Stata Data Cleaning
wentorai/research-plugins
Clean, transform, and validate messy research data using Stata
Clean and transform messy data in Stata with reproducible workflows
$ npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install meleantonio/awesome-econ-ai-stuff stata-data-cleaning --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/meleantonio/awesome-econ-ai-stuff.git skills-src && mkdir -p .claude/skills && cp -r skills-src/_skills/data/stata-data-cleaning .claude/skills/stata-data-cleaning && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "stata-data-cleaning" agent skill from https://github.com/meleantonio/awesome-econ-ai-stuff/tree/main/_skills/data/stata-data-cleaning into .claude/skills/stata-data-cleaning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stata-data-cleaning", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/meleantonio/awesome-econ-ai-stuff/tree/main/_skills/data/stata-data-cleaningType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install meleantonio/awesome-econ-ai-stuff stata-data-cleaning --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/meleantonio/awesome-econ-ai-stuff.git skills-src && mkdir -p .agents/skills && cp -r skills-src/_skills/data/stata-data-cleaning .agents/skills/stata-data-cleaning && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "stata-data-cleaning" agent skill from https://github.com/meleantonio/awesome-econ-ai-stuff/tree/main/_skills/data/stata-data-cleaning into .agents/skills/stata-data-cleaning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stata-data-cleaning", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install meleantonio/awesome-econ-ai-stuff stata-data-cleaning --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/meleantonio/awesome-econ-ai-stuff.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/_skills/data/stata-data-cleaning .cursor/skills/stata-data-cleaning && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "stata-data-cleaning" agent skill from https://github.com/meleantonio/awesome-econ-ai-stuff/tree/main/_skills/data/stata-data-cleaning into .cursor/skills/stata-data-cleaning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stata-data-cleaning", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/meleantonio/awesome-econ-ai-stuff.git --path _skills/data/stata-data-cleaning--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install meleantonio/awesome-econ-ai-stuff stata-data-cleaning --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/meleantonio/awesome-econ-ai-stuff.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/_skills/data/stata-data-cleaning .gemini/skills/stata-data-cleaning && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "stata-data-cleaning" agent skill from https://github.com/meleantonio/awesome-econ-ai-stuff/tree/main/_skills/data/stata-data-cleaning into .gemini/skills/stata-data-cleaning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stata-data-cleaning", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install meleantonio/awesome-econ-ai-stuff stata-data-cleaningInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/meleantonio/awesome-econ-ai-stuff.git skills-src && mkdir -p .github/skills && cp -r skills-src/_skills/data/stata-data-cleaning .github/skills/stata-data-cleaning && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "stata-data-cleaning" agent skill from https://github.com/meleantonio/awesome-econ-ai-stuff/tree/main/_skills/data/stata-data-cleaning into .github/skills/stata-data-cleaning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stata-data-cleaning", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install meleantonio/awesome-econ-ai-stuff stata-data-cleaning --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/meleantonio/awesome-econ-ai-stuff.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/_skills/data/stata-data-cleaning .opencode/skills/stata-data-cleaning && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "stata-data-cleaning" agent skill from https://github.com/meleantonio/awesome-econ-ai-stuff/tree/main/_skills/data/stata-data-cleaning into .opencode/skills/stata-data-cleaning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "stata-data-cleaning", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
stata-data-cleaningClean and transform messy data in Stata with reproducible workflows
Stata Data Cleaning is an agent skill from meleantonio/awesome-econ-ai-stuff. Clean and transform messy data in Stata with reproducible workflows
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `index.md`). Compatibility notes: ["claude-code","cursor","codex","gemini-cli"]
It sits in Research & Science, covering Econometrics and empirical research and Data cleaning. The repository describes itself as: AI Skills for Economists.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit dd890d5. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are stata).
From the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
stata.comweb.stanford.eduworldbank.github.ioFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
["claude-code","cursor","codex","gemini-cli"]
From compatibility in the SKILL.md frontmatter.
Stata Data Cleaning loads about 1.8k tokens when it runs. Until then it costs about 22 tokens; SKILL.md has 315 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 315 words (~1,825 tokens).
“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.”
SKILL.md and 1 other file in _skills/data/stata-data-cleaning of meleantonio/awesome-econ-ai-stuff.
Open the folder on GitHubat commit dd890d5
We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in meleantonio/awesome-econ-ai-stuff, which our catalogue first saw on October 7, 2026.
Stata Data Cleaning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Stata Data Cleaning this skillmeleantonio/awesome-econ-ai-stuff | 645 | 2 repos | ~1.8k | Automated safety check: Pass | Custom licence | |
| Stata Data Cleaningwentorai/research-plugins | 298 | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| Data Cleaningbrycewang-stanford/Auto-Empirical-Research-Skills | 4.5k | — | ~2.9k | Automated safety check: Pass | Custom licence | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Example Datasetspymc-labs/CausalPy | 1.2k | — | ~587 | Automated safety check: Pass | Apache-2.0 | |
| Daily PapersXiangyue-Zhang/auto-deep-researcher-24x7 | 1.3k | — | ~309 | Automated safety check: Pass | Apache-2.0 |
wentorai/research-plugins
Clean, transform, and validate messy research data using Stata
brycewang-stanford/Auto-Empirical-Research-Skills
Clean and transform messy data for analysis in Python, R, or Stata
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
pymc-labs/CausalPy
Load built-in CausalPy example datasets for demos, tutorials, tests, and quick causal-analysis prototypes.
Xiangyue-Zhang/auto-deep-researcher-24x7
Daily arXiv paper recommendations with automatic deduplication
aspi6246/Claude-Code-Skills-for-Academics
Systematic dataset profiling protocol for empirical research.
meleantonio/awesome-econ-ai-stuff
Draft economics papers with proper structure and academic style
meleantonio/awesome-econ-ai-stuff
Fetch economic data from FRED, World Bank, and other APIs. An agent skill from meleantonio/awesome-econ-ai-stuff.
meleantonio/awesome-econ-ai-stuff
Create academic presentations in Beamer with professional themes
meleantonio/awesome-econ-ai-stuff
Create publication-quality charts and graphs for economics papers.
meleantonio/awesome-econ-ai-stuff
Build and solve Walrasian general equilibrium models with theory derivations and Julia computation
meleantonio/awesome-econ-ai-stuff
Generate publication-ready regression tables in LaTeX. An agent skill from meleantonio/awesome-econ-ai-stuff.
Categories
Clean and transform messy data in Stata with reproducible workflows. Stata Data Cleaning is an agent skill from meleantonio/awesome-econ-ai-stuff.
Stata Data Cleaning fits situations like: tasks that involve Econometrics and empirical research; tasks that involve Data cleaning.
Run `npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a claude-code`. Or copy the skill folder (_skills/data/stata-data-cleaning in meleantonio/awesome-econ-ai-stuff) into .claude/skills/stata-data-cleaning in your project. Claude Code loads it when a task matches its description.
Run `npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a codex`. Or copy the skill folder (_skills/data/stata-data-cleaning in meleantonio/awesome-econ-ai-stuff) into .agents/skills/stata-data-cleaning in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add meleantonio/awesome-econ-ai-stuff --skill stata-data-cleaning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/stata-data-cleaning, .gemini/skills/stata-data-cleaning, .github/skills/stata-data-cleaning and .opencode/skills/stata-data-cleaning in your project.
SKILL.md names no scripts, command-line tools or credentials: Stata Data Cleaning is instructions for the agent only. Compatibility (from SKILL.md): ["claude-code","cursor","codex","gemini-cli"].
SKILL.md names 3 domains. As links in the text: stata.com, web.stanford.edu and worldbank.github.io. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Stata Data Cleaning has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.
About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Stata Data Cleaning: Stata Data Cleaning (wentorai/research-plugins, 298 stars), Data Cleaning (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars) and Example Datasets (pymc-labs/CausalPy, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
meleantonio (a GitHub user) maintains it in meleantonio/awesome-econ-ai-stuff, which has 645 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on September 26, 2026.
Source: meleantonio/awesome-econ-ai-stuff on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.