spreadsheet-data

name	spreadsheet-data
description	Interpret tables and Excel/CSV data—trends, comparisons, anomalies, rollups, and chart suggestions. When users upload spreadsheets, ask what data means, want summaries, or need conclusions visualized.

Spreadsheet Data

When to use

Before related work, call skill_view{name: "spreadsheet-data"} to load the full body.

Use when drawing conclusions from tables, e.g.:

• User uploads .xlsx / .csv or pastes a table
• "Look at this data", "what grew fastest", "any anomalies"
• Compare, rank, share, or describe trends
• Narrate trends or comparisons in prose

Not for: pure format conversion, Excel formulas, database ETL (unless user only wants interpretation).

Workflow

1. Get data: user attachment, paste, or read_file on workspace CSV; Excel attachments are parsed to text in chat.
2. Confirm columns: if headers are unclear, state assumptions or clarify key fields.
3. Summary before detail: row/column count → key metrics → findings → recommendations.
4. Numbers carefully: compute only from visible data; note units and time range; do not invent missing columns.
5. Visualization: describe charts in words when asked; use artifact_save for deliverables (markdown table or notes).

Default output structure

## Data overview
(rows/columns, time range, granularity)

## Main findings
- ...

## Anomalies or watch items
- ...

## Recommendations (optional)
- further analysis / chart type suggestions

Platform conventions

• Default: deliver in chat; artifact_save for large-table conclusions.
• Do not write one-off table conclusions to long-term memory (background review should not persist them either).