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-mesh-and-domain-oriented-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/data-mesh-and-domain-oriented-design)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-mesh-and-domain-oriented-design"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-mesh-and-domain-oriented-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/data-mesh-and-domain-oriented-design"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-mesh-and-domain-oriented-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 | $0.00041 | $0.00531 |
| Opus 5 | $0.00020 | $0.00266 |
| Sonnet 5 | $0.00008 | $0.00106 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
data-mesh-and-domain-oriented-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 10d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Mesh And Domain-Oriented Design
Overview
Use this skill when the problem is organizational scale as much as technical scale. It helps agents design domain-owned data products with explicit boundaries, interoperable contracts, and platform guardrails that do not collapse back into central bottlenecks.
When to Use
- designing domain-oriented data products
- defining ownership boundaries across multiple teams
- introducing or refining a data mesh operating model
- clarifying platform versus domain responsibilities
- reducing central-team bottlenecks in large data organizations
Do not use this to rename ordinary pipelines as "data products" without ownership, contracts, or service expectations.
Workflow
-
Identify domains and business boundaries. Clarify:
- which team owns the source behavior
- which team owns publish-ready data
- where cross-domain dependencies exist
-
Define data products, not just datasets. A data product should include:
- owner
- contract
- consumers
- freshness expectations
- support and change path
-
Separate domain ownership from platform ownership. Platform teams should provide capabilities, standards, and guardrails rather than own every dataset.
-
Define federated governance rules. Include:
- minimum contract rules
- lineage requirements
- discoverability
- security and privacy baselines
-
Check whether mesh adds real value. Not every small team or simple platform needs full mesh operating complexity.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "We can call every table a data product." | Without ownership and service expectations, it is just a dataset with better branding. |
| "Data mesh means no central standards." | Federated governance still needs common interoperability rules. |
| "Each domain can optimize however it wants." | Unbounded local choices make shared discovery, trust, and reuse much worse. |
Red Flags
- data products have no named owner
- domain boundaries are driven only by org chart convenience
- the platform team still owns operational details for every domain pipeline
- governance standards are implied but not codified
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.
- 10d ago First seen · 72 lines · 41 tokens per session scan A 54eef8892a32
data-mesh-and-domain-oriented-design is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (44 stars, last pushed 2mo ago), licensed MIT. It adds 41 tokens to every session and 531 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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