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 warehouse-and-schema-designgit 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/warehouse-and-schema-design)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/warehouse-and-schema-design"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/warehouse-and-schema-design/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/warehouse-and-schema-design"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/warehouse-and-schema-design.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 | $0.00044 | $0.00507 |
| Opus 5 | $0.00022 | $0.00253 |
| Sonnet 5 | $0.00009 | $0.00101 |
| Haiku 4.5 | $0.00004 | $0.00051 |
Grade A, and why
warehouse-and-schema-design 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 9d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Warehouse And Schema Design
Overview
Use this skill when the primary challenge is how data should be modeled for reliable analytics consumption. It helps agents choose good grain, keys, relationships, and serving patterns so downstream work stays understandable and performant.
When to Use
- designing marts, warehouse schemas, or curated serving tables
- choosing fact and dimension boundaries
- deciding grain, surrogate keys, or relationship strategy
- balancing normalization and denormalization
- restructuring analytics-facing datasets for usability
Do not reduce schema design to column naming alone. Good schema design is about behavior, meaning, and query ergonomics.
Workflow
-
Define the business grain first. Clarify:
- what one row represents
- what the primary analysis questions are
- how time and change should be represented
-
Choose the schema pattern intentionally. Common options:
- dimensional modeling
- data vault-oriented integration layers
- normalized serving models for operational analytics
- denormalized marts for common consumption patterns
-
Define keys and relationships. Include:
- business keys
- surrogate keys where needed
- slowly changing behavior
- null and unknown-member handling
-
Optimize for consumers, not just model purity. A perfect logical model that no analyst can use is not successful.
-
Validate compatibility with performance, governance, and metric use.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "We can figure out grain later." | Grain mistakes spread quickly through metrics and dashboards. |
| "A wide table is always easier for analysts." | Very wide tables often hide conflicting grains and unclear semantics. |
| "Normalization is more correct, so we should always prefer it." | Correctness and usability both matter; serving models need intentional trade-offs. |
Red Flags
- row grain is undocumented
- fact tables mix incompatible event types
- keys are inconsistent across domains
- schema choices are driven only by current dashboard convenience
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.
- 9d ago First seen · 70 lines · 44 tokens per session scan A 0f23eea49c1a
warehouse-and-schema-design is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 44 tokens to every session and 507 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 2026-09-03.
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