universal-scraping

You can maintain recurring scraping workflows by binding sources, scripts, and runtime state into durable, queryable artifacts. This approach persists findings into SQLite-backed runtime state, enabling long-running, auditable data extraction without rebuilding pipelines each time.

Data extraction drift repair applies target revisioning and run recording within a formal API surface. This coordinates end-to-end extraction across websites, APIs, feeds, and documents, using semantic templates to manage and repair drifting targets.

Initialize a new scraping target by registering its scripts and sources, then performing the initial scrape. This creates a mutable runtime workspace under runtime/scraping/targets/ and records the first run, establishing the target's API scaffolds and evidence storage.

Yes, scraping workflow approaches support scheduling, revisioning, and evidence storage to support end-to-end scrape lifecycles. They satisfy target upsertion, script and source revisioning, run recording, and artifact metadata management across multiple targets and sources.

Yes, you can use semantic templates for managing multiple data extraction targets. The system applies template-based artifact management alongside scheduling and revisioning, supporting end-to-end scrape lifecycles across multiple targets and sources concurrently.