What problem does it solve? Working with tabular data often requires repetitive manual effort to filter rows, join datasets, compute aggregates, convert between formats, and generate summary reports. This Skill provides ready-to-use command-line and Python patterns for handling CSV, TSV, JSON, and JSON Lines files without external dependencies. ## Core Features & Use Cases - Data Transformation: Filter, sort, deduplicate, clean, and validate rows using awk, standard Unix tools, or pure Python with the built-in csv and json modules. - Joins and Aggregations: Perform inner and left joins across datasets, group rows by key columns, and compute sum, average, min, max, and count aggregates. - Format Conversion and Reporting: Convert between CSV, JSON, JSON Lines, and TSV, generate Markdown summary reports, and stream-process large files row-by-row without loading them into memory. - Use Case: Given an orders.csv and customers.csv, join them on customer_id, aggregate revenue by category, and output a Markdown summary report. ## Quick Start Ask the AI to merge orders.csv with customers.csv on customer_id and generate a summary report of total revenue grouped by product category.
