cng-datasets

Verify geospatial data sources and orchestrate Kubernetes dataset workflows.

4|1|Updated Feb 24, 2026

Search Tags:#workflow #kubernetes #cloud-native #geospatial #data-processing #geodata

Ready to run

npx skills add https://github.com/boettiger-lab/agent-skills --skill cng-datasets

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cng-datasets
Source: https://github.com/boettiger-lab/agent-skills/tree/main/cng-datasets
Command: npx skills add https://github.com/boettiger-lab/agent-skills --skill cng-datasets

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Process geospatial datasets into cloud-native formats (GeoParquet, PMTiles, H3 hex Parquet) using the cng-datasets CLI and NRP Kubernetes. Covers the full workflow: URL verification, raw S3 upload, YAML generation, cluster deployment, monitoring, and documentation. Use when processing any geospatial dataset in the data-workflows repo, or when working with the cng-datasets CLI.

Core Features & Use Cases

End-to-end workflow orchestration on Kubernetes: from source verification to deployment and monitoring.
Multi-format outputs: GeoParquet, PMTiles, and H3 hex Parquet suitable for analytics and web maps.
Documentation artifacts creation and guidance to reproduce pipelines.

Quick Start

Run the cng-datasets workflow command with your dataset name, source URL, and bucket to generate Kubernetes YAML and deploy the processing pipeline.

Frequently Asked Questions about cng-datasets

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

FAQPage Schema

How do I orchestrate geospatial data processing workflows on Kubernetes?▼

You can orchestrate end-to-end geospatial workflows on Kubernetes by generating pipeline YAML locally and deploying it to your cluster. This covers source URL verification, raw S3 upload, in-cluster execution, monitoring, and documentation.

What is the best way to convert raw geospatial data into GeoParquet, PMTiles, or H3 hex Parquet?▼

To convert raw geospatial data into GeoParquet, PMTiles, or H3 hex Parquet, upload your source data to cloud storage, generate the processing pipeline YAML, and execute the workflow in-cluster to produce multi-format outputs suitable for analytics and web maps.

Do I need the cng-datasets CLI and Kubernetes access to process geospatial datasets in-cluster?▼

Yes, processing geospatial datasets in-cluster requires the cng-datasets CLI, Kubernetes access, and local Python tooling for setup. These prerequisites enable pipeline YAML generation, deployment, and monitoring of the data-processing workflow.

Can I process multi-layer geospatial datasets with this Kubernetes workflow?▼

Yes, this workflow applies to both single- and multi-layer geospatial datasets. After verifying the source URL and uploading raw data, it generates pipeline YAML and orchestrates in-cluster execution to produce your desired cloud-native outputs.

What are the limitations of running geospatial workflows locally instead of on Kubernetes?▼

Running geospatial workflows locally limits you to YAML generation and setup, preventing in-cluster deployment, monitoring, and orchestrated execution. You must deploy the generated pipeline to Kubernetes to fully process datasets into your target formats.

How do I verify geospatial data sources before uploading to cloud storage?▼

You verify geospatial data sources using URL verification steps within the CLI workflow before uploading raw data. This ensures source validity prior to generating pipeline YAML and orchestrating the in-cluster processing workflow.