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google-cloud-solution-agentic-ai-borderless-data-lakehouseby GoogleOfficialDevelopmentGitHub stars: 21.2k

Agentic AI Data Lakehouse skill: what it does and how to install it

Designs Google Cloud lakehouse architectures that combine Apache Iceberg, multi-cloud storage and BigQuery data agents for distributed analytics.

Summary generated from the skill's documentation.

Install

$ npx skills add google/skills --skill google-cloud-solution-agentic-ai-borderless-data-lakehouse

Run it in a terminal. If your agent is already running, start a new session so it picks the skill up.

About this skill

What it does. Guides requirements discovery, component decomposition and user confirmation, then maps a multi-cloud workload to Google Cloud products. It produces a Mermaid architecture, a solution guide, Terraform or deployment automation, sequential instructions and validation checks for ingestion, serving, connectivity and security.

When to use it. For borderless lakehouses spanning Cloud Storage, Amazon S3 or Azure Blob, including agentic analytics and cross-cloud connectivity. Not for single-cloud warehouses or Knowledge Catalog governance and Spark-based IDE analytics workflows.

History

Repo stars

21.2kAbout +2.9k since 16 Jul 2026

Before 1 Oct 2026 the curve is estimated from public event data.

Stars are counted for the whole repository, which holds 86 skills.

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Repo stars by day
DateRepo stars
12 Oct 202621,200
11 Oct 202621,140
10 Oct 202621,101
9 Oct 202621,071
8 Oct 202621,041
7 Oct 202621,006
6 Oct 202620,967
5 Oct 202620,939
4 Oct 202620,924
3 Oct 202620,865
2 Oct 202620,592
1 Oct 202620,557
24 Sept 2026 (estimated)20,498
17 Sept 2026 (estimated)20,452
10 Sept 2026 (estimated)19,878
3 Sept 2026 (estimated)18,833
27 Aug 2026 (estimated)18,776
20 Aug 2026 (estimated)18,765
13 Aug 2026 (estimated)18,765
6 Aug 2026 (estimated)18,294
30 Jul 2026 (estimated)18,271
23 Jul 2026 (estimated)18,271
16 Jul 2026 (estimated)18,260

Installs

4.5k

Tracking since . A chart appears once there are 7 days of data.

Installs via skills.sh

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