Pudl

If you're working with PUDL energy data outputs and need to understand what's actually in those Parquet tables, this gets you oriented fast. It surfaces table schemas, column definitions, and source metadata directly in Jupyter or Marimo notebooks without making you grep through documentation or ETL code. The skill treats its metadata references as canonical, so you get consistent answers about field semantics and methodology whether you're analyzing data or debugging pipelines. It's scoped specifically for data users and analysts, not for people modifying the ETL itself. Honest take: this is the kind of contextual lookup layer that saves you from constantly asking "wait, what does this column actually measure?"

skills.sh installs: 2.1K
GitHub stars: 3
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add catalyst-cooperative/agent-skills --skill pudl --agent claude-code

Installs into .claude/skills of the current project.

Files

PUDL Data Explorer Guide

This skill is for data users who want to explore, understand, and load PUDL's public energy data products. It assumes no access to the PUDL Python package or source repository — only the publicly distributed data files and their metadata.

PUDL's primary outputs are Apache Parquet files, described by a Frictionless Data Package descriptor. For generic descriptor-querying patterns (jq), use the datapackage skill — this skill provides PUDL-specific knowledge layered on top.

Beyond the main Parquet outputs, PUDL also distributes raw per-form FERC Parquet data (covering Forms 1/2/6/60/714, each with its own datapackage.json) and the FERC EQR (partitioned Parquet, separate from the main build). These have different access patterns and are not covered by the main Frictionless descriptor — see Data Access for the full picture.

Workflow overview

Every step below is inexpensive and should happen by default whenever it's relevant to the question at hand, not only when the user asks for it by name.