# stata-data-cleaning

Generates reproducible Stata do-files for empirical research, covering missing value replacement, outlier capping, and data integrity checks.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 351 installs · ★ 656
- Author: Awesome Econ AI Community
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/meleantonio/awesome-econ-ai-stuff/tree/HEAD/_skills/data/stata-data-cleaning/SKILL.md
- Updated: 2026-09-26
- HTML: https://affpapa.org/en/nevibecode/skills/stata-data-cleaning

## What it does

The skill directs the coding agent to gather context before writing code, asking about the data source, observation unit, and known quality issues. It then produces a complete do-file with header documentation, global directory paths, logging, and inspection steps using `describe` and `duplicates report`.

The cleaning pipeline recodes survey missing values with `mvdecode`, standardizes strings, creates category labels, and caps extreme values. It embeds verification steps using `assert` and `isid` so invalid assumptions halt execution immediately. At the end, the script compacts storage using `compress` and outputs a codebook.

You need Stata 15 or newer, as some generated commands will fail on older releases. Installing community packages `unique`, `mdesc`, and `labutil` from SSC is also advised.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/stata-data-cleaning/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/stata-data-cleaning/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/stata-data-cleaning/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/stata-data-cleaning/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install meleantonio/awesome-econ-ai-stuff stata-data-cleaning
```

2. **Manually: put the files in place**
- File: `.github/skills/stata-data-cleaning/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/meleantonio/awesome-econ-ai-stuff --path _skills/data/stata-data-cleaning
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/stata-data-cleaning/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/meleantonio/awesome-econ-ai-stuff/tree/HEAD/_skills/data/stata-data-cleaning/SKILL.md

## When to use it
- build reproducible do-files with logging and path globals
- recode survey missing values with mvdecode
- verify observational units using assert and isid
- assign variable labels and categorical value labels

## Example prompts
- Write a Stata do-file to clean raw_survey_data.dta and verify unique id_var values
- Generate a Stata cleaning script that handles -99 missing codes and caps income outliers
- Create a reproducible Stata pipeline with log files and assert checks for panel data

## Source text · SKILL.md · CC0-1.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 535580e501d444df.

BEGIN THIRD-PARTY FILE 535580e501d444df
````text
---
name: stata-data-cleaning
description: Clean and transform messy data in Stata with reproducible workflows
workflow_stage: data
compatibility:
  - claude-code
  - cursor
  - codex
  - gemini-cli
author: Awesome Econ AI Community
version: 1.0.0
tags:
  - Stata
  - data-cleaning
  - data-wrangling
  - reproducibility
---

# Stata Data Cleaning

## Purpose

This skill helps economists clean, transform, and prepare datasets for analysis in Stata. It emphasizes reproducibility, proper documentation, and handling common data quality issues found in economic research.

## When to Use

- Cleaning raw survey or administrative data
- Merging multiple data sources
- Handling missing values, duplicates, and outliers
- Creating analysis-ready panel datasets
- Documenting data transformations for replication

## Instructions

### Step 1: Understand the Data

Before generating code, ask the user:
1. What is the data source? (survey, administrative, API, etc.)
2. What is the unit of observation?
3. What are the key variables needed for analysis?
4. Are there known data quality issues to address?

### Step 2: Generate Cleaning Pipeline

Create a Stata do-file that:

1. **Has a clear header** with project info and date
2. **Sets up the environment** (clear all, set memory, log)
3. **Loads and inspects raw data**
4. **Documents each transformation** with comments
5. **Creates a codebook** for the final dataset

### Step 3: Follow Best Practices

- Use `assert` statements to verify data integrity
- Create labeled variables with `label variable`
- Use value labels for categorical variables
- Generate a log file for reproducibility
- Save intermediate files when appropriate

## Example Output

```stata
/*==============================================================================
    Project:    Economic Analysis Data Cleaning
    Author:     [Your Name]
    Date:       [Date]
    Purpose:    Clean raw survey data for regression analysis
    Input:      raw_survey_data.dta
    Output:     cleaned_analysis_data.dta
==============================================================================*/

* ============================================
* 1. SETUP
* ============================================

clear all
set more off
cap log close
log using "logs/data_cleaning_`c(current_date)'.log", replace

* Set working directory
cd "/path/to/project"

* Define globals for paths
global raw_data "data/raw"
global clean_data "data/clean"
global output "output"

* ============================================
* 2. LOAD AND INSPECT RAW DATA
* ============================================

use "${raw_data}/raw_survey_data.dta", clear

* Basic inspection
describe
summarize
codebook, compact
````
END THIRD-PARTY FILE 535580e501d444df

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