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)

| Username | Followers | Domain | Tags | |----------|-----------|--------|------| | thedankoe | 804K | Personal growth / creator economy | writing, philosophy | | levelsio | 795K | Indie Hacker | nomad, shipping, bootstrapped | | naval | 3M | Philosophy / investing | philosophy, wealth, startups | | thejustinwelsh | 560K | Solopreneur | linkedin, one-person-business | | AlexHormozi | 1M | Business / fitness | offers, sales | | ShaanVP | 458K | Podcast / startups | ideas, newsletter | | arvidkahl | 188K | Bootstrapping | audience, zero-to-sold | | tibo_maker | 178K | Maker | product, distribution | | tdinh_me | 176K | Indie Hacker | saas, products | | noahkagan | 170K | Marketing | growth, appsumo | | dannypostma | 167K | AI Products | maker, ai-tools | | yongfook | 147K | SaaS | bootstrapped, design | | nathanbarry | 146K | Creator Economy | newsletters, convertkit | | thepatwalls | 134K | Startups | interviews, stories | | csallen | 68K | Community | indie-hackers, founder |

Tier 2: Tech / AI Thought Leaders

| Username | Followers | Domain | Tags | |----------|-----------|--------|------| | karpathy | 1.6M | AI education | deep-learning, youtube | | lexfridman | 4.5M | Podcast / AI | interviews, research | | ylecun | 1M | AI Research | meta, deep-learning | | DrJimFan | 351K | AI Research | embodied-ai, nvidia | | lilianweng | 183K | AI Research | openai, blog | | mattpocockuk | 230K | TypeScript | tutorials, ts |

Tier 3: Writing / Content

| Username | Followers | Domain | Tags | |----------|-----------|--------|------| | SahilBloom | 1M+ | Writing / investing | threads, frameworks | | JamesClear | 2M+ | Habits / productivity | habits, atomic | | dickiebush | 400K | Writing instruction | writing, ship30 | | david_perell | 500K | Writing | essays |

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

| Frequency | Creators | Why | |-----------|----------|-----| | Weekly | thedankoe, naval, SahilBloom | High volume, trending content | | Bi-weekly | AlexHormozi, JamesClear | Medium volume | | Monthly | Others | Reference / archive |

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:

Research first — Check follower count, content quality, posting frequency
Test scrape — python twitter_scraper.py <username> --limit 50
Add to watchlist — Update the table in this file with tier, follower count, domain, tags
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]":

Query database for that creator's recent tweets
Find top-performing content (sorted by likes)
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
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":

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

Current Status Tracking

Track scrape status in this format:

| Username | Status | Tweets | Last Scraped | |----------|--------|--------|--------------| | @thedankoe | Done | 200 | 2026-01-27 | | @levelsio | Done | 200 | 2026-02-05 | | @naval | Done | 200 | 2026-02-05 | | @thejustinwelsh | Done | 200 | 2026-02-05 | | @AlexHormozi | Partial | 18 | 2026-02-05 (rate limited) | | @karpathy | Pending | — | — |

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:

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