Agent Skills › sgharlow/claude-code-recipes › data-cleanup

data-cleanup

GitHub

用于清理和标准化混乱的表格数据（如CSV、粘贴内容），将其转换为分析就绪的数据集。该技能确保所有转换透明，明确处理日期、名称、重复项和缺失值，绝不静默猜测或修改数据含义，保障数据完整性与可追溯性。

skills/data-cleanup/SKILL.md sgharlow/claude-code-recipes

触发场景

用户说'clean this data' 用户说'standardize this CSV' 用户说'dedupe this list' 用户粘贴格式不一致的表格

安装

npx skills add sgharlow/claude-code-recipes --skill data-cleanup -g -y

更多选项

不安装直接使用

npx skills use sgharlow/claude-code-recipes@data-cleanup

指定 Agent (Claude Code)

npx skills add sgharlow/claude-code-recipes --skill data-cleanup -a claude-code -g -y

安装 repo 全部 skill

npx skills add sgharlow/claude-code-recipes --all -g -y

预览 repo 内 skill

npx skills add sgharlow/claude-code-recipes --list

SKILL.md

Frontmatter

{
    "name": "data-cleanup",
    "description": "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

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.