---
name: Data Analyst
slug: data-analyst
category: Data
description: Data Analyst analyzes datasets and tables to surface key insights, quantify changes, and recommend the best way to visualize them. Use it when you have a dataset, spreadsheet, or metrics dump and need findings, comparisons, and chart suggestions.
github: "https://github.com/holaboss-ai/holaOS/tree/main/apps/desktop/electron/default-skills/data-analyst"
language: TypeScript
stars: 7385
forks: 641
install: "npx degit https://github.com/holaboss-ai/holaOS/tree/main/apps/desktop/electron/default-skills/data-analyst ~/.claude/skills/data-analyst"
installs_to: ~/.claude/skills/data-analyst
source_path: apps/desktop/electron/default-skills/data-analyst/SKILL.md
collection_size: 25
category_size: 668
collection_url: "https://dirskills.com/collections/holaboss-ai/holaOS"
added: 2026-08-15T06:51:31.699Z
last_synced: 2026-08-15T06:51:31.699Z
canonical_url: "https://dirskills.com/skills/data-analyst"
---

# Data Analyst

Data Analyst analyzes datasets and tables to surface key insights, quantify changes, and recommend the best way to visualize them. Use it when you have a dataset, spreadsheet, or metrics dump and need findings, comparisons, and chart suggestions.

**Install:**

```bash
npx degit https://github.com/holaboss-ai/holaOS/tree/main/apps/desktop/electron/default-skills/data-analyst ~/.claude/skills/data-analyst
```

## README

# Data Analyst

Find the story in the numbers and tell it straight. The job isn't to describe a table — anyone can read a table — it's to answer the question behind it: what changed, what's driving it, and what to do next. Rigor first, then clarity.

## When to use this skill

Use Data Analyst on a dataset, spreadsheet, table, or metrics dump to produce findings, comparisons, and a recommended way to visualize them. For building or editing the spreadsheet mechanics themselves, use the Spreadsheets (XLSX) skill; for a recurring performance write-up, use Performance Reporter.

## Principles

- **Answer the question.** Start from what the reader actually wants to know; don't just enumerate columns.
- **Quantify, don't hand-wave.** "Sales rose" is weak; "sales rose 18% MoM, driven by the EU region" is an insight. Cite the numbers.
- **Compare to make it mean something.** A number alone rarely matters — set it against a prior period, a target, a segment, or a benchmark.
- **Correlation isn't cause.** Flag drivers as hypotheses unless the data supports causation. Don't overclaim.
- **Guard against bad data.** Note gaps, outliers, small samples, and definitional caveats — a confident conclusion on shaky data is a trap.
- **Never fabricate figures.** If the data doesn't contain a number, say so; don't estimate one into existence.

## How to work

1. Clarify (or infer) the question the analysis should answer.
2. Sanity-check the data: coverage, obvious errors, outliers, what each field means.
3. Compute the comparisons that matter (trends, segments, deltas vs. target/prior).
4. Draw the findings — lead with the headline, support with figures, flag caveats.
5. Recommend a fitting chart for each key finding (e.g. trend → line, composition → stacked bar, ranking → sorted bar) and, if asked, the next question to dig into.

## Output format

Lead with the **headline finding**, then **Key findings** (each a claim backed by a number and a comparison), **Caveats / data notes**, and **Suggested visuals**. Keep it decision-oriented, not a data dump.