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>
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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
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current_resp = await self._call_ahrefs(
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"site-explorer-metrics",
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{"target": domain},
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)
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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
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movement.lost_keywords = abs(net_change)
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movement.improved_positions = 0
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movement.declined_positions = abs(net_change)
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else:
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movement.new_keywords = 0
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movement.lost_keywords = 0
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movements.append(movement)
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self.logger.info(
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f"Keyword movement for {domain}: net={movement.net_change:+d}"
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)
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return movements
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async def compare_content_velocity(
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self, domains: list[str], period: int
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) -> list[ContentVelocity]:
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"""Compare content publication velocity across domains."""
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velocities: list[ContentVelocity] = []
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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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velocity = ContentVelocity(domain=domain)
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resp = await self._call_ahrefs(
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"site-explorer-pages-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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if len(data_points) >= 2:
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start_pages = int(data_points[0].get("pages", 0))
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end_pages = int(data_points[-1].get("pages", 0))
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velocity.total_pages_start = start_pages
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velocity.total_pages_end = end_pages
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velocity.net_new_pages = end_pages - start_pages
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months = max(period / 30.0, 1.0)
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velocity.new_pages_per_month = round(
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velocity.net_new_pages / months, 1
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)
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else:
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# Fallback: get current pages count
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metrics_resp = await self._call_ahrefs(
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"site-explorer-metrics", {"target": domain}
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)
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velocity.total_pages_end = int(
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metrics_resp.get("data", {}).get("pages", 0)
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)
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velocities.append(velocity)
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self.logger.info(
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f"Content velocity for {domain}: "
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f"{velocity.new_pages_per_month:.1f} pages/month"
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)
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return velocities
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def generate_alerts(
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self,
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traffic_trends: list[TrafficTrend],
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dr_trends: list[DrTrend],
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keyword_movements: list[KeywordMovement],
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content_velocities: list[ContentVelocity],
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target_domain: str,
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) -> list[CompetitiveAlert]:
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"""Generate alerts for significant competitive movements."""
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alerts: list[CompetitiveAlert] = []
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target_domain = self._extract_domain(target_domain)
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for trend in traffic_trends:
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if trend.domain == target_domain:
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continue
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abs_growth = abs(trend.growth_rate) / 100.0
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if abs_growth >= self.TRAFFIC_CHANGE_THRESHOLD:
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severity = "critical" if abs_growth >= 0.50 else "warning"
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direction = "surge" if trend.growth_rate > 0 else "decline"
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alerts.append(CompetitiveAlert(
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domain=trend.domain,
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alert_type=f"traffic_{direction}",
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message=(
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f"{trend.domain} traffic {direction}: "
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f"{trend.growth_rate:+.1f}% "
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f"({trend.period_start_traffic:,} -> {trend.current_traffic:,})"
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),
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severity=severity,
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metric_value=trend.growth_rate,
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threshold=self.TRAFFIC_CHANGE_THRESHOLD * 100,
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))
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for trend in dr_trends:
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if trend.domain == target_domain:
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continue
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if abs(trend.change) >= self.DR_CHANGE_THRESHOLD:
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severity = "warning" if abs(trend.change) < 5 else "critical"
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direction = "jump" if trend.change > 0 else "drop"
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alerts.append(CompetitiveAlert(
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domain=trend.domain,
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alert_type=f"dr_{direction}",
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message=(
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f"{trend.domain} DR {direction}: "
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f"{trend.change:+.1f} points "
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f"({trend.period_start_dr:.1f} -> {trend.current_dr:.1f})"
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),
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severity=severity,
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metric_value=trend.change,
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threshold=self.DR_CHANGE_THRESHOLD,
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))
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for movement in keyword_movements:
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if movement.domain == target_domain:
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continue
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# Check for keyword surge relative to total keywords
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if movement.new_keywords > 0:
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total_est = max(movement.new_keywords + movement.lost_keywords, 1)
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surge_ratio = movement.new_keywords / total_est
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if surge_ratio >= self.KEYWORD_SURGE_THRESHOLD:
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alerts.append(CompetitiveAlert(
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domain=movement.domain,
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alert_type="keyword_surge",
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message=(
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f"{movement.domain} gained {movement.new_keywords} "
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f"new keywords (net: {movement.net_change:+d})"
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),
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severity="warning",
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metric_value=float(movement.new_keywords),
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threshold=self.KEYWORD_SURGE_THRESHOLD * 100,
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))
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# Check for content burst
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if content_velocities:
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avg_velocity = (
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sum(v.new_pages_per_month for v in content_velocities)
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/ len(content_velocities)
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)
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for vel in content_velocities:
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if vel.domain == target_domain:
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continue
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if avg_velocity > 0 and vel.new_pages_per_month > avg_velocity * self.CONTENT_BURST_THRESHOLD:
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alerts.append(CompetitiveAlert(
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domain=vel.domain,
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alert_type="content_burst",
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message=(
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f"{vel.domain} publishing {vel.new_pages_per_month:.0f} "
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f"pages/month ({self.CONTENT_BURST_THRESHOLD:.0f}x above average)"
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),
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severity="info",
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metric_value=vel.new_pages_per_month,
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threshold=avg_velocity * self.CONTENT_BURST_THRESHOLD,
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))
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# Sort alerts by severity
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severity_order = {"critical": 0, "warning": 1, "info": 2}
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alerts.sort(key=lambda a: severity_order.get(a.severity, 3))
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self.logger.info(f"Generated {len(alerts)} competitive alerts")
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return alerts
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def estimate_market_share(
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self,
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traffic_trends: list[TrafficTrend],
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keyword_movements: list[KeywordMovement],
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) -> list[MarketShare]:
|
||||
"""Estimate market share based on organic traffic within competitive set."""
|
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shares: list[MarketShare] = []
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total_traffic = sum(t.current_traffic for t in traffic_trends) or 1
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total_kw_est = sum(
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max(m.new_keywords + abs(m.net_change), 1) for m in keyword_movements
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) or 1
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kw_by_domain: dict[str, int] = {}
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for m in keyword_movements:
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kw_by_domain[m.domain] = max(m.new_keywords + abs(m.net_change), 1)
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for trend in traffic_trends:
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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()
|
||||
Reference in New Issue
Block a user