Programmatic trend research using three free tools:
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsocial-media-trends-researchExecute the skills CLI command in your project's root directory to begin installation:
Fetches social-media-trends-research from drshailesh88/integrated_content_os and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate social-media-trends-research. Access via /social-media-trends-research in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Programmatic trend research using three free tools:
This skill provides executable code for trend research. Use alongside content-marketing-social-listening for strategy and perplexity-search for deep queries.
# Install dependencies (one-time)
pip install pytrends requests --break-system-packages
No API keys required. Reddit scraping uses public .json endpoints.
from pytrends.request import TrendReq
import time
# Initialize (no API key needed)
pytrends = TrendReq(hl='en-US', tz=330) # tz=330 for India (IST)
# Get real-time trending searches
trending = pytrends.trending_searches(pn='india')
print(trending.head(20))
from pytrends.request import TrendReq
import time
pytrends = TrendReq(hl='en-US', tz=330)
# Define your niche keywords (max 5 per request)
keywords = ['heart health', 'cardiology', 'cholesterol']
# Build payload
pytrends.build_payload(keywords, timeframe='now 7-d', geo='IN')
# Get interest over time
interest = pytrends.interest_over_time()
print(interest)
# CRITICAL: Wait between requests to avoid rate limiting
time.sleep(3)
# Get related queries (THIS IS GOLD - shows rising topics)
related = pytrends.related_queries()
for kw in keywords:
print(f"\n=== Rising queries for '{kw}' ===")
rising = related[kw]['rising']
if rising is not None:
print(rising.head(10))
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
# Example usage
breakouts = find_breakout_topics('heart health', geo='IN')
print(breakouts)
import time
# SAFE: 1 request per 3-5 seconds for casual use
time.sleep(5)
# BULK RESEARCH: 1 request per 60 seconds
time.sleep(60)
# If you get rate limited (429 error): Wait 60-120 seconds, then continue
# If persistent issues: Wait 4-6 hours before resuming
| Timeframe | Use Case |
|---|---|
'now 1-H' |
Last hour (real-time spikes) |
'now 4-H' |
Last 4 hours |
'now 1-d' |
Last 24 hours |
'now 7-d' |
Last 7 days (best for trends) |
'today 1-m' |
Last 30 days |
'today 3-m' |
Last 90 days (velocity analysis) |
'today 12-m' |
Last year (seasonal patterns) |
import requests
import time
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'}
# Search Reddit for your niche
url = "https://www.reddit.com/search.json?q=heart+health&limit=10&sort=relevance&t=week"
response = requests.get(url, headers=headers, timeout=10)
data = response.json()
# Display results
for child in data.get('data', {}).get('children', []):
post = child.get('data', {})
print(f"Title: {post.get('title')}")
print(f"Subreddit: r/{post.get('subreddit')}")
print(f"Score: {post.get('score')}")
print("---")
import requests
import time
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'}
# Define subreddits relevant to your niche
subreddits = ['cardiology', 'health', 'medicine']
for sub in subreddits:
print(f"\n=== Hot in r/{sub} ===")
try:
url = f"https://www.reddit.com/r/{sub}/hot.json?limit=10"
response = requests.get(url, headers=headers, timeout=10)
data = response.json()
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 eMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
social-media-trends-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
social-media-trends-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in social-media-trends-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
social-media-trends-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
social-media-trends-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
social-media-trends-research is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
social-media-trends-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for social-media-trends-research matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: social-media-trends-research is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added social-media-trends-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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