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No API keys required. Reddit scraping uses public .json endpoints.

---

from pytrends.request import TrendReq
import time

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from pytrends.request import TrendReq
import time

pytrends = TrendReq(hl='en-US', tz=330)

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from pytrends.request import TrendReq
import time

pytrends = TrendReq(hl='en-US', tz=330)

def find_breakout_topics(keyword, geo=''):
    """Find topics with explosive growth (potential viral content)"""
    pytrends.build_payload([keyword], timeframe='today 3-m', geo=geo)
    time.sleep(3)  # Rate limiting
    
    related = pytrends.related_queries()
    rising = related[keyword]['rising']
    
    if rising is not None:
        # Filter for breakout topics (marked as "Breakout" or very high %)
        breakouts = rising[rising['value'] >= 1000]  # 1000%+ growth
        return breakouts
    return None

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import time

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import requests
import time

headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'}

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import requests
import time

headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'}

    for child in data.get('data', {}).get('children', [])[:5]:
        post = child.get('data', {})
        print(f"- [{post.get('score')}] {post.get('title')[:60]}...")
except Exception as e:
    print(f"Error: {e}")

time.sleep(3)  # Rate limiting between requests

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from scripts.reddit_scraper import SimpleRedditScraper

scraper = SimpleRedditScraper()

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import time

---

"What are the most discussed [YOUR NICHE] topics on Twitter/X this week? 
Include specific examples of viral tweets and their engagement."

"What [YOUR NICHE] content is trending on TikTok right now? 
Include hashtags, view counts, and content formats that are working."

"What [YOUR NICHE] videos are getting the most views on YouTube this week? 
Include channel names, view counts, and video topics."

"What [YOUR NICHE] topics are professionals discussing on LinkedIn this week? 
Include examples of high-engagement posts."

"What [YOUR NICHE] content has gone viral across social media in the past 7 days? 
Include platform, format, and why it resonated."

python scripts/perplexity_search.py \
  "What cardiology topics are trending on Twitter and TikTok this week? Include specific viral posts and hashtags." \
  --model sonar-pro

from pytrends.request import TrendReq
import requests
import time
import json
from datetime import datetime

class TrendResearcher:
    def __init__(self):
        self.pytrends = TrendReq(hl='en-US', tz=330)
        self.reddit_headers = {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
        }
    
    def _reddit_request(self, url):
        """Make a Reddit API request."""
        try:
            response = requests.get(url, headers=self.reddit_headers, timeout=10)
            response.raise_for_status()
            return response.json()
        except Exception as e:
            return {'error': str(e)}
    
    def research_niche(self, keywords, subreddits=None, geo='IN'):
        """
        Complete trend research for a niche.
        
        Args:
            keywords: List of keywords (max 5)
            subreddits: List of subreddit names to monitor
            geo: Geographic region code
        
        Returns:
            Dictionary with all research data
        """
        results = {
            'timestamp': datetime.now().isoformat(),
            'keywords': keywords,
            'google_trends': {},
            'reddit': {},
            'recommendations': []
        }
        
        # 1. Google Trends - Interest Over Time
        print("📊 Fetching Google Trends data...")
        try:
            self.pytrends.build_payload(keywords[:5], timeframe='now 7-d', geo=geo)
            results['google_trends']['interest'] = self.pytrends.interest_over_time().to_dict()
            time.sleep(5)
            
            # Related queries (rising topics)
            related = self.pytrends.related_queries()
            results['google_trends']['rising_queries'] = {}
            for kw in keywords[:5]:
                rising = related[kw]['rising']
                if rising is not None:
                    results['google_trends']['rising_queries'][kw] = rising.head(10).to_dict()
            time.sleep(5)
        except Exception as e:
            results['google_trends']['error'] = str(e)
        
        # 2. Reddit Research
        print("👽 Fetching Reddit discussions...")
        if subreddits:
            for sub in subreddits[:5]:
                try:
                    url = f"https://www.reddit.com/r/{sub}/hot.json?limit=10"
                    data = self._reddit_request(url)
                    posts = []
                    for child in data.get('data', {}).get('children', [])[:5]:
                        post = child.get('data', {})
                        posts.append({
                            'title': post.get('title', ''),
                            'score': post.get('score', 0),
                            'comments': post.get('num_comments', 0)
                        })
                    results['reddit'][sub] = posts
                    time.sleep(3)
                except Exception as e:
                    results['reddit'][sub] = {'error': str(e)}
        
        # 3. Keyword search on Reddit
        print("🔍 Searching Reddit for keywords...")
        for kw in keywords[:3]:
            try:
                url = f"https://www.reddit.com/search.json?q={kw}&limit=10&sort=relevance&t=week"
                data = self._reddit_request(url)
                posts = []
                for child in data.get('data', {}).get('children', [])[:5]:
                    post = child.get('data', {})
                    posts.append({
                        'title': post.get('title', ''),
                        'subreddit': post.get('subreddit', ''),
                        'score': post.get('score', 0),
                        'comments': post.get('num_comments', 0)
                    })
                results['reddit'][f'search_{kw}'] = posts
                time.sleep(3)
            except Exception as e:
                results['reddit'][f'search_{kw}'] = {'error': str(e)}
        
        # 4. Generate recommendations
        results['recommendations'] = self._generate_recommendations(results)
        
        return results
    
    def _generate_recommendations(self, data):
        """Generate content recommendations from research data"""
        recommendations = []
        
        # From rising queries
        rising = data.get('google_trends', {}).get('rising_queries', {})
        for kw, queries in rising.items():
            if isinstance(queries, dict) and 'query' in queries:
                for query in list(queries['query'].values())[:3]:
                    recommendations.append({
                        'source': 'Google Trends',
                        'topic': query,
                        'reason': f"Rising search term related to '{kw}'"
                    })
        
        # From Reddit hot posts
        for sub, posts in data.get('reddit', {}).items():
            if isinstance(posts, list):
                for post in posts[:2]:
                    if post.get('score', 0) > 50:
                        recommendations.append({
                            'source': f'Reddit r/{sub}',
                            'topic': post.get('title', ''),
                            'reason': f"High engagement ({post.get('score')} upvotes)"
                        })
        
        return recommendations

results = researcher.research_niche(
    keywords=['heart health', 'cardiology', 'cholesterol'],
    subreddits=['cardiology', 'health', 'medicine'],
    geo='IN'
)

# Save results
with open('trend_research.json', 'w') as f:
    json.dump(results, f, indent=2, default=str)

# Print recommendations
print("\n🎯 CONTENT RECOMMENDATIONS:")
for rec in results['recommendations']:
    print(f"- [{rec['source']}] {rec['topic']}")
    print(f"  Why: {rec['reason']}")

---

from pytrends.request import TrendReq
import requests
import time

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---

1. Run trend research (this skill)
2. Identify top 3-5 opportunities
3. Use content-marketing-social-listening for strategy
4. Use cardiology-content-repurposer or similar for content creation
5. Use authentic-voice for final polish