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gcs-data-catalog

Discover Danish agricultural datasets in GCS and inspect schemas.

39|15|Updated Nov 16, 2024
Search Tags:#schema#parquet#joins#datasets#gcs#data-discovery
One-click install
npx skills add https://github.com/Klimabevaegelsen/landbruget.dk --skill gcs-data-catalog
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gcs-data-catalog
Source: https://github.com/Klimabevaegelsen/landbruget.dk/tree/main/.claude/skills/gcs-data-catalog
Command: npx skills add https://github.com/Klimabevaegelsen/landbruget.dk --skill gcs-data-catalog

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Identifies and accesses Danish agricultural datasets stored in GCS, providing a unified view of available datasets, their schemas, and bucket organization to accelerate discovery and planning.

Core Features & Use Cases

  • Data discovery across 18+ Danish agricultural datasets
  • Quick access to schemas, datasets, and categorization for rapid exploration
  • Cross-dataset joins using identifiers like CVR, CHR, and BFE for integrated analyses

Quick Start

Set the GCS_BUCKET environment variable and run a sample read using read_gcs_parquet to fetch a Parquet dataset from GCS and convert it to a pandas DataFrame.

Frequently Asked Questions about gcs-data-catalog

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

FAQPage Schema
How do I discover Danish agricultural datasets stored in GCS?▼

To discover Danish agricultural datasets in GCS, use this Skill to identify and access over 18 datasets, providing a unified view of available schemas and bucket organization for rapid exploration.

How do I read Parquet files from GCS into a pandas DataFrame for analysis?▼

You can read Parquet files from GCS into a pandas DataFrame by setting the GCS_BUCKET environment variable and using the read_gcs_parquet helper function to fetch and convert the dataset.

Can I join agricultural datasets across different buckets using CVR and CHR identifiers?▼

Yes, you can perform cross-dataset joins using CVR, CHR, and BFE identifiers to integrate multiple Danish agricultural datasets for comprehensive data science workflows.

What is the best way to inspect schemas of Danish agricultural datasets in GCS?▼

The best way to inspect schemas of Danish agricultural datasets in GCS is to use this Skill to present a unified view of available datasets and their schemas for rapid exploration and planning.

Do I need to set up any environment variables to access GCS datasets?▼

Yes, you need to set the GCS_BUCKET environment variable to access GCS datasets, enabling the read_gcs_parquet helper function to fetch Parquet datasets for your analysis.

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