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vo-explorer

Enable cross-archive VO data discovery and analysis workflows across missions.

Updated Apr 16, 2026
Search Tags:#workflow#visualization#tap#vo#cross-mission#soda#datalink
One-click install
npx skills add https://github.com/ejoliet/claude-skills --skill vo-explorer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: vo-explorer
Source: https://github.com/ejoliet/claude-skills/tree/main/vo-explorer
Command: npx skills add https://github.com/ejoliet/claude-skills --skill vo-explorer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

VO development and cross-mission data workflows can be complex and error-prone; this guide provides a centralized, best-practice framework to access, integrate, and visualize VO data across archives and standards. This skill consolidates VO protocols, formats, platforms, and cross-mission patterns to speed up research and pipeline development.

Core Features & Use Cases

  • End-to-end VO workflow patterns spanning TAP, SODA, SIA, SSA, DataLink, RegTAP, ObsCore, ADQL, MOC, HiPS, UWS, and SAMP.
  • Cross-mission data access and cross-match strategies across Rubin, Euclid, SPHEREx, Roman, JWST, Gaia, and NOIRLab data.
  • Guidance on modern formats and storage patterns (Parquet, LSDB, HATS, Zarr, VOTable, ASDF, Iceberg) and visualization toolchains (Firefly, jdaviz, ipyaladin, glue-jupyter, datashader).
  • Context7 live documentation protocol integration to fetch current docs and APIs before coding.

Quick Start

Call Context7 to fetch current library docs before coding any VO module.

Frequently Asked Questions about vo-explorer

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

FAQPage Schema
How do I query cross-mission VO data from archives like Rubin, Euclid, and JWST?▼

Cross-mission VO data discovery uses TAP, SODA, and ObsCore protocols to query and integrate datasets from Rubin, Euclid, SPHEREx, Roman, JWST, Gaia, and NOIRLab archives.

What's the best way to cross-match astronomical datasets across different missions?▼

Cross-mission data access and cross-match strategies are supported through standardized VO protocols like ADQL and DataLink, enabling consistent queries across Rubin, Roman, Gaia, and NOIRLab data.

How do I visualize HiPS and MOC data in a Jupyter environment?▼

VO visualization toolchains include Firefly, jdaviz, ipyaladin, glue-jupyter, and datashader for rendering HiPS and MOC data directly within interactive Jupyter workflows.

Which modern data formats are supported for VO astronomical data storage and analysis?▼

Modern formats and storage patterns for VO workflows include Parquet, LSDB, HATS, Zarr, VOTable, ASDF, and Iceberg, supporting scalable cross-archive data integration and analysis.

Does this VO workflow approach require fetching external library documentation before coding?▼

Yes, it requires using Context7 to fetch up-to-date library docs and API signatures before coding any VO module, ensuring current best practices for TAP, SIA, and SSA standards.

Can I use ADQL and RegTAP together for cross-archive resource discovery?▼

ADQL and RegTAP are supported VO protocols within this workflow, allowing you to perform complex queries and discover registered resources across multiple astronomical archives.

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