Signal Pipeline

A marketing intelligence pipeline that aggregates signals from multiple sources, stores them in SQLite, and generates content for personal branding.

What It Does

• RSS feeds → SQLite database (rss_db.py)
• X/Twitter → SQLite database (x_monitor.py)
• Telegram channels → SQLite database (telegram_monitor.py)
• Gmail newsletters → Signal extraction (newsletter_monitor.py)
• Daily signals → Draft posts
• Weekly synthesis → Theme analysis
• Monthly deep-dive → Essay/book chapter

Files Included

signal-pipeline/
├── SKILL.md              # This file
├── README.md             # Setup instructions
├── requirements.txt      # Python dependencies
├── daily_signals.py      # Main script (daily/weekly/monthly)
├── rss_db.py           # RSS feed storage
├── x_monitor.py        # X/Twitter monitoring
├── telegram_monitor.py  # Telegram channel scraping
└── newsletter_monitor.py # Gmail newsletter extraction

Quick Start

# Install dependencies
cd skills/signal-pipeline
pip install -r requirements.txt

# Run daily signals
python daily_signals.py

# Generate weekly summary
python daily_signals.py --weekly

# Generate monthly report
python daily_signals.py --monthly

Configuration

RSS Feeds

new_feeds = [
    ('Feed Name', 'https://example.com/feed.xml'),
]

Telegram Channels

CHANNELS = ['channel_name_1', 'channel_name_2']

X Accounts

MONITOR_URLS = [
    'https://x.com/username/status/123456789',
]

Gmail Newsletters

gog gmail search 'newer_than:30d label:newsletter'

Requirements

• Python 3.8+
• feedparser>=6.0.0
• beautifulsoup4>=4.12.0
• requests>=2.31.0
• httpx>=0.25.0

Database

Three SQLite databases are created:

• rss_db.db - RSS articles
• x_monitor.db - X/Twitter data
• telegram_db.db - Telegram posts

Use Cases

• Content Creation - Daily signals for X/LinkedIn posts
• Market Research - Track industry trends
• Competitive Intelligence - Monitor competitors
• Personal Branding - Build content streak
• Book Writing - Compile monthly insights

Author

Open source - free to use and modify.