66dcf674c5
- Profile auditor (bio quality, posting patterns, growth readiness) - Tweet composer (hooks, threads, validation, 30+ proven patterns) - Content planner (weekly calendars with format mix) - Competitor analyzer (competitive intel via data import) - Growth tracker (snapshot-based progress tracking + milestone projection) - Algorithm reference doc (ranking signals, timing, format performance) - 226-line SKILL.md with practical playbook (no fluff) - Security audit: PASS (0 findings)
236 lines
8.1 KiB
Python
236 lines
8.1 KiB
Python
#!/usr/bin/env python3
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"""
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X/Twitter Competitor Analyzer — Analyze competitor profiles for content strategy insights.
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Takes competitor handles and available data, produces a competitive
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intelligence report with content patterns, engagement strategies, and gaps.
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Usage:
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python3 competitor_analyzer.py --handles @user1 @user2 @user3
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python3 competitor_analyzer.py --handles @user1 --followers 50000 --niche "AI"
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python3 competitor_analyzer.py --import data.json
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"""
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import argparse
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import json
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import sys
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from dataclasses import dataclass, field, asdict
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from typing import Optional
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@dataclass
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class CompetitorProfile:
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handle: str
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followers: int = 0
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following: int = 0
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posts_per_week: float = 0
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avg_likes: float = 0
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avg_replies: float = 0
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avg_retweets: float = 0
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thread_frequency: str = "" # daily, weekly, rarely
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top_topics: list = field(default_factory=list)
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content_mix: dict = field(default_factory=dict) # format: percentage
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posting_times: list = field(default_factory=list)
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bio: str = ""
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notes: str = ""
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@dataclass
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class CompetitiveInsight:
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category: str
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finding: str
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opportunity: str
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priority: str # HIGH, MEDIUM, LOW
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def calculate_engagement_rate(profile: CompetitorProfile) -> float:
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if profile.followers <= 0:
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return 0
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total_engagement = profile.avg_likes + profile.avg_replies + profile.avg_retweets
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return (total_engagement / profile.followers) * 100
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def analyze_competitors(competitors: list) -> list:
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insights = []
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# Engagement comparison
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engagement_rates = []
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for c in competitors:
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er = calculate_engagement_rate(c)
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engagement_rates.append((c.handle, er))
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if engagement_rates:
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top = max(engagement_rates, key=lambda x: x[1])
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if top[1] > 0:
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insights.append(CompetitiveInsight(
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"Engagement", f"Highest engagement: {top[0]} ({top[1]:.2f}%)",
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"Study their top posts — what format and topics drive replies?",
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"HIGH"
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))
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# Posting frequency
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frequencies = [(c.handle, c.posts_per_week) for c in competitors if c.posts_per_week > 0]
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if frequencies:
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avg_freq = sum(f for _, f in frequencies) / len(frequencies)
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insights.append(CompetitiveInsight(
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"Frequency", f"Average posting: {avg_freq:.0f}/week across competitors",
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f"Match or exceed {avg_freq:.0f} posts/week to compete for mindshare",
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"HIGH"
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))
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# Thread usage
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thread_users = [c.handle for c in competitors if c.thread_frequency in ("daily", "weekly")]
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if thread_users:
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insights.append(CompetitiveInsight(
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"Format", f"Active thread users: {', '.join(thread_users)}",
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"Threads are a proven growth lever in your niche. Publish 2-3/week minimum.",
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"HIGH"
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))
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# Reply engagement
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reply_heavy = [(c.handle, c.avg_replies) for c in competitors if c.avg_replies > c.avg_likes * 0.3]
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if reply_heavy:
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names = [h for h, _ in reply_heavy]
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insights.append(CompetitiveInsight(
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"Community", f"High reply ratios: {', '.join(names)}",
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"These accounts build community through conversation. Ask more questions in your tweets.",
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"MEDIUM"
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))
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# Follower/following ratio
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for c in competitors:
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if c.followers > 0 and c.following > 0:
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ratio = c.followers / c.following
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if ratio > 10:
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insights.append(CompetitiveInsight(
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"Authority", f"{c.handle} has {ratio:.0f}x follower/following ratio",
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"Strong authority signal — they attract followers without follow-backs",
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"LOW"
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))
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# Topic gaps
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all_topics = []
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for c in competitors:
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all_topics.extend(c.top_topics)
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if all_topics:
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from collections import Counter
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common = Counter(all_topics).most_common(5)
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insights.append(CompetitiveInsight(
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"Topics", f"Most covered topics: {', '.join(t for t, _ in common)}",
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"Cover these topics to compete, but find unique angles. What are they NOT covering?",
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"MEDIUM"
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))
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return insights
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def print_report(competitors: list, insights: list):
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print(f"\n{'='*70}")
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print(f" COMPETITIVE ANALYSIS REPORT")
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print(f"{'='*70}")
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# Profile summary table
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print(f"\n {'Handle':<20} {'Followers':>10} {'Posts/wk':>10} {'Eng Rate':>10}")
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print(f" {'─'*20} {'─'*10} {'─'*10} {'─'*10}")
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for c in competitors:
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er = calculate_engagement_rate(c)
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print(f" {c.handle:<20} {c.followers:>10,} {c.posts_per_week:>10.0f} {er:>9.2f}%")
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# Insights
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if insights:
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print(f"\n {'─'*66}")
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print(f" KEY INSIGHTS\n")
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priority_order = {"HIGH": 0, "MEDIUM": 1, "LOW": 2}
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sorted_insights = sorted(insights, key=lambda x: priority_order.get(x.priority, 3))
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for i in sorted_insights:
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icon = {"HIGH": "🔴", "MEDIUM": "🟡", "LOW": "⚪"}.get(i.priority, "❓")
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print(f" {icon} [{i.category}] {i.finding}")
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print(f" → {i.opportunity}")
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print()
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# Action items
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print(f" {'─'*66}")
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print(f" NEXT STEPS\n")
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print(f" 1. Search each competitor's profile on X — note their pinned tweet and bio")
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print(f" 2. Read their last 20 posts — categorize by format and topic")
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print(f" 3. Identify their top 3 performing posts — what made them work?")
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print(f" 4. Find gaps — what topics do they NOT cover that you can own?")
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print(f" 5. Set engagement targets based on their metrics as benchmarks")
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print(f"\n{'='*70}\n")
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def main():
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parser = argparse.ArgumentParser(
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description="Analyze X/Twitter competitors for content strategy insights",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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%(prog)s --handles @user1 @user2
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%(prog)s --import competitors.json
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JSON format for --import:
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[{"handle": "@user1", "followers": 50000, "posts_per_week": 14, ...}]
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""")
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parser.add_argument("--handles", nargs="+", default=[], help="Competitor handles")
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parser.add_argument("--import", dest="import_file", help="Import from JSON file")
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parser.add_argument("--json", action="store_true", help="Output JSON")
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args = parser.parse_args()
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competitors = []
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if args.import_file:
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with open(args.import_file) as f:
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data = json.load(f)
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for item in data:
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competitors.append(CompetitorProfile(**item))
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elif args.handles:
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for handle in args.handles:
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if not handle.startswith("@"):
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handle = f"@{handle}"
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competitors.append(CompetitorProfile(handle=handle))
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if all(c.followers == 0 for c in competitors):
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print(f"\n ℹ️ Handles registered: {', '.join(c.handle for c in competitors)}")
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print(f" To get full analysis, provide data via JSON import:")
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print(f" 1. Research each profile on X")
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print(f" 2. Create a JSON file with follower counts, posting frequency, etc.")
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print(f" 3. Run: {sys.argv[0]} --import data.json")
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print(f"\n Example JSON:")
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example = [asdict(CompetitorProfile(
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handle="@example",
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followers=25000,
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following=1200,
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posts_per_week=14,
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avg_likes=150,
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avg_replies=30,
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avg_retweets=20,
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thread_frequency="weekly",
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top_topics=["AI", "startups", "engineering"],
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))]
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print(f" {json.dumps(example, indent=2)}")
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print()
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return
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if not competitors:
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print("Error: provide --handles or --import", file=sys.stderr)
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sys.exit(1)
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insights = analyze_competitors(competitors)
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if args.json:
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print(json.dumps({
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"competitors": [asdict(c) for c in competitors],
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"insights": [asdict(i) for i in insights],
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}, indent=2))
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else:
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print_report(competitors, insights)
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if __name__ == "__main__":
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main()
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