How do I install the Data Cleanup skill?

Run npx skillmds@latest add sgharlow/data-cleanup in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Data Cleanup safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Data Cleanup?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Data Cleanup free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Data Cleanup?

sgharlow (@sgharlow) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Data Cleanup by sgharlow — Agent Skill

Name: Data Cleanup
Rating: 4.2 (5 reviews)
Author: sgharlow

Data Cleanup

Clean and standardize messy tabular data (CSV, spreadsheet paste, system exports) into an analysis-ready dataset — consistent dates and names, typed columns, duplicates identified, missing values handled explicitly. Use when the user says "clean this data", "standardize this CSV", "dedupe this list", or pastes a table with inconsistent formatting.

sgharlow Updated Sep 9, 2026

File contents

Data Cleanup and Formatting

Standardize messy tabular data without ever silently changing what it says. Every transformation is declared; every ambiguous value is surfaced, not guessed.

Steps

Get the data (file path or paste) and profile it first: row count, columns, detected types, distinct-value oddities (mixed date formats, case-inconsistent names, stray whitespace, mixed types in one column), missing-value counts, and candidate duplicates. Present this profile BEFORE changing anything.
Propose the cleanup plan as a checklist the user confirms: target date format, name casing, type per column, duplicate rule (exact vs. fuzzy key), and missing-value policy per column (leave blank / fill with sentinel / drop row — never a silent default).
Apply the confirmed plan. For anything ambiguous (is 02/03/24 Feb 3 or Mar 2? are "J. Smith" and "John Smith" the same person?), stop and ask — wrong-but-tidy is worse than messy.
Deliver: the cleaned dataset, plus a transformation log — rows in/out, per-column changes applied, duplicates found (listed, not just deleted), missing values and how each was handled, and any rows quarantined as unparseable rather than mangled.
Verify: spot-check that no value changed meaning (dates shifted, names merged wrongly); re-state the row count arithmetic (in = out + dropped + quarantined) so nothing vanishes.

Constraints

Never delete or merge rows without listing exactly which ones and why.
Never guess an ambiguous date, unit, or identity — ask.
The original input is never overwritten; cleaned output is a new file/table.
If the data is too large to show fully, show the profile + a sample and operate via a script the user can inspect, not invisible edits.

Full walkthrough, examples, and variations: recipes/Recipe-009-Data-Cleanup-Formatting.md.

---name: data-cleanupdescription: Clean and standardize messy tabular data (CSV, spreadsheet paste, system exports) into an analysis-ready dataset — consistent dates and names, typed columns, duplicates identified, missing values handled explicitly. Use when the user says "clean this data", "standardize this CSV", "dedupe this list", or pastes a table with inconsistent formatting.--- # Data Cleanup and Formatting Standardize messy tabular data without ever silently changing what it says. Every transformation is declared; every ambiguous value is surfaced, not guessed. ## Steps 1. Get the data (file path or paste) and profile it first: row count, columns, detected types, distinct-value oddities (mixed date formats, case-inconsistent names, stray whitespace, mixed types in one column), missing-value counts, and candidate duplicates. Present this profile BEFORE changing anything. 2. Propose the cleanup plan as a checklist the user confirms: target date format, name casing, type per column, duplicate rule (exact vs. fuzzy key), and missing-value policy per column (leave blank / fill with sentinel / drop row — never a silent default). 3. Apply the confirmed plan. For anything ambiguous (is `02/03/24` Feb 3 or Mar 2? are "J. Smith" and "John Smith" the same person?), stop and ask — wrong-but-tidy is worse than messy. 4. Deliver: the cleaned dataset, plus a transformation log — rows in/out, per-column changes applied, duplicates found (listed, not just deleted), missing values and how each was handled, and any rows quarantined as unparseable rather than mangled. 5. Verify: spot-check that no value changed meaning (dates shifted, names merged wrongly); re-state the row count arithmetic (in = out + dropped + quarantined) so nothing vanishes. ## Constraints - Never delete or merge rows without listing exactly which ones and why. - Never guess an ambiguous date, unit, or identity — ask. - The original input is never overwritten; cleaned output is a new file/table. - If the data is too large to show fully, show the profile + a sample and operate via a script the user can inspect, not invisible edits. Full walkthrough, examples, and variations: `recipes/Recipe-009-Data-Cleanup-Formatting.md`.

Frequently asked questions