CSV Data Cleaning Assistant
Diagnoses and fixes common CSV data quality issues — duplicates, inconsistent formatting, missing values — and returns a documented cleaning plan.
CSV Data Cleaning Assistant is a free AI skill for data workflows. Diagnoses and fixes common CSV data quality issues — duplicates, inconsistent formatting, missing values — and returns a documented cleaning plan. It works with Claude, ChatGPT, Cursor and is ready to use out of the box.
About this skill
CSV Data Cleaning Assistant inspects a described or pasted dataset, identifies the specific data quality issues present (duplicate rows, inconsistent date/text formatting, missing values, mismatched types, outliers), and produces both a documented cleaning plan and the transformation logic needed to fix it (as plain steps, pandas code, or spreadsheet formulas depending on what the user is using). It's built for anyone who inherits messy spreadsheets and needs a repeatable, explainable cleaning process rather than manual row-by-row fixes.
What it does
Given a data sample or description of a dataset's problems, the skill outputs a data quality diagnosis (issue by issue), a step-by-step cleaning plan, and matching implementation — either pandas/Python code or spreadsheet formulas — for each fix, plus a validation checklist to confirm the cleaning worked.
What is included
- Data quality diagnosis framework (duplicates, formatting, missing values, types, outliers)
- Step-by-step cleaning plan
- Pandas/Python implementation for each fix
- Spreadsheet formula alternative for non-coders
- Post-clean validation checklist
How to use it
1. Download the SKILL.md file 2. Upload it into a new Claude conversation 3. Paste a data sample or describe the dataset's known issues 4. Receive a diagnosis, cleaning plan, and implementation code/formulas
Examples
Sample rows: 'John Smith, john@email.com, 2024-01-15' / 'john smith , JOHN@EMAIL.COM, 01/15/2024' / 'Jane Doe,,15-Jan-2024' — duplicates and inconsistent formatting across ~5000 rows.
DATA QUALITY DIAGNOSIS 1. Duplicate records (case/whitespace variants of same person) 2. Inconsistent email casing 3. Inconsistent date formats (ISO, US, and 'DD-Mon-YYYY' mixed) 4. Missing email field in some rows CLEANING PLAN 1. Normalize name/email: strip whitespace, lowercase email 2. Deduplicate on normalized name+email 3. Parse all date formats to ISO 8601 4. Flag rows with missing email for manual review (don't auto-fill) PANDAS IMPLEMENTATION ```python df['email'] = df['email'].str.strip().str.lower() df['name'] = df['name'].str.strip().str.lower() df = df.drop_duplicates(subset=['name','email']) df['date'] = pd.to_datetime(df['date'], format='mixed') missing_email = df[df['email'].isna()] ``` VALIDATION CHECKLIST - Row count reduced by duplicate count removed - No remaining case-mismatched duplicates - All dates parse to valid datetime - Missing-email rows exported separately for review
FAQ
What is this skill for?
How do I install it?
Do I need to know Python to use this?
What if my dataset is too large to paste in full?
How is this different from just asking Claude to 'clean this data'?
Does it handle outlier detection and statistical cleaning too?
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