Empirical Analysis Skill Python
Drchronx/ai-agent-research-starter-kit
Parameterized Python empirical-analysis and machine-learning workflow for applied economics, public health epidemiology, supervised ML, and ML causal inference.
Clean and transform messy data for analysis in Python, R, or Stata
Install Data Cleaning
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-cleaning -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills 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/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/67-econfin-workflow-toolkit/data-cleaning .claude/skills/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 "data-cleaning" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/data-cleaning into .claude/skills/data-cleaning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "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/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/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 brycewang-stanford/Auto-Empirical-Research-Skills --skill data-cleaning -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills data-cleaning --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/67-econfin-workflow-toolkit/data-cleaning .agents/skills/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 "data-cleaning" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/data-cleaning into .agents/skills/data-cleaning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "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 brycewang-stanford/Auto-Empirical-Research-Skills --skill data-cleaning -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills data-cleaning --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/67-econfin-workflow-toolkit/data-cleaning .cursor/skills/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 "data-cleaning" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/data-cleaning into .cursor/skills/data-cleaning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "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/brycewang-stanford/Auto-Empirical-Research-Skills.git --path skills/67-econfin-workflow-toolkit/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 brycewang-stanford/Auto-Empirical-Research-Skills --skill data-cleaning -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills data-cleaning --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/67-econfin-workflow-toolkit/data-cleaning .gemini/skills/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 "data-cleaning" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/data-cleaning into .gemini/skills/data-cleaning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "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 brycewang-stanford/Auto-Empirical-Research-Skills 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 brycewang-stanford/Auto-Empirical-Research-Skills --skill data-cleaning -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/67-econfin-workflow-toolkit/data-cleaning .github/skills/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 "data-cleaning" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/data-cleaning into .github/skills/data-cleaning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "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 brycewang-stanford/Auto-Empirical-Research-Skills --skill 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 brycewang-stanford/Auto-Empirical-Research-Skills data-cleaning --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/67-econfin-workflow-toolkit/data-cleaning .opencode/skills/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 "data-cleaning" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/67-econfin-workflow-toolkit/data-cleaning into .opencode/skills/data-cleaning/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "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.
Facts
- Skill name
data-cleaning- GitHub stars
- 4.5k
- Token cost
- ~2.9k tokens
- SKILL.md length
- 337 words
- Files
- 1
- Skills in repo
- 364
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Clean and transform messy data for analysis in Python, R, or Stata
- Works in 3 steps: Understand the Data → Generate Cleaning Pipeline → Follow Best Practices
- Tasks that involve Data cleaning
- SKILL.md covers Purpose, When to Use, Instructions and Example Output, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Data Cleaning is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. Clean and transform messy data for analysis in Python, R, or Stata
Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Data cleaning and Econometrics and empirical research. It works with Python. The repository describes itself as: 🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库，覆盖8大社会科学学科的实证研究。CoPaper.AI…
When your agent uses it
- Tasks that involve Data cleaning
- Tasks that involve Econometrics and empirical research
Example prompts
- “/data-cleaning”
Requirements
- Python 3
Workflow steps
3 steps, taken from the step headings in SKILL.md.
- Understand the Data
- Generate Cleaning Pipeline
- Follow Best Practices
What it can do on your machine
Read from SKILL.md and the folder at commit 9fa87d8. It shows what the files ask for, not the result of running them.
Tool permissions
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.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are stata, python and r).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Data Cleaning loads about 2.9k tokens when it runs. Until then it costs about 20 tokens; SKILL.md has 337 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.
Safety
Auto-check passed
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.
SKILL.md
Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 337 words (~2,914 tokens).
“This skill helps economists clean, transform, and prepare datasets for analysis in Python, R, or Stata. It emphasizes reproducibility, proper documentation, and handling common data quality issues found in economic research.”
- name
- data-cleaning
Files
Just SKILL.md in skills/67-econfin-workflow-toolkit/data-cleaning of brycewang-stanford/Auto-Empirical-Research-Skills.
Open the folder on GitHubat commit 9fa87d8
Compare with similar skills
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 |
|---|---|---|---|---|---|---|
| Data Cleaning this skillbrycewang-stanford/Auto-Empirical-Research-Skills | 4.5k | — | ~2.9k | Automated safety check: Pass | Custom licence | |
| Empirical Analysis Skill PythonDrchronx/ai-agent-research-starter-kit | 135 | — | ~3k | Automated safety check: Pass | Custom licence | |
| Authoritative Data Harvesteryushui2022/MathModel-Skill | 452 | 1 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Fin Arch Diagramcsmar432/finai-research | 109 | — | ~1.5k | Automated safety check: Pass | MIT | |
| Bio Proteomics Differential AbundanceGPTomics/bioSkills | 1.2k | 1 repos | ~5.7k | Automated safety check: Pass | MIT | |
| Bio Splicing QcFreedomIntelligence/OpenClaw-Medical-Skills | 3.1k | — | ~1.6k | Automated safety check: Pass | None |
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Related
Works with
Categories
Questions about Data Cleaning
What does Data Cleaning do?
Clean and transform messy data for analysis in Python, R, or Stata. Data Cleaning is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills.
When should I use Data Cleaning?
Data Cleaning fits situations like: tasks that involve Data cleaning; tasks that involve Econometrics and empirical research.
How do I install Data Cleaning in Claude Code?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-cleaning -a claude-code`. Or copy the skill folder (skills/67-econfin-workflow-toolkit/data-cleaning in brycewang-stanford/Auto-Empirical-Research-Skills) into .claude/skills/data-cleaning in your project. Claude Code loads it when a task matches its description.
How do I install Data Cleaning in Codex?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-cleaning -a codex`. Or copy the skill folder (skills/67-econfin-workflow-toolkit/data-cleaning in brycewang-stanford/Auto-Empirical-Research-Skills) into .agents/skills/data-cleaning in your project. Codex loads it when a task matches its description.
Can I use Data Cleaning in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill 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/data-cleaning, .gemini/skills/data-cleaning, .github/skills/data-cleaning and .opencode/skills/data-cleaning in your project.
What does Data Cleaning need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Cleaning is instructions for the agent only. Our summary lists: Python 3.
Does Data Cleaning access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Data Cleaning safe to install?
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.
What licence does Data Cleaning use?
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.
How many tokens does Data Cleaning use?
About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Data Cleaning?
Skills that share tags, products or a category with Data Cleaning: Empirical Analysis Skill Python (Drchronx/ai-agent-research-starter-kit, 135 stars), Authoritative Data Harvester (yushui2022/MathModel-Skill, 452 stars), Fin Arch Diagram (csmar432/finai-research, 109 stars) and Bio Proteomics Differential Abundance (GPTomics/bioSkills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Cleaning?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Auto-Empirical-Research-Skills, which has 4,529 GitHub stars. The repository holds 364 skills in this directory. The repository was last updated on October 5, 2026.
Source: brycewang-stanford/Auto-Empirical-Research-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.