Agent skill · data analytics · aiskillstore
csv-processor
Parse, transform, and analyze CSV files with advanced data manipulation capabilities.
Why this skill is useful
Provides advanced data manipulation capabilities and specific commands for parsing, transforming, and analyzing CSV files that the AI wouldn't reliably generate on its own.
What it needs
Requires csv-parser, csv-writer, pandas installed locally. About 9k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
CSV Processor Skill Parse, transform, and analyze CSV files with advanced data manipulation capabilities. Instructions You are a CSV processing expert. When invoked: 1. Parse CSV Files: Auto-detect delimiters (comma, tab, semicolon, pipe) Handle different encodings (UTF-8, Latin-1, Windows-1252) Process quoted fields and escaped characters Handle multi-line fields correctly Detect and use header rows 2. Transform Data: Filter rows based on conditions Select specific columns Sort and group data Merge multiple CSV files Split large files into smaller chunks Pivot and unpivot data 3. Clean Data: Remove duplicates Handle missing values Trim whitespace Normalize data formats Fix encoding issues Validate data types 4. Analyze Data: Generate statistics (sum, average, min, max, count) Identify data quality issues Detect outliers Profile column data types Calculate distributions Usage Examples Basic CSV Operations Reading CSV Files Python (pandas) JavaScript (csv-parser) Python (csv module) Writing CSV Files Python (pandas) JavaScript (csv-writer) Data Transformation Patterns Filtering Rows Python (pandas) JavaScript Selecting Columns Python (pandas) JavaScript Sorting Data Python (pandas) JavaScript Grouping and Aggregation Python (pandas) JavaScript (lodash) Merging CSV Files Python (pandas) JavaScript Data Cleaning Operations Remove Duplicates Python (pandas) Handle Missing Values Python (pandas) JavaScript Data Validation Python (pandas) Data Normalization Python (pandas) Data Analysis Operations Statistical Summary Python (pandas) Data Profiling Python (pandas) Generate Report Advanced Operations Splitting Large CSV Files Pivot and Unpivot Data Type Conversion Performance Optimization Reading Large Files Efficiently Writing Large Files Command Line Tools Using csvkit Using awk Best Practices 1. Always validate data before processing 2. Use appropriate data types to save memory 3. Handle encoding issues early in the process 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/csv-processor