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Pipeline reconnaissance locates scrapers, schemas, warehouse tables, and dbt models to flag missing dbt marts, unmapped values, and stale last-run dates for a named data concept. It inventories the full pipeline to summarize completeness and staleness without modifying any state.

Data pipeline gap detection identifies what exists, what is missing, and what is stale across scrapers, warehouse tables, and SQL models. You need it to prioritize data engineering work and plan next steps safely without guessing about resource availability.

Yes, pipeline reconnaissance performs read-only operations using CLI, SQL, git, and filesystem checks. It requires read-only access to the soria CLI, filesystem, and git, and runs SQL queries against the warehouse to ensure no state is modified.

Inventory uncommitted changes and unmapped values by running environment-aware reconnaissance that locates value mappings, frontend dive artifacts, and warehouse tables. It detects uncommitted dive changes and unmapped values to produce a reproducible artifact for planning workflows.

Yes, environment-aware pipeline reconnaissance reports state scoped to the active soria environment and explicitly warns if the user is not in the intended environment. It inspects groups, schemas, and extractors within that specific environment context to ensure accurate findings.

The best way to audit staleness is running read-only reconnaissance that flags last-run dates, unmaterialized tables, and missing dbt marts. It summarizes pipeline completeness and staleness across files, schemas, and dive artifacts to inform safe next steps.