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

1. 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.
2. 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.
3. 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.