Agent skill · data analytics · jeremylongshore
data-processing
Use when working with structured data files (CSV, JSON, YAML, TOML, Parquet) — querying, transforming, filtering, aggregating, or converting between formats
Why this skill is useful
Provides specific commands and patterns for querying and transforming structured data formats that the AI wouldn't reliably generate on its own.
What it needs
Requires duckdb, gron, jq, mlr, xsv, yq installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
Data Processing When to Use Querying, filtering, or transforming JSON files Reading or converting YAML, TOML, or XML config files Analyzing, aggregating, or joining CSV/TSV/Parquet files Running SQL queries against local data files without a database server Converting between data formats (JSON to YAML, CSV to JSON, etc.) Exploring deeply nested JSON structures Tools Tool Purpose Structured output ------ --------- ------------------- jq Query and transform JSON Native JSON yq Query and transform YAML, TOML, XML -o json for JSON output gron Flatten JSON into greppable path.to.key = value lines --ungron to reverse back to JSON miller (mlr) Transform CSV/JSON/TSV records with awk-like verbs --json for JSON output xsv Fast CSV slicing, searching, joining, statistics CSV native (pipe to xsv table for display) DuckDB SQL queries on CSV, JSON, Parquet files -json flag for JSON output Patterns JSON: Filter array elements by field value JSON: Extract specific fields from array JSON: Count items grouped by field YAML: Read a nested value YAML: Convert entire file to JSON TOML: Read a value JSON: Explore unknown structure by grepping paths CSV: Column statistics (min, max, mean, stddev) CSV: Search rows matching a pattern in a column CSV: Select specific columns CSV: Sort by column SQL: Query a CSV file SQL: Query a JSON file SQL: Query Parquet files CSV/JSON: Transform records with miller Format conversion: CSV to JSON Pipelines YAML config → JSON → SQL query Each stage: yq converts YAML to JSON, DuckDB runs SQL on the JSON stream. Grep nested JSON paths → reconstruct matching subset Each stage: gron flattens JSON to paths, grep filters, ungron reconstructs valid JSON from matches. CSV filter → aggregate with SQL Each stage: xsv filters rows by region, DuckDB aggregates the filtered stream. Join two CSV files with SQL Multi-format pipeline: JSON → CSV → stats Each stage: jq extracts fields to CSV format, xsv computes statistics. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/data-processing