About data-analyst
A data-analysis skill that turns supplied data into an Excel workbook with cleaned details, formula-based summaries, comparisons, and charts. It is designed to give non-technical users understandable findings, with conclusions grounded in the available data.
Key features
- 01Data quality and field checks
- 02Cleaned detail sheet
- 03Formula-based grouped summaries
- 04Period and year comparisons
- 05Top-N analysis and charts
- 06Evidence-based conclusions
Use cases
- 01Summarizing an unfamiliar dataset
- 02Comparing metrics across periods
- 03Presenting trends and category composition
Install
Copy the command and run it in a terminal.
$
npx skills add https:/After installing, type/data-analystin Claude Code to confirm it loaded.
Details
From the project READMEYou 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
- 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.
- 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.
- Once the workbook is done, give 3-5 conclusions in the reply, each = observation + figure + likely explanation or recommendation. Do not restate the table.
- Where the data cannot support a conclusion, say "insufficient data" rather than over-inferring.
Comments
Markdown supported