Splitting Datasets
jeremylongshore/tons-of-skills-marketplace
Process split datasets into training, validation, and testing sets for ML model development.
Produce documented data cleaning scripts that log every transformation with N before/after each step, generate a CONSORT-style exclusion flow diagram, create decision log entries for every…
Install Data Clean
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-clean -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills data-clean --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/61-phdemotions-research-methods/skills/data-clean .claude/skills/data-clean && 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-clean" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/61-phdemotions-research-methods/skills/data-clean into .claude/skills/data-clean/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-clean", 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/61-phdemotions-research-methods/skills/data-cleanType 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-clean -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills data-clean --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/61-phdemotions-research-methods/skills/data-clean .agents/skills/data-clean && 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-clean" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/61-phdemotions-research-methods/skills/data-clean into .agents/skills/data-clean/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-clean", 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-clean -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills data-clean --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/61-phdemotions-research-methods/skills/data-clean .cursor/skills/data-clean && 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-clean" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/61-phdemotions-research-methods/skills/data-clean into .cursor/skills/data-clean/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-clean", 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/61-phdemotions-research-methods/skills/data-clean--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-clean -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Auto-Empirical-Research-Skills data-clean --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/61-phdemotions-research-methods/skills/data-clean .gemini/skills/data-clean && 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-clean" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/61-phdemotions-research-methods/skills/data-clean into .gemini/skills/data-clean/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-clean", 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-cleanInstalls 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-clean -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/61-phdemotions-research-methods/skills/data-clean .github/skills/data-clean && 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-clean" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/61-phdemotions-research-methods/skills/data-clean into .github/skills/data-clean/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-clean", 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-clean -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-clean --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/61-phdemotions-research-methods/skills/data-clean .opencode/skills/data-clean && 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-clean" agent skill from https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills/tree/main/skills/61-phdemotions-research-methods/skills/data-clean into .opencode/skills/data-clean/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-clean", 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-clean- GitHub stars
- 4.5k
- Token cost
- ~1.1k tokens
- SKILL.md length
- 459 words
- Files
- 4 (incl. references)
- Skills in repo
- 364
- Repo updated
- First seen
- Licence
- Custom licence
At a glance
Produce documented data cleaning scripts that log every transformation with N before/after each step, generate a CONSORT-style exclusion flow diagram, create decision log entries for every…
- Works in 7 steps: Read context → Load principles and rubric → Plan the cleaning pipeline → …
- The user says clean data
- SKILL.md covers How to run cleaning, Voice and Argument handling
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Data Clean is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. Produce documented data cleaning scripts that log every transformation with N before/after each step, generate a CONSORT-style exclusion flow diagram, create decision log entries for every subjective choice, compute scale reliability and composites, and write cleaned data to data/processed/. Never modifies raw data. Use when the user says "clean data," "prepare data," "apply exclusion criteria," "handle missing data," "create composites," "data preprocessing," or when /data-validate found issues to address…
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/criteria.md`, `references/principles.md` and `references/templates/consort-flow.md`).
It sits in Data & Analytics, covering Data cleaning, Architecture decision records and Machine learning. 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
- The user says clean data
- Apply exclusion criteria
- Handle missing data
- Create composites
Example prompts
- “clean data,”
- “prepare data,”
- “apply exclusion criteria,”
- “/data-clean”
Requirements
- Python 3
Workflow steps
7 steps, taken from the step headings in SKILL.md.
- Read context
- Load principles and rubric
- Plan the cleaning pipeline
- Write the cleaning functions
- Generate CONSORT-style exclusion flow
- Write cleaned data
- Summary and next steps
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.
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 Clean loads about 1.1k tokens when it runs, and up to ~3.7k if it reads all its reference files. Until then it costs about 155 tokens; SKILL.md has 459 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 459 words (~1,114 tokens).
“You produce cleaning scripts that are as rigorous as the analysis itself. Every transformation is logged. Every exclusion is counted. Every subjective choice is documented. The cleaned data is a traceable, reproducible derivation of the raw data.”
- name
- data-clean
- argument-hint
- <path to raw data or validation report — defaults to data/raw/>
Files
SKILL.md and 3 other files (references) in skills/61-phdemotions-research-methods/skills/data-clean of brycewang-stanford/Auto-Empirical-Research-Skills.
- SKILL.md
- references/criteria.md
- references/principles.md
- references/templates/consort-flow.md
Open the folder on GitHubat commit 9fa87d8
Compare with similar skills
Data Clean 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 Clean this skillbrycewang-stanford/Auto-Empirical-Research-Skills | 4.5k | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Splitting Datasetsjeremylongshore/tons-of-skills-marketplace | 2.8k | 1 repos | ~836 | Automated safety check: Pass | MIT | |
| Sap Hana Cloud Data Intelligencesecondsky/sap-skills | 462 | — | ~3.2k | Automated safety check: Pass | GPL-3.0 | |
| Rf Model Importance Analysisaipoch/medical-research-skills | 2k | — | ~2.7k | Automated safety check: Pass | MIT | |
| Scientific Data Preprocessingforyourhealth111-pixel/Vibe-Skills | 3.6k | — | ~5k | Automated safety check: Pass | Apache-2.0 | |
| Nan Safe Correlationjaechang-hits/SciAgent-Skills | 370 | 1 repos | ~2.9k | Automated safety check: Pass | CC-BY-4.0 |
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Related
Categories
Questions about Data Clean
What does Data Clean do?
Produce documented data cleaning scripts that log every transformation with N before/after each step, generate a CONSORT-style exclusion flow diagram, create decision log entries for every…. Data Clean is an agent skill from brycewang-stanford/Auto-Empirical-Research-Skills. Produce documented data cleaning scripts that log every transformation with N before/after each step, generate a CONSORT-style exclusion flow diagram, create decision log entries for every subjective choice, compute scale reliability and composites, and write cleaned data to data/processed/.
When should I use Data Clean?
Data Clean fits situations like: the user says clean data; apply exclusion criteria; handle missing data; create composites.
How do I install Data Clean in Claude Code?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-clean -a claude-code`. Or copy the skill folder (skills/61-phdemotions-research-methods/skills/data-clean in brycewang-stanford/Auto-Empirical-Research-Skills) into .claude/skills/data-clean in your project. Claude Code loads it when a task matches its description.
How do I install Data Clean in Codex?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-clean -a codex`. Or copy the skill folder (skills/61-phdemotions-research-methods/skills/data-clean in brycewang-stanford/Auto-Empirical-Research-Skills) into .agents/skills/data-clean in your project. Codex loads it when a task matches its description.
Can I use Data Clean 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-clean -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-clean, .gemini/skills/data-clean, .github/skills/data-clean and .opencode/skills/data-clean in your project.
What does Data Clean need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Clean is instructions for the agent only. Our summary lists: Python 3.
Does Data Clean 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 Clean 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 Clean use?
Data Clean 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 Clean use?
About 1.1k tokens (SKILL.md is roughly 4.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.6k tokens, read only when the agent opens those files.
What are the alternatives to Data Clean?
Skills that share tags, products or a category with Data Clean: Splitting Datasets (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), Sap Hana Cloud Data Intelligence (secondsky/sap-skills, 462 stars), Rf Model Importance Analysis (aipoch/medical-research-skills, 2k stars) and Scientific Data Preprocessing (foryourhealth111-pixel/Vibe-Skills, 3.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Clean?
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.