data-scraper-agent

Automate public data scraping by configuring a GitHub Actions-based schedule to collect data from job boards or news feeds. The agent runs the scraping tasks automatically at set intervals and persists results to your centralized store.

AI enrichment in web scraping applies a model like Gemini Flash to collected data to extract relevance or add context. It processes scraped records in batches, enhancing raw public data before persisting it to your storage system.

Yes, you can store scraped web data directly into Notion, Google Sheets, or Supabase. The collection pipeline supports persisting enriched records to these destinations automatically after the scraping and processing steps complete.

Yes, the data scraper deduplicates URLs when collecting from diverse public sources. This prevents redundant entries in your centralized store, ensuring only unique records are enriched and persisted across scraping runs.

The best way to scrape pricing pages and GitHub repositories without manual effort is using a three-layer architecture: Collect, Enrich, and Store. This automates extraction, applies AI enrichment, and saves data to your destination.

Using free-stack components like requests and BeautifulSoup for data scraping limits you to public data sources without JavaScript rendering. This approach relies on static HTML parsing and may not handle dynamic, client-side rendered content.