data-analysis

Use when the user wants an iterative, self-checking exploratory analysis of a dataset — surfacing findings that are each verified by re-running the computation, not asserted. Proposes one specific hypothesis at a time, writes and runs analysis code to test it, and records the finding only if the numbers support it at a meaningful effect size; loops until no new verified finding appears or the budget is hit. The result is a findings report where every claim is backed by a reproducible number. Not for diagnosing a single known anomaly or pipeline failure, and not for verifying an external claim against sources (that is a claim-verification task) — this is open-ended discovery over a bound dataset.