Open CLI

mlr

scriptabledata
$ brew install miller
Summary

CSV transforms, stream processing, and data shaping from the terminal.

  • mlr fits data & db well, especially for csv transforms, stream processing, and data shaping from the terminal.
  • 1,052 homebrew installs (30d).
  • Easy to automate.
  • Good fit for coding-agent workflows and repeatable scripts.
  • Output is mostly text-first, so verify results before scripting around it.
database-mlr-SKILL.md

Mlr guide

CSV transforms, stream processing, and data shaping from the terminal. Built by John Kerl.

Open CLI packages the install path, verify step, and safe-start workflow so this tool can move from “interesting CLI” to something you can actually use. It also integrates with skills.sh so each CLI comes with the right companion skills, not just a binary and a docs link.

When to apply

  • csv transforms, stream processing, and data shaping from the terminal.
  • You need data processing in both local dev and CI.
  • You need csv transforms.
  • You need stream processing.
  • You need data shaping.

Quick reference

Installbrew install miller
Verifymlr --version
First real commandmlr --csv cut -f name,email data.csv

Open CLI × skills.sh

Open CLI integrates mlr with the right skills.sh companions so you get the tool and the workflow together.

Data Analysis

Recommended pairing

Open CLI recommends this skills.sh skill because it fits data workflows. Turn query output and flat files into usable insights faster.

View on skills.sh
$ npx skills add https://github.com/supercent-io/skills-template --skill data-analysis
Starter prompt

Use mlr together with the Data Analysis skills.sh skill. Start with safe inspection commands, summarize what you find, and ask before any step with side effects.

Also useful from skills.sh
Codebase Searchskills.shWorkflow Automationskills.sh

Why this tool

  • mlr fits data & db well, especially for csv transforms, stream processing, and data shaping from the terminal.
  • 1,052 homebrew installs (30d).
  • Easy to automate.

Watch-outs

  • Output is mostly plain text.

Example workflow

1. mlr --csv cut -f name,email data.csv

Safe start

Step 1

Install mlr.

Step 2

Run `mlr --version` first.

Step 3

Start with `mlr --csv cut -f name,email data.csv`.

Step 4

Install a CLI that matches your database engine.

Alternatives worth considering

jq

JSON processing, apis, and agent output parsing from the terminal.

http

API testing, readable http, and debugging endpoints from the terminal.

yq

YAML processing, config edits, and kubernetes files from the terminal.