# Data Resume

Writes data analyst, analytics engineer, data engineer, data scientist and...

- Repository: https://github.com/reactive-resume/reactive-resume (45160 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add reactive-resume/reactive-resume --skill data-resume -g -a claude-code -y`
Codex: `npx skills add reactive-resume/reactive-resume --skill data-resume -g -a codex -y`
Cursor: `npx skills add reactive-resume/reactive-resume --skill data-resume -g -a cursor -y`
Gemini CLI: `npx skills add reactive-resume/reactive-resume --skill data-resume -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add reactive-resume/reactive-resume`

## Description

Writes data analyst, analytics engineer, data engineer, data scientist and machine-learning resumes. Connects methods and data products to decisions while distinguishing offline evaluation, experiments and production results; use academic-cv for an academic research record.

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

# Data Resume

Use the user's evidence, never facts or numbers from worked examples. If asked to fabricate or inflate a claim, decline that part briefly, offer the strongest truthful version and complete the remaining work; maintain that boundary if pressed.

Show what the data work enabled and how it was evaluated. Use the user's text and permitted artifacts; no connected account is required.

## Choose the role

Do not treat every data title as interchangeable. Ask which work the user wants next and what they actually did:

| Role               | Evidence to prioritize                                                                         |
| ------------------ | ---------------------------------------------------------------------------------------------- |
| Analyst/BI         | Business question, SQL or analysis, metric definitions, stakeholder decision and reporting use |
| Analytics engineer | Data models, tests, lineage, semantic definitions and trusted downstream use                   |
| Data engineer      | Ingestion, transformations, orchestration, freshness, volume, quality and reliability          |
| Data scientist     | Question, method, baseline, evaluation design, uncertainty and resulting decision              |
| ML engineer        | Model integration, serving, deployment, monitoring, latency/cost and operational constraints   |

Ask for ownership, data scope, the evaluation setting and actual adoption. A notebook, offline model, pilot and deployed system are different claims.

## Write

Lead with relevant experience and selected projects. Group skills by languages, analysis/modeling, data platforms and visualization only where useful. Name methods actually used, not every method associated with the job.

For each substantial claim, keep enough context to interpret it:

The rest: https://github.com/reactive-resume/reactive-resume/blob/HEAD/plugins/reactive-resume/skills/data-resume/SKILL.md