data-scraper-agent

Automated data scraping on a schedule persists enriched results directly to Supabase. You configure Python-based scraping scripts to run as scheduled GitHub Actions, collecting public data and storing it in your Supabase database without manual intervention.

AI enrichment of scraped data is supported before saving to Notion or Sheets. The agent uses Gemini Flash to score, summarize, and classify collected records, ensuring your persisted data in Notion or Google Sheets is processed and structured.

GitHub Actions supports running automated web scraping jobs entirely for free. The agent's Python-based scraping processes are designed to execute within GitHub Actions, enabling scheduled monitoring, price tracking, and data collection without infrastructure costs.

The best way to track prices and monitor listings without hosting fees is running Python scrapers on GitHub Actions. This approach automates public data collection on a schedule, applies AI enrichment via Gemini Flash, and stores results in Notion or Supabase.

Scraping GitHub repositories and sports scores automatically is supported through Python-based collection. The agent automates end-to-end public data collection from diverse sources, including repository data and sports scores, on scheduled runs.

A data collection agent that learns from user feedback adapts its enrichment logic over time. After scraping and storing results in Notion, Sheets, or Supabase, the agent incorporates user feedback to refine future AI-powered data collection runs.