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 agentmods add skills/rainmanjam/poka-yoke/datanpx skills add rainmanjam/poka-yoke --skill datagit clone --depth 1 https://github.com/rainmanjam/poka-yokeWrote 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/rainmanjam/poka-yoke/data)<a href="https://agentmods.dev/skills/rainmanjam/poka-yoke/data"><img src="https://agentmods.dev/badge/skills/rainmanjam/poka-yoke/data.svg" alt="Measured on agentmods" 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 | $0.00084 | $0.01587 |
| Opus 5 | $0.00042 | $0.00794 |
| Sonnet 5 | $0.00017 | $0.00317 |
| Haiku 4.5 | $0.00008 | $0.00159 |
Grade A, and why
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 5d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Poka-Yoke for Data
Data systems fail differently from application code, and that difference determines every device here. An application bug throws an exception, pages someone, and gets fixed. A data bug produces a number. The number looks fine. Someone makes a decision with it. Three weeks later a person notices revenue looks odd, and now you have three weeks of decisions to unwind and no way to know which were wrong.
In data, silence is the defect. A pipeline that fails loudly is working correctly. A pipeline that succeeds while producing garbage is the thing to design against, so most devices here are about converting silent wrongness into loud failure, which in Shingo's terms is buying yourself a Warning rung where you currently have nothing at all.
The four questions
Run these over any table or model. They map onto the standard lenses but the data-specific phrasing is what finds things.
Is it there? (freshness), Did the data arrive at all, and recently enough to be worth trusting? A stale table is the most dangerous artifact in a warehouse because it looks completely healthy. Every table needs a max-age assertion, and dashboards should surface last-updated rather than hiding it.
Is there the right amount? (volume, fixed-value lens), Row counts against expectation. This catches the breakages that leave every individual row looking fine: a partial load, a filter that silently matched nothing, a join that fanned out 100x. Assert both a floor and a ceiling, and compare against the same weekday historically rather than against yesterday: most business data is weekly-seasonal and a naive day-over-day check will cry wolf every Monday.
Is it shaped right? (schema and validity, contact lens), Types, nullability, accepted
value sets, ranges. Negative quantities, percentages above 100, timestamps in the future,
currency codes that don't exist, a status value nobody has seen before.
Does it agree? (reconciliation), Does the warehouse total match the source system?
Does the sum of the parts match the whole? This is the only check that catches a logic error
the data still looks well-shaped after, everything above validates shape, and a wrong JOIN
produces perfectly well-shaped, wrong data. It catches what moves a total, not a
mis-attribution that nets out. If you install one device, install this one on your
revenue-critical tables.
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.
- 5d ago First seen · 128 lines · 84 tokens per session scan A d35e45f708f3
data is a skill published in the GitHub repository rainmanjam/poka-yoke (22 stars, last pushed 3d ago), licensed MIT. It adds 84 tokens to every session and 1,587 once invoked, about $0.0004 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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