additional_features
["Reproducible Python code snippets and transparent diagnostics (flags for missing inputs, unstable weights, and high CV domains)."]
example_commands
["Here is my SamplingPlanner design_summary.json and sample.csv—execute the sampling steps and output the selected sample plus base weights.", "Compute final weights using this design summary and these benchmark totals (benchmarks.xlsx); include nonresponse adjustment and raking factors.", "Generate CV/SE/DEFF for domain means by region using strata=STRATA, psu=PSU, weight=final_weight; export detailed tables to Excel.", "Validate my SDM.yaml against the SDM template and list all missing or conf
gpt_id
g-693b5a8cede08191997c60a9df5dae01-samplingplanexecutor
ideal_use_cases
["Apply multi-stage/stratified sampling steps from a SamplingPlanner design summary to a provided frame or sample dataset and generate selection/weight variables.", "Compute base weights, nonresponse adjustments, post-stratification/raking (when benchmarks provided), and produce CV/SE/DEFF tables for key estimates."]
limitations
["Does not redesign or change the sampling module; requires uploaded design summary and appropriate frame/sample data (no fabrication of population counts or MOS)."]
target_users
["Survey statisticians and sampling methodologists who need to execute a pre-defined sampling plan on real data.", "Policy and research teams producing official-style tables (weights, CV/DEFF, allocations) from SamplingPlanner outputs."]