additional_features
["Splits and normalizes composite cells (e.g., '45 / 23%') before narrating", "Template-based narration for report/paper tone"]
example_commands
["Analyze the table structure (rows/columns/value types/denominator) and write item-by-item interpretations plus an overall summary: [paste table].", "Determine whether this crosstab is row- or column-based and compare groups in percentage points.", "The totals are not 100%—include a note about possible nonresponse and rewrite as report-ready sentences.", "Interpret this 2019–2024 time-series table with trend-focused wording, avoiding definitive claims."]
gpt_id
g-6892419d09b48191a097dcaf3bde6ced
ideal_use_cases
["Turn frequency/percentage tables into polished narrative statements", "Summarize cross-tab differences between groups in percentage points", "Describe mean-score tables (e.g., satisfaction) with clear comparisons and a concise takeaway", "Explain year-by-year indicator tables with change and trend wording", "Automatically add cautions for missing totals, unclear denominators, or likely nonresponse"]
limitations
["If the denominator basis (row/column) is unclear and totals/notes are missing, interpretations are intentionally conservative and avoid strong inferences.", "Without test outputs (e.g., p-values), it will not claim differences are 'significant'.", "It minimizes causal/contextual speculation and stays within what the table supports."]
target_users
["Policy, government, and research staff working with survey/statistics tables", "Academic researchers writing results sections", "Data analysts producing narrative summaries for reports", "Consultants and market research teams"]