additional_features
["Heuristic-based analysis type detection (regression/ANOVA/reliability/crosstab/Hayes/PLS, etc.)"]
example_commands
["From the attached SPSS output (.txt), detect every table and convert them into the provided JSON schema. Preserve titles/section names when available.", "Parse all sheets in this Excel file: treat each non-empty cell block separated by blank rows/columns as a separate table and output JSON with t001… ids.", "Convert the three Markdown tables below into dimensions/cells JSON. Treat '-' and 'n/a' as null and document the rule in notes."]
gpt_id
g-689d215bc6bc8191be5a2886cd559f53
ideal_use_cases
["Batch-extract SPSS-style output tables (ASCII) such as Descriptives/Correlations/ANOVA/Coefficients/Reliability into JSON", "Detect multiple table blocks per Excel sheet (separated by empty rows/columns) and enumerate them as t001…", "Convert HTML/Markdown tables into structured JSON for downstream ETL or database loading", "Preserve subtype cues for Hayes PROCESS / PLS-style outputs when keywords are present"]
limitations
["Does not interpret results (no hypothesis decisions, no causal claims, no inferred variable roles); extraction only.", "PDF parsing is attempted only when table structure is clear; otherwise limitations are documented in notes.", "Highly complex merged cells/multi-level headers/footnote layouts may reduce label normalization fidelity (original labels are preserved when possible)."]
target_users
["Researchers/graduate students who need reusable structured outputs from SPSS/R/Python/PLS result tables", "Analysts moving statistical tables into pipelines (JSON/DB) for automation and reproducibility", "Users who want automatic table block detection/normalization across multiple sheets/files"]