Creator Watchlist Skill

Manage a curated list of Twitter/X creators worth learning from. Batch scrape their content, import to a database, and extract patterns from high-performing posts.

Watchlist

Tier 1: Solopreneur / Indie Hacker (Priority)

Tier 2: Tech / AI Thought Leaders

Tier 3: Writing / Content

Quick Commands

Scrape One Creator

# Using rnet_twitter.py (async Twitter GraphQL client)
python twitter_scraper.py <username> --limit 200
python db_import.py storage/twitter/<username>.json

Scrape Batch by Tier

# Tier 1: Solopreneur / Indie Hacker (Priority)
TIER1="thedankoe levelsio naval thejustinwelsh AlexHormozi ShaanVP arvidkahl tibo_maker tdinh_me noahkagan dannypostma yongfook nathanbarry thepatwalls csallen"

# Tier 2: Tech / AI
TIER2="karpathy lexfridman ylecun DrJimFan lilianweng"

# Tier 3: Writing
TIER3="SahilBloom JamesClear dickiebush david_perell"

for user in $TIER1; do
  echo "=== Scraping @$user ==="
  python twitter_scraper.py $user --limit 200
  python db_import.py storage/twitter/$user.json
  echo "Waiting 30s to avoid rate limit..."
  sleep 30
done

Scraping Schedule

Database Schema

CREATE TABLE twitter_content (
  id TEXT PRIMARY KEY,
  username TEXT NOT NULL,
  text TEXT,
  created_at TIMESTAMPTZ,
  likes INTEGER DEFAULT 0,
  retweets INTEGER DEFAULT 0,
  replies INTEGER DEFAULT 0,
  views BIGINT,
  url TEXT,
  is_retweet BOOLEAN DEFAULT FALSE,
  imported_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX idx_twitter_content_username ON twitter_content(username);

Analysis Queries

Top Performing Tweets by Likes

SELECT username, text, likes, retweets, url
FROM twitter_content
WHERE likes > 1000
ORDER BY likes DESC
LIMIT 20;

Engagement Rate by Creator

SELECT
  username,
  COUNT(*) as tweets,
  AVG(likes) as avg_likes,
  AVG(retweets) as avg_retweets,
  MAX(likes) as top_tweet_likes
FROM twitter_content
GROUP BY username
ORDER BY avg_likes DESC;

Find Viral Threads (High Engagement)

SELECT username, text, likes, retweets, views, url
FROM twitter_content
WHERE likes > 5000
  AND text NOT LIKE 'RT @%'
ORDER BY likes DESC;

Content Patterns (Time Analysis)

SELECT
  EXTRACT(DOW FROM created_at) as day_of_week,
  EXTRACT(HOUR FROM created_at) as hour,
  AVG(likes) as avg_likes
FROM twitter_content
GROUP BY day_of_week, hour
ORDER BY avg_likes DESC
LIMIT 10;

Topic Frequency (Simple Text Search)

-- Find posts mentioning a topic
SELECT username, text, likes, url
FROM twitter_content
WHERE text ILIKE '%indie hacker%'
ORDER BY likes DESC
LIMIT 20;

Adding New Creators

To add a creator to the watchlist:

1. Research first — Check follower count, content quality, posting frequency
2. Test scrape — python twitter_scraper.py <username> --limit 50
3. Add to watchlist — Update the table in this file with tier, follower count, domain, tags
4. Import — python db_import.py storage/twitter/<username>.json

Criteria for Watchlist

• 100K+ followers (established audience)
• Consistent posting (at least weekly)
• High engagement rate (likes/followers > 0.1%)
• Content relevant to: creator economy, solopreneurship, personal growth, writing, AI, or technical building

Analysis Workflow

When user says "analyze [creator]":

1. Query database for that creator's recent tweets
2. Find top-performing content (sorted by likes)
3. Identify patterns:
   • Topics: What subjects get the most engagement?
   • Formats: Threads vs single tweets vs lists
   • Timing: What day/hour performs best?
   • Hook types: Questions, contrarian takes, stats, stories
4. Summarize insights with specific examples

Analysis output format:

## @[username] Content Analysis

### Top Performing Formats
1. [Format] — avg [X] likes
2. [Format] — avg [X] likes

### Highest Engagement Topics
1. [Topic] — [N] posts, avg [X] likes
2. [Topic] — [N] posts, avg [X] likes

### Best Posting Times
- [Day] at [Hour] — avg [X] likes

### Hook Patterns That Work
- [Hook type]: "[example]" ([X] likes)
- [Hook type]: "[example]" ([X] likes)

### Key Takeaways
- [Insight 1]
- [Insight 2]
- [Insight 3]

When user says "update creator database":

1. Check which creators have not been scraped recently (> 7 days for Tier 1)
2. Run scrape for each with 30-second delay between users
3. Import each to database
4. Report summary: creators updated, tweets imported, any errors

Current Status Tracking

Track scrape status in this format:

Status values:

• Done — Full scrape completed
• Partial — Rate limited, partial data
• Pending — Not yet scraped
• Error — Scrape failed, needs investigation

Rate Limiting Strategy

Twitter/X has aggressive rate limiting. Follow these rules:

• 30-second delay between user scrapes in batch mode
• Max 10 users per scraping session
• If rate limited mid-batch, wait 15 minutes before continuing
• Prefer scraping during off-peak hours (early morning or late night)
• Do not run two scraping sessions simultaneously

Cross-Creator Pattern Analysis

To find patterns across the entire watchlist:

-- What topics go viral across all creators?
SELECT
  -- extract common words/phrases manually or with NLP
  username,
  text,
  likes
FROM twitter_content
WHERE likes > 5000
  AND is_retweet = FALSE
ORDER BY likes DESC
LIMIT 50;

-- Which creator has the most consistent engagement?
SELECT
  username,
  AVG(likes) as avg_likes,
  STDDEV(likes) as stddev_likes,
  AVG(likes) / NULLIF(STDDEV(likes), 0) as consistency_score
FROM twitter_content
GROUP BY username
ORDER BY consistency_score DESC;

Insight Applications

Use creator analysis to:

1. Content inspiration — Find angles that work in your niche
2. Hook formulas — Extract winning opening lines to adapt
3. Posting timing — Learn optimal days/hours for your audience
4. Topic validation — See which topics get traction before writing
5. Competitive intelligence — Understand what resonates in your market