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Stata Data Cleaning

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meleantonio·vlatest·data

Automates repetitive Stata data cleaning tasks like handling missing values, outliers, and variable transformations for efficient analysis.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

meleantonio

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Install target

Live repository

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No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

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  • No pipe-to-shell installers: passed

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    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned meleantonio/awesome-econ-ai-stuff at dd890d51c7.

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Commit
dd890d51c7
Manifest
_skills/data/stata-data-cleaning/SKILL.md
Scanned
2 files, 508 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
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About this skill

What it does

This skill enables AI agents to perform data cleaning tasks directly within the Stata statistical software environment. It automates the preparation of datasets for econometric analysis by handling missing values, recoding variables, and managing file formats specific to Stata's .dta or .do syntax.

When to use it

  • You need to preprocess raw survey data before running regression models in Stata.
  • Your dataset contains inconsistent variable names or types that require standardization for analysis.
  • You want to automate repetitive cleaning steps like dropping outliers or imputing missing values across large datasets.
  • You are working with legacy Stata files and need to convert them into a clean, analysis-ready format.

Key capabilities

  • Executes Stata commands for data manipulation and transformation.
  • Handles missing value identification and treatment strategies.
  • Manages variable renaming and recoding within the Stata environment.
  • Generates cleaned dataset files compatible with standard econometric workflows.

Example prompts

  • "Clean this raw survey dataset by removing rows with missing income values and recoding gender into binary numeric variables."
  • "Load my .dta file, drop observations where age is below 18 or above 90, and save the result as cleaned_data.dta."
  • "Standardize variable names in this dataset to snake_case format and create a summary table of missing values for each column."

Tips & gotchas

Ensure your source data is properly formatted for Stata ingestion before requesting cleaning operations. Complex logic involving conditional imputation may require iterative prompting or custom .do file generation rather than single-command execution.