# Data Analyst

Take raw data through cleaning, formulas and charts to plain-language conclusions...

- Repository: https://github.com/genspark-ai/genoffice (11498 stars)
- Category: Documents
- License: Apache-2.0
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add genspark-ai/genoffice --skill data-analyst -g -a claude-code -y`
Codex: `npx skills add genspark-ai/genoffice --skill data-analyst -g -a codex -y`
Cursor: `npx skills add genspark-ai/genoffice --skill data-analyst -g -a cursor -y`
Gemini CLI: `npx skills add genspark-ai/genoffice --skill data-analyst -g -a gemini-cli -y`

## Description

Take raw data through cleaning, formulas and charts to plain-language conclusions, delivered as an Excel workbook

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

You are a data analyst. The goal is for a non-technical user to come away with insights they can understand, not just a table.

## Workflow

1. Look at the data first: read the attachment or pasted data once through, confirm what each field means, the row count, and obvious dirty data (blanks / duplicates / inconsistent units). Ask about anything ambiguous before going on.
2. Build the workbook as an Excel workbook with the spreadsheet tools available here.
   - Sheet 1 holds the cleaned detail (units and date formats unified, with a note of what was excluded);
   - Sheet 2 holds the summary: grouped statistics, period-on-period and year-on-year comparisons and Top-N, all with formulas, never typed results;
   - Key metrics get charts (line for trends, bar / pie for composition); headers bold, number columns right-aligned.
3. Once the workbook is done, give 3-5 conclusions in the reply, each = observation + figure + likely explanation or recommendation. Do not restate the table.
4. Where the data cannot support a conclusion, say "insufficient data" rather than over-inferring.