Initial commit: The Ultimate Antigravity Skills Collection (58 Skills)
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sck_0
185
skills/content-creator/scripts/brand_voice_analyzer.py
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185
skills/content-creator/scripts/brand_voice_analyzer.py
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#!/usr/bin/env python3
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"""
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Brand Voice Analyzer - Analyzes content to establish and maintain brand voice consistency
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"""
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import re
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from typing import Dict, List, Tuple
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import json
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class BrandVoiceAnalyzer:
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def __init__(self):
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self.voice_dimensions = {
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'formality': {
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'formal': ['hereby', 'therefore', 'furthermore', 'pursuant', 'regarding'],
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'casual': ['hey', 'cool', 'awesome', 'stuff', 'yeah', 'gonna']
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},
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'tone': {
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'professional': ['expertise', 'solution', 'optimize', 'leverage', 'strategic'],
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'friendly': ['happy', 'excited', 'love', 'enjoy', 'together', 'share']
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},
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'perspective': {
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'authoritative': ['proven', 'research shows', 'experts agree', 'data indicates'],
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'conversational': ['you might', 'let\'s explore', 'we think', 'imagine if']
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}
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}
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def analyze_text(self, text: str) -> Dict:
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"""Analyze text for brand voice characteristics"""
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text_lower = text.lower()
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word_count = len(text.split())
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results = {
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'word_count': word_count,
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'readability_score': self._calculate_readability(text),
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'voice_profile': {},
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'sentence_analysis': self._analyze_sentences(text),
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'recommendations': []
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}
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# Analyze voice dimensions
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for dimension, categories in self.voice_dimensions.items():
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dim_scores = {}
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for category, keywords in categories.items():
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score = sum(1 for keyword in keywords if keyword in text_lower)
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dim_scores[category] = score
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# Determine dominant voice
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if sum(dim_scores.values()) > 0:
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dominant = max(dim_scores, key=dim_scores.get)
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results['voice_profile'][dimension] = {
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'dominant': dominant,
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'scores': dim_scores
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}
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# Generate recommendations
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results['recommendations'] = self._generate_recommendations(results)
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return results
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def _calculate_readability(self, text: str) -> float:
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"""Calculate Flesch Reading Ease score"""
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sentences = re.split(r'[.!?]+', text)
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words = text.split()
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syllables = sum(self._count_syllables(word) for word in words)
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if len(sentences) == 0 or len(words) == 0:
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return 0
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avg_sentence_length = len(words) / len(sentences)
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avg_syllables_per_word = syllables / len(words)
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# Flesch Reading Ease formula
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score = 206.835 - 1.015 * avg_sentence_length - 84.6 * avg_syllables_per_word
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return max(0, min(100, score))
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def _count_syllables(self, word: str) -> int:
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"""Count syllables in a word (simplified)"""
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word = word.lower()
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vowels = 'aeiou'
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syllable_count = 0
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previous_was_vowel = False
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for char in word:
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is_vowel = char in vowels
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if is_vowel and not previous_was_vowel:
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syllable_count += 1
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previous_was_vowel = is_vowel
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# Adjust for silent e
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if word.endswith('e'):
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syllable_count -= 1
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return max(1, syllable_count)
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def _analyze_sentences(self, text: str) -> Dict:
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"""Analyze sentence structure"""
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sentences = re.split(r'[.!?]+', text)
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sentences = [s.strip() for s in sentences if s.strip()]
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if not sentences:
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return {'average_length': 0, 'variety': 'low'}
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lengths = [len(s.split()) for s in sentences]
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avg_length = sum(lengths) / len(lengths) if lengths else 0
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# Calculate variety
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if len(set(lengths)) < 3:
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variety = 'low'
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elif len(set(lengths)) < 5:
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variety = 'medium'
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else:
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variety = 'high'
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return {
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'average_length': round(avg_length, 1),
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'variety': variety,
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'count': len(sentences)
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}
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def _generate_recommendations(self, analysis: Dict) -> List[str]:
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"""Generate recommendations based on analysis"""
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recommendations = []
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# Readability recommendations
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if analysis['readability_score'] < 30:
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recommendations.append("Consider simplifying language for better readability")
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elif analysis['readability_score'] > 70:
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recommendations.append("Content is very easy to read - consider if this matches your audience")
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# Sentence variety
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if analysis['sentence_analysis']['variety'] == 'low':
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recommendations.append("Vary sentence length for better flow and engagement")
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# Voice consistency
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if analysis['voice_profile']:
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recommendations.append("Maintain consistent voice across all content")
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return recommendations
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def analyze_content(content: str, output_format: str = 'json') -> str:
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"""Main function to analyze content"""
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analyzer = BrandVoiceAnalyzer()
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results = analyzer.analyze_text(content)
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if output_format == 'json':
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return json.dumps(results, indent=2)
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else:
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# Human-readable format
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output = [
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f"=== Brand Voice Analysis ===",
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f"Word Count: {results['word_count']}",
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f"Readability Score: {results['readability_score']:.1f}/100",
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f"",
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f"Voice Profile:"
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]
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for dimension, profile in results['voice_profile'].items():
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output.append(f" {dimension.title()}: {profile['dominant']}")
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output.extend([
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f"",
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f"Sentence Analysis:",
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f" Average Length: {results['sentence_analysis']['average_length']} words",
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f" Variety: {results['sentence_analysis']['variety']}",
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f" Total Sentences: {results['sentence_analysis']['count']}",
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f"",
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f"Recommendations:"
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])
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for rec in results['recommendations']:
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output.append(f" • {rec}")
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return '\n'.join(output)
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if __name__ == "__main__":
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import sys
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if len(sys.argv) > 1:
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with open(sys.argv[1], 'r') as f:
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content = f.read()
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output_format = sys.argv[2] if len(sys.argv) > 2 else 'text'
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print(analyze_content(content, output_format))
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else:
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print("Usage: python brand_voice_analyzer.py <file> [json|text]")
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419
skills/content-creator/scripts/seo_optimizer.py
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419
skills/content-creator/scripts/seo_optimizer.py
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#!/usr/bin/env python3
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"""
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SEO Content Optimizer - Analyzes and optimizes content for SEO
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"""
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import re
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from typing import Dict, List, Set
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import json
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class SEOOptimizer:
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def __init__(self):
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# Common stop words to filter
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self.stop_words = {
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'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for',
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'of', 'with', 'by', 'from', 'as', 'is', 'was', 'are', 'were', 'be',
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'been', 'being', 'have', 'has', 'had', 'do', 'does', 'did', 'will',
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'would', 'could', 'should', 'may', 'might', 'must', 'can', 'shall'
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}
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# SEO best practices
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self.best_practices = {
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'title_length': (50, 60),
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'meta_description_length': (150, 160),
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'url_length': (50, 60),
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'paragraph_length': (40, 150),
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'heading_keyword_placement': True,
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'keyword_density': (0.01, 0.03) # 1-3%
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}
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def analyze(self, content: str, target_keyword: str = None,
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secondary_keywords: List[str] = None) -> Dict:
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"""Analyze content for SEO optimization"""
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analysis = {
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'content_length': len(content.split()),
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'keyword_analysis': {},
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'structure_analysis': self._analyze_structure(content),
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'readability': self._analyze_readability(content),
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'meta_suggestions': {},
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'optimization_score': 0,
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'recommendations': []
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}
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# Keyword analysis
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if target_keyword:
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analysis['keyword_analysis'] = self._analyze_keywords(
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content, target_keyword, secondary_keywords or []
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)
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# Generate meta suggestions
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analysis['meta_suggestions'] = self._generate_meta_suggestions(
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content, target_keyword
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)
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# Calculate optimization score
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analysis['optimization_score'] = self._calculate_seo_score(analysis)
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# Generate recommendations
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analysis['recommendations'] = self._generate_recommendations(analysis)
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return analysis
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def _analyze_keywords(self, content: str, primary: str,
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secondary: List[str]) -> Dict:
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"""Analyze keyword usage and density"""
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content_lower = content.lower()
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word_count = len(content.split())
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results = {
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'primary_keyword': {
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'keyword': primary,
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'count': content_lower.count(primary.lower()),
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'density': 0,
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'in_title': False,
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'in_headings': False,
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'in_first_paragraph': False
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},
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'secondary_keywords': [],
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'lsi_keywords': []
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}
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# Calculate primary keyword metrics
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if word_count > 0:
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results['primary_keyword']['density'] = (
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results['primary_keyword']['count'] / word_count
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)
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# Check keyword placement
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first_para = content.split('\n\n')[0] if '\n\n' in content else content[:200]
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results['primary_keyword']['in_first_paragraph'] = (
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primary.lower() in first_para.lower()
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)
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# Analyze secondary keywords
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for keyword in secondary:
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count = content_lower.count(keyword.lower())
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results['secondary_keywords'].append({
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'keyword': keyword,
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'count': count,
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'density': count / word_count if word_count > 0 else 0
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})
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# Extract potential LSI keywords
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results['lsi_keywords'] = self._extract_lsi_keywords(content, primary)
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return results
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def _analyze_structure(self, content: str) -> Dict:
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"""Analyze content structure for SEO"""
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lines = content.split('\n')
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structure = {
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'headings': {'h1': 0, 'h2': 0, 'h3': 0, 'total': 0},
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'paragraphs': 0,
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'lists': 0,
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'images': 0,
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'links': {'internal': 0, 'external': 0},
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'avg_paragraph_length': 0
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}
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paragraphs = []
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current_para = []
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for line in lines:
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# Count headings
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if line.startswith('# '):
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structure['headings']['h1'] += 1
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structure['headings']['total'] += 1
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elif line.startswith('## '):
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structure['headings']['h2'] += 1
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structure['headings']['total'] += 1
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elif line.startswith('### '):
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structure['headings']['h3'] += 1
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structure['headings']['total'] += 1
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# Count lists
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if line.strip().startswith(('- ', '* ', '1. ')):
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structure['lists'] += 1
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# Count links
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internal_links = len(re.findall(r'\[.*?\]\(/.*?\)', line))
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external_links = len(re.findall(r'\[.*?\]\(https?://.*?\)', line))
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structure['links']['internal'] += internal_links
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structure['links']['external'] += external_links
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# Track paragraphs
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if line.strip() and not line.startswith('#'):
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current_para.append(line)
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elif current_para:
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paragraphs.append(' '.join(current_para))
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current_para = []
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if current_para:
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paragraphs.append(' '.join(current_para))
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structure['paragraphs'] = len(paragraphs)
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if paragraphs:
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avg_length = sum(len(p.split()) for p in paragraphs) / len(paragraphs)
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structure['avg_paragraph_length'] = round(avg_length, 1)
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return structure
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def _analyze_readability(self, content: str) -> Dict:
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"""Analyze content readability"""
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sentences = re.split(r'[.!?]+', content)
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words = content.split()
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if not sentences or not words:
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return {'score': 0, 'level': 'Unknown'}
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avg_sentence_length = len(words) / len(sentences)
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# Simple readability scoring
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if avg_sentence_length < 15:
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level = 'Easy'
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score = 90
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elif avg_sentence_length < 20:
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level = 'Moderate'
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score = 70
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elif avg_sentence_length < 25:
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level = 'Difficult'
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score = 50
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else:
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level = 'Very Difficult'
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score = 30
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return {
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'score': score,
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'level': level,
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'avg_sentence_length': round(avg_sentence_length, 1)
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}
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def _extract_lsi_keywords(self, content: str, primary_keyword: str) -> List[str]:
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"""Extract potential LSI (semantically related) keywords"""
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words = re.findall(r'\b[a-z]+\b', content.lower())
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word_freq = {}
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# Count word frequencies
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for word in words:
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if word not in self.stop_words and len(word) > 3:
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word_freq[word] = word_freq.get(word, 0) + 1
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# Sort by frequency and return top related terms
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sorted_words = sorted(word_freq.items(), key=lambda x: x[1], reverse=True)
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# Filter out the primary keyword and return top 10
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lsi_keywords = []
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for word, count in sorted_words:
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if word != primary_keyword.lower() and count > 1:
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lsi_keywords.append(word)
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if len(lsi_keywords) >= 10:
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break
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return lsi_keywords
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def _generate_meta_suggestions(self, content: str, keyword: str = None) -> Dict:
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"""Generate SEO meta tag suggestions"""
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# Extract first sentence for description base
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sentences = re.split(r'[.!?]+', content)
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first_sentence = sentences[0] if sentences else content[:160]
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suggestions = {
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'title': '',
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'meta_description': '',
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'url_slug': '',
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'og_title': '',
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'og_description': ''
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}
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if keyword:
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# Title suggestion
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suggestions['title'] = f"{keyword.title()} - Complete Guide"
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if len(suggestions['title']) > 60:
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suggestions['title'] = keyword.title()[:57] + "..."
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# Meta description
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desc_base = f"Learn everything about {keyword}. {first_sentence}"
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if len(desc_base) > 160:
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desc_base = desc_base[:157] + "..."
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suggestions['meta_description'] = desc_base
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# URL slug
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suggestions['url_slug'] = re.sub(r'[^a-z0-9-]+', '-',
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keyword.lower()).strip('-')
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# Open Graph tags
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suggestions['og_title'] = suggestions['title']
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suggestions['og_description'] = suggestions['meta_description']
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return suggestions
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def _calculate_seo_score(self, analysis: Dict) -> int:
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"""Calculate overall SEO optimization score"""
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score = 0
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max_score = 100
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# Content length scoring (20 points)
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if 300 <= analysis['content_length'] <= 2500:
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score += 20
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elif 200 <= analysis['content_length'] < 300:
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score += 10
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elif analysis['content_length'] > 2500:
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score += 15
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# Keyword optimization (30 points)
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if analysis['keyword_analysis']:
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kw_data = analysis['keyword_analysis']['primary_keyword']
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# Density scoring
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if 0.01 <= kw_data['density'] <= 0.03:
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score += 15
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elif 0.005 <= kw_data['density'] < 0.01:
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score += 8
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# Placement scoring
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if kw_data['in_first_paragraph']:
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score += 10
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if kw_data.get('in_headings'):
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score += 5
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# Structure scoring (25 points)
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struct = analysis['structure_analysis']
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if struct['headings']['total'] > 0:
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score += 10
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if struct['paragraphs'] >= 3:
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score += 10
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if struct['links']['internal'] > 0 or struct['links']['external'] > 0:
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score += 5
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# Readability scoring (25 points)
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readability_score = analysis['readability']['score']
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score += int(readability_score * 0.25)
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return min(score, max_score)
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def _generate_recommendations(self, analysis: Dict) -> List[str]:
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"""Generate SEO improvement recommendations"""
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recommendations = []
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# Content length recommendations
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if analysis['content_length'] < 300:
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recommendations.append(
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f"Increase content length to at least 300 words (currently {analysis['content_length']})"
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)
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elif analysis['content_length'] > 3000:
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recommendations.append(
|
||||
"Consider breaking long content into multiple pages or adding a table of contents"
|
||||
)
|
||||
|
||||
# Keyword recommendations
|
||||
if analysis['keyword_analysis']:
|
||||
kw_data = analysis['keyword_analysis']['primary_keyword']
|
||||
|
||||
if kw_data['density'] < 0.01:
|
||||
recommendations.append(
|
||||
f"Increase keyword density for '{kw_data['keyword']}' (currently {kw_data['density']:.2%})"
|
||||
)
|
||||
elif kw_data['density'] > 0.03:
|
||||
recommendations.append(
|
||||
f"Reduce keyword density to avoid over-optimization (currently {kw_data['density']:.2%})"
|
||||
)
|
||||
|
||||
if not kw_data['in_first_paragraph']:
|
||||
recommendations.append(
|
||||
"Include primary keyword in the first paragraph"
|
||||
)
|
||||
|
||||
# Structure recommendations
|
||||
struct = analysis['structure_analysis']
|
||||
if struct['headings']['total'] == 0:
|
||||
recommendations.append("Add headings (H1, H2, H3) to improve content structure")
|
||||
if struct['links']['internal'] == 0:
|
||||
recommendations.append("Add internal links to related content")
|
||||
if struct['avg_paragraph_length'] > 150:
|
||||
recommendations.append("Break up long paragraphs for better readability")
|
||||
|
||||
# Readability recommendations
|
||||
if analysis['readability']['avg_sentence_length'] > 20:
|
||||
recommendations.append("Simplify sentences for better readability")
|
||||
|
||||
return recommendations
|
||||
|
||||
def optimize_content(content: str, keyword: str = None,
|
||||
secondary_keywords: List[str] = None) -> str:
|
||||
"""Main function to optimize content"""
|
||||
optimizer = SEOOptimizer()
|
||||
|
||||
# Parse secondary keywords from comma-separated string if provided
|
||||
if secondary_keywords and isinstance(secondary_keywords, str):
|
||||
secondary_keywords = [kw.strip() for kw in secondary_keywords.split(',')]
|
||||
|
||||
results = optimizer.analyze(content, keyword, secondary_keywords)
|
||||
|
||||
# Format output
|
||||
output = [
|
||||
"=== SEO Content Analysis ===",
|
||||
f"Overall SEO Score: {results['optimization_score']}/100",
|
||||
f"Content Length: {results['content_length']} words",
|
||||
f"",
|
||||
"Content Structure:",
|
||||
f" Headings: {results['structure_analysis']['headings']['total']}",
|
||||
f" Paragraphs: {results['structure_analysis']['paragraphs']}",
|
||||
f" Avg Paragraph Length: {results['structure_analysis']['avg_paragraph_length']} words",
|
||||
f" Internal Links: {results['structure_analysis']['links']['internal']}",
|
||||
f" External Links: {results['structure_analysis']['links']['external']}",
|
||||
f"",
|
||||
f"Readability: {results['readability']['level']} (Score: {results['readability']['score']})",
|
||||
f""
|
||||
]
|
||||
|
||||
if results['keyword_analysis']:
|
||||
kw = results['keyword_analysis']['primary_keyword']
|
||||
output.extend([
|
||||
"Keyword Analysis:",
|
||||
f" Primary Keyword: {kw['keyword']}",
|
||||
f" Count: {kw['count']}",
|
||||
f" Density: {kw['density']:.2%}",
|
||||
f" In First Paragraph: {'Yes' if kw['in_first_paragraph'] else 'No'}",
|
||||
f""
|
||||
])
|
||||
|
||||
if results['keyword_analysis']['lsi_keywords']:
|
||||
output.append(" Related Keywords Found:")
|
||||
for lsi in results['keyword_analysis']['lsi_keywords'][:5]:
|
||||
output.append(f" • {lsi}")
|
||||
output.append("")
|
||||
|
||||
if results['meta_suggestions']:
|
||||
output.extend([
|
||||
"Meta Tag Suggestions:",
|
||||
f" Title: {results['meta_suggestions']['title']}",
|
||||
f" Description: {results['meta_suggestions']['meta_description']}",
|
||||
f" URL Slug: {results['meta_suggestions']['url_slug']}",
|
||||
f""
|
||||
])
|
||||
|
||||
output.extend([
|
||||
"Recommendations:",
|
||||
])
|
||||
|
||||
for rec in results['recommendations']:
|
||||
output.append(f" • {rec}")
|
||||
|
||||
return '\n'.join(output)
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
|
||||
if len(sys.argv) > 1:
|
||||
with open(sys.argv[1], 'r') as f:
|
||||
content = f.read()
|
||||
|
||||
keyword = sys.argv[2] if len(sys.argv) > 2 else None
|
||||
secondary = sys.argv[3] if len(sys.argv) > 3 else None
|
||||
|
||||
print(optimize_content(content, keyword, secondary))
|
||||
else:
|
||||
print("Usage: python seo_optimizer.py <file> [primary_keyword] [secondary_keywords]")
|
||||
Reference in New Issue
Block a user