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-platform-operating-model-and-service-ownershipgit 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-platform-operating-model-and-service-ownership)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-platform-operating-model-and-service-ownership"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-platform-operating-model-and-service-ownership/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-platform-operating-model-and-service-ownership"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-platform-operating-model-and-service-ownership.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 | $0.00054 | $0.00602 |
| Opus 5 | $0.00027 | $0.00301 |
| Sonnet 5 | $0.00011 | $0.00120 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
data-platform-operating-model-and-service-ownership 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 12d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Platform Operating Model And Service Ownership
Overview
Use this skill when the hard problem is organizational and operational, not just technical. It helps agents define who owns platform capabilities, how teams onboard safely, what the golden paths are, and how support, escalation, and service boundaries work across a shared data platform.
When to Use
- defining central platform-team responsibilities
- clarifying handoffs between platform and domain teams
- designing golden paths for new datasets, pipelines, or consumer onboarding
- documenting support tiers, ownership, and escalation paths
- reducing operational sprawl and unclear platform accountability
Do not assume a good architecture will operate well if the ownership model is vague.
Workflow
-
Define the service catalog. List:
- supported platform capabilities
- approved golden paths
- self-service versus managed services
- support boundaries and exclusions
-
Assign ownership clearly. Clarify:
- platform-team ownership
- domain-team ownership
- on-call or support expectations
- approval and escalation paths
-
Define onboarding and lifecycle flows. Cover:
- new source intake
- dataset onboarding
- access requests
- incident routing
- deprecation and retirement
-
Define service levels and guardrails. Include:
- support tiers
- change windows where relevant
- escalation expectations
- required contracts, validation, and operational evidence
-
Keep the model reviewable and executable. The operating model should be visible in docs, runbooks, and actual platform workflows, not just organization charts.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "Everyone owns quality." | Shared responsibility with no explicit owner usually means no one acts quickly enough. |
| "The platform team can handle anything." | Undefined service boundaries create burnout, delays, and unsafe tribal processes. |
| "We can formalize the golden path later." | Without an explicit adoption path, teams create inconsistent local patterns that are harder to govern. |
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.
- 12d ago First seen · 79 lines · 54 tokens per session scan A 29bd18dec5d2
data-platform-operating-model-and-service-ownership is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 54 tokens to every session and 602 once invoked, about $0.0003 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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