#!/usr/bin/env python3 """ headline_scorer.py — Scores headlines 0-100 Usage: python3 headline_scorer.py "Your headline here" python3 headline_scorer.py --file headlines.txt python3 headline_scorer.py --json python3 headline_scorer.py # demo mode """ import argparse import json import re import sys # --------------------------------------------------------------------------- # Word lists # --------------------------------------------------------------------------- POWER_WORDS = { # urgency / scarcity "now", "today", "instantly", "immediately", "urgent", "limited", "exclusive", "last", "hurry", "deadline", "expires", "fast", # value / benefit "free", "save", "proven", "guaranteed", "results", "boost", "increase", "grow", "maximize", "unlock", "secret", "revealed", "transform", "master", "ultimate", "best", "top", "powerful", # curiosity / intrigue "discover", "uncover", "surprising", "shocking", "hidden", "unknown", "insider", "hack", "trick", "truth", # social proof / authority "experts", "researchers", "scientists", "officially", "certified", "award-winning", "world-class", # ease / simplicity "easy", "simple", "effortless", "quick", "step-by-step", "foolproof", "beginner", "without", # negative triggers (fear/loss) "avoid", "stop", "never", "mistake", "fail", "warning", "danger", "worst", "deadly", "risky", } EMOTIONAL_TRIGGERS = { "love", "hate", "fear", "hope", "joy", "pain", "anger", "envy", "trust", "doubt", "regret", "pride", "shame", "relief", "success", "failure", "happiness", "frustration", "excitement", "anxiety", "lonely", "powerful", "confident", "inspired", } JARGON_WORDS = { "synergy", "leverage", "disruptive", "paradigm", "scalable", "bandwidth", "circle back", "ping", "holistic", "ecosystem", "utilize", "facilitate", "ideate", "incentivize", "stakeholders", "deliverables", "actionable", "bespoke", "granular", "boil the ocean", "low-hanging fruit", "move the needle", "thought leader", "deep dive", } # --------------------------------------------------------------------------- # Scoring functions # --------------------------------------------------------------------------- def tokenize(headline: str) -> list: return re.findall(r"\b\w+(?:[-']\w+)*\b", headline.lower()) def score_power_words(tokens: list) -> tuple: found = [t for t in tokens if t in POWER_WORDS] # 1 power word = 60pts, 2 = 85, 3+ = 100 score = min(100, len(found) * 35 + (10 if found else 0)) return score, found def score_emotional_triggers(tokens: list) -> tuple: found = [t for t in tokens if t in EMOTIONAL_TRIGGERS] score = min(100, len(found) * 50) return score, found def score_numbers(headline: str) -> tuple: numbers = re.findall(r"\b\d+(?:[,\.]\d+)?%?\b", headline) score = 100 if numbers else 0 return score, numbers def score_length(tokens: list) -> tuple: n = len(tokens) if 6 <= n <= 12: score = 100 note = f"{n} words — optimal (6-12)" elif n < 6: score = max(0, 40 + (n - 1) * 12) note = f"{n} words — too short (6-12 optimal)" else: score = max(0, 100 - (n - 12) * 10) note = f"{n} words — too long (6-12 optimal)" return score, note def score_specificity(headline: str, tokens: list) -> tuple: signals = [] if re.search(r"\b\d+\b", headline): signals.append("contains number") if re.search(r"\b(in \d+|within \d+|\d+ days?|\d+ weeks?|\d+ months?|\d+ hours?|\d+ minutes?)\b", headline, re.I): signals.append("timeframe") if re.search(r"\b(how to|step|guide|checklist|strategy|system|framework|formula)\b", headline, re.I): signals.append("concrete format") if re.search(r"\b\d+%\b", headline): signals.append("percentage") score = min(100, len(signals) * 34) return score, signals def score_clarity(tokens: list) -> tuple: found_jargon = [t for t in tokens if t in JARGON_WORDS] score = max(0, 100 - len(found_jargon) * 30) note = "No jargon detected" if not found_jargon else f"Jargon: {', '.join(found_jargon)}" return score, note # --------------------------------------------------------------------------- # Aggregate # --------------------------------------------------------------------------- WEIGHTS = { "power_words": 0.25, "emotional_triggers": 0.15, "numbers": 0.15, "length": 0.20, "specificity": 0.15, "clarity": 0.10, } def score_headline(headline: str) -> dict: tokens = tokenize(headline) pw_score, pw_found = score_power_words(tokens) et_score, et_found = score_emotional_triggers(tokens) num_score, nums = score_numbers(headline) len_score, len_note = score_length(tokens) spec_score, spec_signals = score_specificity(headline, tokens) clar_score, clar_note = score_clarity(tokens) breakdown = { "power_words": {"score": pw_score, "found": pw_found, "weight": "25%"}, "emotional_triggers": {"score": et_score, "found": et_found, "weight": "15%"}, "numbers": {"score": num_score, "found": nums, "weight": "15%"}, "length": {"score": len_score, "note": len_note, "weight": "20%"}, "specificity": {"score": spec_score, "signals": spec_signals, "weight": "15%"}, "clarity": {"score": clar_score, "note": clar_note, "weight": "10%"}, } overall = round(sum( breakdown[k]["score"] * WEIGHTS[k] for k in WEIGHTS )) grade = "A" if overall >= 85 else "B" if overall >= 70 else "C" if overall >= 55 else "D" if overall >= 40 else "F" return { "headline": headline, "overall_score": overall, "grade": grade, "breakdown": breakdown, } # --------------------------------------------------------------------------- # Demo headlines # --------------------------------------------------------------------------- DEMO_HEADLINES = [ "10 Proven Ways to Double Your Email Open Rates in 30 Days", "Marketing Tips for Better Results", "Unlock the Secret Formula That Top Experts Use to Grow Revenue Fast", "How to Leverage Synergistic Paradigms for Scalable Growth", "Our New Product Is Now Available", ] # --------------------------------------------------------------------------- # Output helpers # --------------------------------------------------------------------------- def print_result(result: dict): h = result["headline"] score = result["overall_score"] grade = result["grade"] print(f"\n{'─' * 60}") print(f" Headline: {h}") print(f" Score: {score}/100 Grade: {grade}") print(f"{'─' * 60}") bd = result["breakdown"] rows = [ ("Power Words", "power_words", lambda r: f"found: {r['found'] or 'none'}"), ("Emotional Trigger", "emotional_triggers", lambda r: f"found: {r['found'] or 'none'}"), ("Numbers/Stats", "numbers", lambda r: f"found: {r['found'] or 'none'}"), ("Length", "length", lambda r: r["note"]), ("Specificity", "specificity", lambda r: f"signals: {r['signals'] or 'none'}"), ("Clarity", "clarity", lambda r: r["note"]), ] for label, key, detail_fn in rows: r = bd[key] bar_len = round(r["score"] / 10) bar = "█" * bar_len + "░" * (10 - bar_len) detail = detail_fn(r) print(f" {label:<20} [{bar}] {r['score']:>3}/100 {detail}") # --------------------------------------------------------------------------- # Main # --------------------------------------------------------------------------- def main(): parser = argparse.ArgumentParser( description="Headline scorer — rates headlines 0-100 across 6 dimensions." ) parser.add_argument("headline", nargs="?", help="Single headline to score") parser.add_argument("--file", help="Text file with one headline per line") parser.add_argument("--json", action="store_true", help="Output as JSON") args = parser.parse_args() if args.headline: headlines = [args.headline] elif args.file: with open(args.file, "r", encoding="utf-8") as f: headlines = [line.strip() for line in f if line.strip()] else: headlines = DEMO_HEADLINES if not args.json: print("No input provided — running in demo mode.\n") print("Demo headlines:") for h in headlines: print(f" • {h}") results = [score_headline(h) for h in headlines] if args.json: print(json.dumps(results, indent=2)) return for result in results: print_result(result) if len(results) > 1: avg = round(sum(r["overall_score"] for r in results) / len(results)) best = max(results, key=lambda r: r["overall_score"]) print(f"\n{'=' * 60}") print(f" {len(results)} headlines analyzed | Avg score: {avg}/100") print(f" Best: \"{best['headline'][:50]}\" ({best['overall_score']}/100)") print("=" * 60) if __name__ == "__main__": main()