data-scraper-agent

Automate data scraping by orchestrating a Python-based agent on GitHub Actions to collect, enrich, and store public data on a schedule entirely for free. This pipeline eliminates manual extraction and hosting fees by running in the open.

You can enrich scraped data using Gemini Flash before storing it in Notion, Google Sheets, or Supabase. The pipeline automatically scores and processes collected information, adding AI-driven insights directly into your preferred storage destination.

The best way to track jobs, prices, and news is using a three-layer pipeline that collects, enriches, and stores data on a schedule. This automated approach ensures your trackers remain up-to-date without manual intervention.

You need a Python-based stack including requests, bs4, and Gemini Flash to run the data collection pipeline. GitHub Actions provides the free, production-ready scheduling environment required to execute the agent without hosting costs.

User feedback improves web scraping accuracy by teaching the agent to better prioritize and extract relevant data. The pipeline learns from your inputs to continuously refine its collection and enrichment logic for future runs.

Using GitHub Actions for scheduled data scraping limits you to public source collection and its execution time constraints. The pipeline is designed for monitoring public data like jobs, prices, and repos rather than private or authenticated sources.