
|
||||
lines.append(f" Threat Score: {comp['threat_score']}/100")
|
||||
lines.append(f" Domain Rating: {p['domain_rating']}")
|
||||
lines.append(f" Organic Traffic: {p['organic_traffic']:,}")
|
||||
lines.append(f" Keywords: {p['organic_keywords']:,}")
|
||||
lines.append(f" Referring Doms: {p['referring_domains']:,}")
|
||||
lines.append(f" Keyword Overlap: {o['shared']} shared, {o['gap_keywords']} gap")
|
||||
if t.get("strengths"):
|
||||
lines.append(f" Strengths: {'; '.join(t['strengths'])}")
|
||||
if t.get("weaknesses"):
|
||||
lines.append(f" Weaknesses: {'; '.join(t['weaknesses'])}")
|
||||
|
||||
if result.comparison_matrix:
|
||||
m = result.comparison_matrix
|
||||
lines.append("")
|
||||
lines.append("--- COMPARISON MATRIX ---")
|
||||
header = f" {'Dimension':<20} {'Target':>10}"
|
||||
for domain in m.competitor_scores:
|
||||
short = domain[:15]
|
||||
header += f" {short:>15}"
|
||||
lines.append(header)
|
||||
lines.append(" " + "-" * (len(header) - 2))
|
||||
|
||||
for dim in m.dimensions:
|
||||
row = f" {dim:<20} {m.target_scores.get(dim, 0):>10.1f}"
|
||||
for domain, scores in m.competitor_scores.items():
|
||||
row += f" {scores.get(dim, 0):>15.1f}"
|
||||
lines.append(row)
|
||||
|
||||
if result.errors:
|
||||
lines.append("")
|
||||
lines.append("--- ERRORS ---")
|
||||
for err in result.errors:
|
||||
lines.append(f" - {err}")
|
||||
|
||||
lines.append("")
|
||||
lines.append("=" * 70)
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _serialize_result(result: CompetitorProfilingResult) -> dict:
|
||||
"""Convert result to JSON-serializable dict."""
|
||||
output = {
|
||||
"target": result.target,
|
||||
"target_profile": asdict(result.target_profile) if result.target_profile else None,
|
||||
"competitors": result.competitors,
|
||||
"comparison_matrix": asdict(result.comparison_matrix)
|
||||
if result.comparison_matrix
|
||||
else None,
|
||||
"market_position": result.market_position,
|
||||
"timestamp": result.timestamp,
|
||||
}
|
||||
if result.errors:
|
||||
output["errors"] = result.errors
|
||||
return output
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# CLI
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(
|
||||
description="SEO Competitor Profiler - Build competitive intelligence reports",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""\
|
||||
Examples:
|
||||
python competitor_profiler.py --target https://example.com --json
|
||||
python competitor_profiler.py --target https://example.com --competitor https://comp1.com --json
|
||||
python competitor_profiler.py --target https://example.com --max-competitors 10 --korean-market --json
|
||||
""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--target",
|
||||
required=True,
|
||||
help="Target website URL or domain to analyze",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--competitor",
|
||||
action="append",
|
||||
dest="competitors",
|
||||
default=[],
|
||||
help="Competitor URL/domain (repeatable; omit for auto-discovery)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max-competitors",
|
||||
type=int,
|
||||
default=10,
|
||||
help="Maximum competitors to profile (default: 10)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--korean-market",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Include Korean market analysis (Naver Blog/Cafe presence)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--json",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Output in JSON format",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Save output to file path",
|
||||
)
|
||||
return parser.parse_args(argv)
|
||||
|
||||
|
||||
async def async_main(args: argparse.Namespace) -> None:
|
||||
profiler = CompetitorProfiler(korean_market=args.korean_market)
|
||||
|
||||
result = await profiler.profile(
|
||||
target=args.target,
|
||||
competitors=args.competitors or None,
|
||||
max_competitors=args.max_competitors,
|
||||
)
|
||||
|
||||
if args.json:
|
||||
output_str = json.dumps(_serialize_result(result), indent=2, ensure_ascii=False)
|
||||
else:
|
||||
output_str = _format_text_report(result)
|
||||
|
||||
if args.output:
|
||||
with open(args.output, "w", encoding="utf-8") as f:
|
||||
f.write(output_str)
|
||||
logger.info(f"Report saved to {args.output}")
|
||||
else:
|
||||
print(output_str)
|
||||
|
||||
profiler.print_stats()
|
||||
|
||||
|
||||
def main() -> None:
|
||||
args = parse_args()
|
||||
asyncio.run(async_main(args))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,8 @@
|
||||
# 31-seo-competitor-intel dependencies
|
||||
requests>=2.31.0
|
||||
aiohttp>=3.9.0
|
||||
pandas>=2.1.0
|
||||
tenacity>=8.2.0
|
||||
tqdm>=4.66.0
|
||||
python-dotenv>=1.0.0
|
||||
rich>=13.7.0
|
||||
Reference in New Issue
Block a user