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 hollandkevint/data-product-operator --skill grill-me-datagit clone --depth 1 https://github.com/hollandkevint/data-product-operatorWrote 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/hollandkevint/data-product-operator/grill-me-data)<a href="https://agentmods.dev/skills/hollandkevint/data-product-operator/grill-me-data"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/grill-me-data/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/hollandkevint/data-product-operator/grill-me-data"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/grill-me-data.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 | $0.00069 | $0.00865 |
| Opus 5 | $0.00034 | $0.00432 |
| Sonnet 5 | $0.00014 | $0.00173 |
| Haiku 4.5 | $0.00007 | $0.00086 |
Grade A, and why
grill-me-data 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Core Instruction
Interview the user relentlessly about every aspect of their data product plan until you reach a shared understanding. Walk down each branch of the decision tree, resolving dependencies between decisions one by one.
For each question, provide your recommended answer based on context. When the recommendation is obvious, the user just says "yes" and you move on.
Data Product Decision Branches
Start with: "What data product are you planning? Who consumes it and what decision does it inform?"
Then walk the tree. These are the branches specific to data products:
Consumer Branch
- Who are the primary consumers? (Explorers, Reporters, Decision-makers, Builders)
- How do they access this today? (manual query, existing dashboard, Excel export, nothing)
- What format do they need? (dashboard, API, flat file, embedded metric)
- What's their technical sophistication? (SQL-fluent, BI-tool-only, non-technical)
- What decision does this inform? If they can't name one, stop and run
data-consumer-discovery.
Schema Branch
- What's the grain? (one row = what?)
- Fact or dimension? (measurable events vs. descriptive attributes)
- SCD strategy needed? (Type 1 overwrite, Type 2 history, Type 3 limited history)
- Conformed dimensions available? (shared across products, or isolated)
- Denormalization tradeoffs? (query performance vs. maintenance complexity)
Cross-reference data-model-design for schema pattern details.
Quality Branch
- What accuracy SLA does this need? (99.9% for clinical, 95% for internal reporting)
- Freshness requirement? (real-time, hourly, daily, weekly)
- What happens when data is late or wrong? (circuit breaker, fallback, manual override)
- Who gets paged when quality drops?
Cross-reference data-quality-assessment for the 5-dimension scoring model.
Delivery Branch
- Push or pull? (scheduled delivery vs. on-demand query)
- What system of record does this feed? (BI tool, application, API, spreadsheet)
- Schema evolution strategy? (breaking changes, versioning, consumer migration)
- Consumer contract defined? (SLA, format, access method per consumer type)
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 · 85 lines · 69 tokens per session scan A 70621f88608f
grill-me-data is a skill published in the GitHub repository hollandkevint/data-product-operator (3 stars, last pushed yesterday), licensed MIT. It adds 69 tokens to every session and 865 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 2026-08-31.
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