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.
npx skills add vaquarkhan/data-engineering-agent-skills --skill data-lake-and-zone-architecturegit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skillsWrote 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.
[](https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-lake-and-zone-architecture)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-lake-and-zone-architecture"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-lake-and-zone-architecture/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.
<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-lake-and-zone-architecture"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-lake-and-zone-architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00045 | $0.00589 |
| Opus 5 | $0.00023 | $0.00295 |
| Sonnet 5 | $0.00009 | $0.00118 |
| Haiku 4.5 | $0.00005 | $0.00059 |
Grade A, and why
data-lake-and-zone-architecture 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Lake And Zone Architecture
Overview
Use this skill when the storage platform needs structure before pipelines scale into chaos. It helps agents design clear lake zones, dataset boundaries, ownership, lifecycle rules, and publish-safe storage conventions.
When to Use
- designing a new data lake
- reorganizing raw, staging, refined, or curated zones
- defining object storage layout and lifecycle rules
- separating landing, transformation, and publish responsibilities
- reducing data swamp behavior in shared lake storage
Do not use this to justify creating extra layers with no operational purpose.
Workflow
-
Define the lake purpose and consumers. Clarify:
- source landing needs
- internal producer teams
- publish consumers
- compliance and retention expectations
-
Define the zone model intentionally. Typical zones include:
- raw or landing
- standardized or staging
- refined or modeled
- publish or serving
-
Assign responsibilities to each zone. Decide:
- who writes to it
- who reads from it
- what quality guarantees exist
- whether mutation is allowed
-
Design storage conventions. Include:
- path or catalog naming
- partition strategy
- retention lifecycle
- file-size expectations
- ownership tags and metadata
-
Keep publish rules separate from lake convenience. Not every dataset in the lake is ready for shared consumption.
Cross-Cloud Architecture
Use references/cloud-data-engineering-architecture-patterns.md when the task is not only zone design, but choosing the overall cloud architecture pattern across lake, warehouse, lakehouse, streaming, and hybrid shapes.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "We can dump everything into one bucket or container and organize later." | That is how data lakes turn into data swamps. |
| "More zones always means better governance." | Extra layers without distinct purpose add complexity and slow teams down. |
| "If the file exists in the lake, it is available for analytics." | Raw landing data rarely has the quality or contract guarantees needed for shared use. |
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.
- 11d ago First seen · 81 lines · 45 tokens per session scan A bc631b8c98d1
data-lake-and-zone-architecture is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 2mo ago), licensed MIT. It adds 45 tokens to every session and 589 once invoked, about $0.0002 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 2026-08-30.
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