name	spreadsheet-tabular-analysis
description	Analyze and reshape spreadsheet, CSV, or TSV data with `pandas` and stable output files. Use when the job is aggregation, filtering, joins, metrics, or tabular transformations rather than workbook-style editing.

Spreadsheet Tabular Analysis

Metadata

Trigger when: the task is to analyze, aggregate, filter, join, reshape, or summarize spreadsheet-like data.
Do not use when: the job is primarily about preserving workbook formatting, formulas, or sheet structure.

Skill Purpose

Treat the task as tabular analysis first: produce correct metrics and stable outputs with pandas before worrying about polished workbook-level formatting.

Instructions

Inspect the input tables first: file type, schema, key columns, joins, date/number handling, and the exact output shape the user needs. Use pandas as the default analysis surface.
Run the analysis and write stable outputs under a path such as output/spreadsheet/, choosing .csv or .xlsx based on what the user needs. Keep transformations explicit so row counts, joins, and metrics can be checked.
Validate totals, row counts, joins, deduping, date/number formatting, and any source or citation columns the task requires. If the result now needs workbook-style formatting or preservation, hand off to $spreadsheet-xlsx-edit.

Non-Negotiable Acceptance Criteria

The output tables are reproducible from explicit transformations, not ad hoc spreadsheet clicking.
Row counts, joins, and aggregates are checked before delivery.
The skill does not pretend to preserve rich workbook formatting it never managed.
Source or citation columns are included when the task depends on externally sourced data.

Output

The result file path or paths.
A short summary of the transformation or analysis performed.
Any validation caveat involving joins, missing values, or formatting limits.
Next skill options (only if needed): $spreadsheet-xlsx-edit — use when the analyzed output now needs workbook-preserving formatting or formula-aware editing.

Spreadsheet Tabular Analysis

Metadata

Trigger when: the task is to analyze, aggregate, filter, join, reshape, or summarize spreadsheet-like data.

Do not use when: the job is primarily about preserving workbook formatting, formulas, or sheet structure.

Skill Purpose

Treat the task as tabular analysis first: produce correct metrics and stable outputs with pandas before worrying about polished workbook-level formatting.

Instructions

Inspect the input tables first: file type, schema, key columns, joins, date/number handling, and the exact output shape the user needs. Use pandas as the default analysis surface.

Run the analysis and write stable outputs under a path such as output/spreadsheet/, choosing .csv or .xlsx based on what the user needs. Keep transformations explicit so row counts, joins, and metrics can be checked.

Validate totals, row counts, joins, deduping, date/number formatting, and any source or citation columns the task requires. If the result now needs workbook-style formatting or preservation, hand off to $spreadsheet-xlsx-edit.

Non-Negotiable Acceptance Criteria

The output tables are reproducible from explicit transformations, not ad hoc spreadsheet clicking.

Row counts, joins, and aggregates are checked before delivery.

The skill does not pretend to preserve rich workbook formatting it never managed.

Source or citation columns are included when the task depends on externally sourced data.

Output

The result file path or paths.

A short summary of the transformation or analysis performed.

Any validation caveat involving joins, missing values, or formatting limits.

Next skill options (only if needed): $spreadsheet-xlsx-edit — use when the analyzed output now needs workbook-preserving formatting or formula-aware editing.

spreadsheet-tabular-analysis