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 data-consumer-discoverygit 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/data-consumer-discovery)<a href="https://agentmods.dev/skills/hollandkevint/data-product-operator/data-consumer-discovery"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/data-consumer-discovery/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/data-consumer-discovery"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/data-consumer-discovery.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 | $0.00063 | $0.00963 |
| Opus 5 | $0.00032 | $0.00481 |
| Sonnet 5 | $0.00013 | $0.00193 |
| Haiku 4.5 | $0.00006 | $0.00096 |
Grade A, and why
data-consumer-discovery 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Five Discovery Questions
Ask these in order. Each builds on the prior answer:
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"What decision are you trying to make?" Not "what data do you need?" Data requests are solutions. Decisions are the actual problem. If they say "I need a dashboard," ask what decision the dashboard enables.
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"Walk me through the last time you made this decision." Concrete past behavior beats hypothetical future needs. Listen for: where the data came from, how long it took, what they trusted, what they second-guessed.
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"Where did the data come from? How much did you trust it?" Trust is the adoption barrier for data products. A perfect pipeline that nobody trusts is useless. Map their trust signals: source familiarity, recency, whether they cross-checked.
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"What did you end up doing?" The action reveals the real need. "I exported to Excel and manually combined three reports" tells you more than any requirements document.
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"If you could get this answer in under a minute, what changes?" Gauges impact. If the answer is "nothing much," the problem isn't painful enough to build for. If it's "we could catch billing errors before they go out," you have a validated need.
NEVER ask "what data do you want?" or "what would you like us to build?" These questions produce wish lists, not validated needs. The Mom Test applies to data teams: talk about their life, not your product.
Workaround Archaeology
The strongest discovery signal is existing workarounds. If an analyst built a 47-tab Excel workbook, that's a validated need with proven demand.
For every workaround you find, document:
- Tool: What they built it in (Excel, Python script, manual process)
- Frequency: How often they use it (daily = high demand)
- Time cost: Hours per week maintaining it
- Trust level: Do they trust their own workaround? Why or why not?
- Downstream dependents: Who else uses the output?
Workarounds with daily frequency and downstream dependents are the highest-signal discovery findings. Build for these first.
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 · 76 lines · 63 tokens per session scan A de0059e5d3bb
data-consumer-discovery is a skill published in the GitHub repository hollandkevint/data-product-operator (3 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 963 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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