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csv-cleaner

@elertan/csv-cleaner-skill
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Clean and normalize CSV data by analyzing structure, detecting issues (missing values, duplicates, type inconsistencies), and applying transformations. Use when users need to prepare messy CSV files for analysis or import.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name csv-cleaner
description Clean and normalize CSV data by analyzing structure, detecting issues (missing values, duplicates, type inconsistencies), and applying transformations. Use when users need to prepare messy CSV files for analysis or import.

CSV Cleaner Skill

You are a data cleaning specialist. Use this skill to clean and normalize CSV data.

Setup

Before running scripts, install dependencies:

pip install -r requirements.txt

How to Use This Skill

  1. Start: Read knowledge/index.md for overview
  2. Analyze: Run python scripts/analyze.py <input.csv> to get data profile
  3. Learn: Based on issues found, read relevant knowledge files
  4. Clean: Run cleaning operations using scripts/clean.py
  5. Output: Generate cleaned CSV, report, and schema

Available Scripts

analyze.py

python scripts/analyze.py input.csv [--output analysis.json]

Returns JSON with:

  • Column names, types, stats
  • Missing value counts
  • Duplicate detection
  • Semantic type inference (email, phone, date, etc.)

clean.py

python scripts/clean.py input.csv output.csv --operations ops.json

Operations file format:

{
  "operations": [
    {"type": "fill_missing", "column": "age", "strategy": "median"},
    {"type": "normalize_strings", "column": "name", "ops": ["trim", "lowercase"]},
    {"type": "standardize_dates", "column": "created_at", "format": "%Y-%m-%d"}
  ]
}

validate.py

python scripts/validate.py input.csv --schema schema.json

Validates data against JSON Schema, reports violations.

Workflow

  1. Run analyze.py on input CSV
  2. Review output, identify issues
  3. Read knowledge files for relevant topics:
    • Missing values → knowledge/operations/missing-values.md
    • Duplicates → knowledge/operations/duplicates.md
    • String issues → knowledge/types/strings.md
    • Date parsing → knowledge/types/dates.md
  4. Build operations JSON based on knowledge
  5. Run clean.py with operations
  6. Generate report and schema

Decision Making

When unsure which strategy to use, consult the knowledge files. They contain decision trees and best practices for each scenario.

Available Operations

Operation Description Required Params
fill_missing Fill null values column, strategy (mean/median/mode/constant/forward/backward)
drop_missing Drop rows with nulls columns (list), how (any/all)
remove_duplicates Remove duplicate rows columns (optional), keep (first/last/none)
normalize_strings Clean string columns column, ops (trim/lowercase/uppercase/remove_special)
standardize_dates Parse and format dates column, format (strftime format)
normalize_phones Convert to E.164 format column, country (default: US)
cap_outliers Cap extreme values column, method (iqr/zscore), multiplier

Knowledge Base Structure

knowledge/
├── index.md                 # Start here
├── operations/
│   ├── missing-values.md    # Handling nulls
│   ├── duplicates.md        # Deduplication
│   ├── outliers.md          # Outlier detection
│   └── normalization.md     # General patterns
├── types/
│   ├── strings.md           # Text cleaning
│   ├── numbers.md           # Numeric formatting
│   ├── dates.md             # Date parsing
│   ├── emails.md            # Email validation
│   └── phones.md            # Phone normalization
├── validation/
│   └── index.md             # JSON Schema rules
└── csv/
    └── edge-cases.md        # Encoding, quoting

Read only what you need based on detected issues.