Declarative API for Staged Survey Weights // Builds survey weights from design base weights by chaining the stages of a weighting workflow (unknown-eligibility redistribution, nonresponse adjustment, calibration to known population totals, and weight trimming) through a declarative, pipeable, 'tidymodels'-style API, with nonresponse handled by weighting classes, by response-propensity models fitted with logistic regression or machine-learning learners (trees, random forests and gradient boosting), or by calibration. Calibration follows Deville and Sarndal (1992) doi:10.2307/2290268, and a range- restricted variant trims the weights into a fixed interval while preserving the calibration totals, following the generalized exponential method of Folsom and Singh (2000). Variances are obtained with a recipe-aware bootstrap and jackknife that resample primary sampling units and re-apply the whole cascade on each replicate, following the rescaling bootstrap of Rao and Wu (1988) doi:10.1080/01621459.1988.10478591, so the replicate weights carry the variability of every adjustment. A self-contained HTML report documents each step with diagnostics, and the weights bridge to the 'survey' and 'srvyr' packages for design- based inference.