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)
295 lines
11 KiB
Python
295 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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X/Twitter Profile Auditor — Audit any X profile for growth readiness.
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Checks bio quality, pinned tweet, posting patterns, and provides
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actionable recommendations. Works without API access by analyzing
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profile data you provide or scraping public info via web search.
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Usage:
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python3 profile_auditor.py --handle @username
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python3 profile_auditor.py --handle @username --json
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python3 profile_auditor.py --bio "current bio text" --followers 5000 --posts-per-week 10
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"""
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import argparse
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import json
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import re
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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 ProfileData:
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handle: str = ""
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bio: 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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reply_ratio: float = 0 # % of posts that are replies
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thread_ratio: float = 0 # % of posts that are threads
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has_pinned: bool = False
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pinned_age_days: int = 0
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has_link: bool = False
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has_newsletter: bool = False
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avg_engagement_rate: float = 0 # likes+replies+rts / followers
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@dataclass
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class AuditFinding:
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area: str
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status: str # GOOD, WARN, CRITICAL
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message: str
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fix: str = ""
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@dataclass
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class AuditReport:
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handle: str
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score: int = 0
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max_score: int = 100
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grade: str = ""
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findings: list = field(default_factory=list)
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recommendations: list = field(default_factory=list)
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def audit_bio(profile: ProfileData) -> list:
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findings = []
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bio = profile.bio.strip()
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if not bio:
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findings.append(AuditFinding("Bio", "CRITICAL", "No bio provided for audit",
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"Provide bio text with --bio flag"))
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return findings
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# Length check
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if len(bio) < 30:
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findings.append(AuditFinding("Bio", "WARN", f"Bio too short ({len(bio)} chars)",
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"Aim for 100-160 characters with clear value prop"))
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elif len(bio) > 160:
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findings.append(AuditFinding("Bio", "WARN", f"Bio may be too long ({len(bio)} chars)",
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"Keep under 160 chars for readability"))
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else:
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findings.append(AuditFinding("Bio", "GOOD", f"Bio length OK ({len(bio)} chars)"))
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# Hashtag check
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hashtags = re.findall(r'#\w+', bio)
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if hashtags:
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findings.append(AuditFinding("Bio", "WARN", f"Hashtags in bio ({', '.join(hashtags)})",
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"Remove hashtags — signals amateur. Use plain text."))
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else:
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findings.append(AuditFinding("Bio", "GOOD", "No hashtags in bio"))
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# Buzzword check
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buzzwords = ['entrepreneur', 'guru', 'ninja', 'rockstar', 'visionary', 'hustler',
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'thought leader', 'serial entrepreneur', 'dreamer', 'doer']
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found = [bw for bw in buzzwords if bw.lower() in bio.lower()]
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if found:
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findings.append(AuditFinding("Bio", "WARN", f"Buzzwords detected: {', '.join(found)}",
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"Replace with specific, concrete descriptions of what you do"))
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# Specificity check — pipes and slashes often signal unfocused bios
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if bio.count('|') >= 3 or bio.count('/') >= 3:
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findings.append(AuditFinding("Bio", "WARN", "Bio may lack focus (too many roles/identities)",
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"Lead with ONE clear identity. What's the #1 thing you want to be known for?"))
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# Social proof check
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proof_patterns = [r'\d+[kKmM]?\+?\s*(followers|subscribers|readers|users|customers)',
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r'(founder|ceo|cto|vp|head|director|lead)\s+(of|at|@)',
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r'(author|writer)\s+of', r'featured\s+in', r'ex-\w+']
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has_proof = any(re.search(p, bio, re.IGNORECASE) for p in proof_patterns)
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if has_proof:
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findings.append(AuditFinding("Bio", "GOOD", "Social proof detected"))
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else:
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findings.append(AuditFinding("Bio", "WARN", "No obvious social proof in bio",
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"Add a credential: title, metric, brand association, or achievement"))
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# CTA/Link check
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if profile.has_link:
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findings.append(AuditFinding("Bio", "GOOD", "Profile has a link"))
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else:
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findings.append(AuditFinding("Bio", "WARN", "No link in profile",
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"Add a link to newsletter, product, or portfolio"))
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return findings
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def audit_activity(profile: ProfileData) -> list:
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findings = []
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# Posting frequency
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if profile.posts_per_week <= 0:
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findings.append(AuditFinding("Activity", "CRITICAL", "No posting data provided",
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"Provide --posts-per-week estimate"))
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elif profile.posts_per_week < 3:
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findings.append(AuditFinding("Activity", "CRITICAL",
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f"Very low posting ({profile.posts_per_week:.0f}/week)",
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"Minimum 7 posts/week (1/day). Aim for 14-21."))
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elif profile.posts_per_week < 7:
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findings.append(AuditFinding("Activity", "WARN",
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f"Low posting ({profile.posts_per_week:.0f}/week)",
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"Aim for 2-3 posts per day for consistent growth"))
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elif profile.posts_per_week < 21:
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findings.append(AuditFinding("Activity", "GOOD",
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f"Good posting cadence ({profile.posts_per_week:.0f}/week)"))
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else:
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findings.append(AuditFinding("Activity", "GOOD",
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f"High posting cadence ({profile.posts_per_week:.0f}/week)"))
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# Reply ratio
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if profile.reply_ratio > 0:
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if profile.reply_ratio < 0.2:
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findings.append(AuditFinding("Activity", "WARN",
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f"Low reply ratio ({profile.reply_ratio:.0%})",
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"Aim for 30%+ replies. Engage with others, don't just broadcast."))
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elif profile.reply_ratio >= 0.3:
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findings.append(AuditFinding("Activity", "GOOD",
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f"Healthy reply ratio ({profile.reply_ratio:.0%})"))
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# Follower/following ratio
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if profile.followers > 0 and profile.following > 0:
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ratio = profile.followers / profile.following
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if ratio < 0.5:
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findings.append(AuditFinding("Profile", "WARN",
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f"Low follower/following ratio ({ratio:.1f}x)",
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"Unfollow inactive accounts. Ratio should trend toward 2:1+"))
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elif ratio >= 2:
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findings.append(AuditFinding("Profile", "GOOD",
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f"Healthy follower/following ratio ({ratio:.1f}x)"))
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# Pinned tweet
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if profile.has_pinned:
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if profile.pinned_age_days > 30:
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findings.append(AuditFinding("Profile", "WARN",
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f"Pinned tweet is {profile.pinned_age_days} days old",
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"Update pinned tweet monthly with your latest best content"))
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else:
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findings.append(AuditFinding("Profile", "GOOD", "Pinned tweet is recent"))
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else:
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findings.append(AuditFinding("Profile", "WARN", "No pinned tweet",
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"Pin your best-performing tweet or thread. It's your landing page."))
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return findings
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def calculate_score(findings: list) -> tuple:
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total = len(findings)
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if total == 0:
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return 0, "F"
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good = sum(1 for f in findings if f.status == "GOOD")
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score = int((good / total) * 100)
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if score >= 90:
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grade = "A"
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elif score >= 75:
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grade = "B"
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elif score >= 60:
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grade = "C"
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elif score >= 40:
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grade = "D"
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else:
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grade = "F"
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return score, grade
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def generate_recommendations(findings: list, profile: ProfileData) -> list:
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recs = []
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criticals = [f for f in findings if f.status == "CRITICAL"]
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warns = [f for f in findings if f.status == "WARN"]
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for f in criticals:
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if f.fix:
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recs.append(f"🔴 {f.fix}")
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for f in warns[:3]: # Top 3 warnings
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if f.fix:
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recs.append(f"🟡 {f.fix}")
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# Stage-specific advice
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if profile.followers < 1000:
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recs.append("📈 Growth phase: Focus 70% on replies to larger accounts, 30% on your own posts")
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elif profile.followers < 10000:
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recs.append("📈 Momentum phase: 2-3 threads/week + daily engagement. Start a recurring series.")
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else:
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recs.append("📈 Scale phase: Leverage audience with cross-platform repurposing + newsletter growth")
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return recs
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def main():
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parser = argparse.ArgumentParser(
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description="Audit an X/Twitter profile for growth readiness",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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%(prog)s --handle @rezarezvani --bio "CTO building AI products" --followers 5000
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%(prog)s --bio "Entrepreneur | Dreamer | Hustle" --followers 200 --posts-per-week 3
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%(prog)s --handle @example --followers 50000 --posts-per-week 21 --reply-ratio 0.4 --json
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""")
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parser.add_argument("--handle", default="@unknown", help="X handle")
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parser.add_argument("--bio", default="", help="Current bio text")
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parser.add_argument("--followers", type=int, default=0, help="Follower count")
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parser.add_argument("--following", type=int, default=0, help="Following count")
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parser.add_argument("--posts-per-week", type=float, default=0, help="Average posts per week")
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parser.add_argument("--reply-ratio", type=float, default=0, help="Fraction of posts that are replies (0-1)")
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parser.add_argument("--has-pinned", action="store_true", help="Has a pinned tweet")
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parser.add_argument("--pinned-age-days", type=int, default=0, help="Age of pinned tweet in days")
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parser.add_argument("--has-link", action="store_true", help="Has link in profile")
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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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profile = ProfileData(
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handle=args.handle,
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bio=args.bio,
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followers=args.followers,
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following=args.following,
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posts_per_week=args.posts_per_week,
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reply_ratio=args.reply_ratio,
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has_pinned=args.has_pinned,
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pinned_age_days=args.pinned_age_days,
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has_link=args.has_link,
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)
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findings = audit_bio(profile) + audit_activity(profile)
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score, grade = calculate_score(findings)
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recs = generate_recommendations(findings, profile)
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report = AuditReport(
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handle=profile.handle,
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score=score,
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grade=grade,
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findings=[asdict(f) for f in findings],
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recommendations=recs,
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)
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if args.json:
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print(json.dumps(asdict(report), indent=2))
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else:
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print(f"\n{'='*60}")
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print(f" X PROFILE AUDIT — {report.handle}")
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print(f"{'='*60}")
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print(f"\n Score: {report.score}/100 (Grade: {report.grade})\n")
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for f in findings:
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icon = {"GOOD": "✅", "WARN": "⚠️", "CRITICAL": "🔴"}.get(f.status, "❓")
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print(f" {icon} [{f.area}] {f.message}")
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if f.fix and f.status != "GOOD":
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print(f" → {f.fix}")
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if recs:
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print(f"\n {'─'*56}")
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print(f" TOP RECOMMENDATIONS\n")
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for i, r in enumerate(recs, 1):
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print(f" {i}. {r}")
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print(f"\n{'='*60}\n")
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if __name__ == "__main__":
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main()
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