lsdb

Analyze billion-row astronomical catalogs with lazy Dask-based LSDB queries.

Updated Apr 16, 2026

Search Tags:#astronomy #dask #crossmatch #hats #lsdb #catalog-analysis

Ready to run

npx skills add https://github.com/ejoliet/claude-skills --skill lsdb

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lsdb
Source: https://github.com/ejoliet/claude-skills/tree/main/lsdb
Command: npx skills add https://github.com/ejoliet/claude-skills --skill lsdb

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Scalable, lazy-evaluated analysis of billion-row astronomical catalogs using HATS partitions, enabling efficient filtering, cross-matching, and time-series access without loading data into memory.

Core Features & Use Cases

Cross-match catalogs at scale with minimal memory footprint.
Lazy Dask-based execution for scalable transformation without full in-memory loads.
Time-series and nested catalog operations with HATS format support.

Quick Start

Install lsdb and the hats extension, then verify you can load a catalog with read_hats.

Frequently Asked Questions about lsdb

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I crossmatch billion-row astronomical catalogs without loading them into memory?▼

Crossmatching billion-row astronomical catalogs without loading them into memory requires lazy Dask evaluation on HATS-formatted partitions. The LSDB Skill enables scalable cross-matching operations by processing data partitions sequentially rather than pulling entire datasets into RAM.

What is the HATS data format and when do I need it for catalog analysis?▼

The HATS data format is a partitioned structure for organizing massive astronomical catalogs. You need HATS when performing scalable cone searches, cross-matching, or time-series access on datasets like Rubin, Gaia, or ZTF without exceeding memory limits.

Can I perform cone searches and time-series access on LSST-scale data using Dask?▼

Yes, you can perform cone searches and time-series access on LSST-scale data using Dask. The LSDB library leverages lazy Dask execution to query and filter HATS-partitioned astronomical catalogs efficiently at massive scale.

How do I get started analyzing astronomical catalogs with the lsdb library?▼

To get started analyzing astronomical catalogs with the lsdb library, install lsdb and the hats extension. Verify your setup by loading a catalog using the read_hats function to ensure proper HATS partition access.

What is the best way to scale astronomical catalog transformations for Rubin or ZTF datasets?▼

The best way to scale astronomical catalog transformations for Rubin or ZTF datasets is using lazy Dask-based execution. The LSDB Skill processes HATS partitions to enable scalable transformations without requiring full in-memory data loads.