What is data-data-lakehouse?

A guide to lakehouse architecture, which combines inexpensive, flexible data-lake storage with the reliability and querying features associated with a data warehouse. It covers bronze, silver, and gold layers, moving from raw data to cleaned and prepared data.

It is for designing Databricks, Delta Lake, Apache Paimon, or similar multi-cloud systems, including medallion layers, Unity Catalog permissions, data sharing, masking, and row filtering.

It helps organize data as it becomes more trustworthy and useful, while providing access controls and sharing rules across cloud platforms.

data-data-lakehouse — Skill for Claude Code and Codex by j4flmao

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add j4flmao/agent-skills --skill data-lakehouse

Clone the repo

git clone --depth 1 https://github.com/j4flmao/agent-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-lakehouse/github.svg)](https://agentmods.dev/skills/j4flmao/agent-skills/data-lakehouse)

Your own site

<a href="https://agentmods.dev/skills/j4flmao/agent-skills/data-lakehouse"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-lakehouse/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/j4flmao/agent-skills/data-lakehouse"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/data-lakehouse.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 113 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 6,696 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00113	$0.06696
Opus 5	$0.00056	$0.03348
Sonnet 5	$0.00023	$0.01339
Haiku 4.5	$0.00011	$0.00670

Measured 6d ago against content hash 78ab1424e27c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

data-data-lakehouse scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/data/data-lakehouse/SKILL.md · 602 lines

How it starts

The opening of the file, as written. The whole thing — 602 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Data Data Lakehouse

Purpose

Design lakehouse architectures that merge data lake flexibility with warehouse reliability. Implement medallion architecture for data quality progression, Unity Catalog for governance, Delta Sharing for data collaboration, and multi-cloud deployment patterns.

Agent Protocol

Trigger

Exact user phrases: "lakehouse", "medallion architecture", "bronze", "silver", "gold", "Databricks", "Unity Catalog", "Delta Sharing", "Apache Paimon", "multi-cloud lakehouse", "open formats", "lakehouse governance", "data mesh lakehouse".

Input Context

Before activating, verify:

Cloud provider (AWS, Azure, GCP, multi-cloud)
Lakehouse platform (Databricks, AWS EMR, Azure Synapse, GCP Dataproc)
Table format (Delta, Iceberg, Paimon)
Data sources volume and types
Number of data producers and consumers
Security requirements (RBAC, column mask, row filter)
Data sharing requirements (internal teams, external partners)

Output Artifact

Lakehouse architecture with medallion layers, Unity Catalog configuration, and platform deployment specs.

Response Format

Lakehouse Platform: {Databricks | EMR + Iceberg | Synapse + Delta | Dataproc + Iceberg}
Catalog: {Unity Catalog | Hive Metastore | AWS Glue | Nessie}
Table Format: {Delta | Iceberg | Paimon}
Medallion Layers: Bronze (raw), Silver (cleaned), Gold (aggregated)
Sharing: {Delta Sharing | open | proprietary}

# Unity Catalog metastore config
# Medallion pipeline YAML

No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.

Completion Criteria

Medallion layers defined with data flow and transformations
Unity Catalog or equivalent metastore configured
RBAC and column-level security defined
Delta Sharing setup for cross-team/partner data access
Table format selected with interoperability plan
Multi-cloud or cross-region replication strategy
Data quality checks at each medallion layer
Platform deployment topology (compute, storage, catalog)

Read the full file on GitHub · 602 lines

Files

What ships with it

26 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

6d ago First seen · 602 lines · 113 tokens per session scan A 78ab1424e27c

Subscribe to this mod's changes

data-data-lakehouse is a skill published in the GitHub repository j4flmao/agent-skills (22 stars, last pushed 3d ago), licensed MIT. It adds 113 tokens to every session and 6,696 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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