data

data is a skill for Claude Code, Codex from rainmanjam/poka-yoke. It costs 84 tokens per session (1,587 once invoked), scanned A, original, MIT.

A guide to preventing silent errors in data pipelines, warehouses, dbt models, and metrics.

In plain words
What is it for?
Use it to add freshness, row-count, null-rate, and related data-quality checks, and to handle schema changes and safe backfills.
Why use it?
It helps catch stale or incorrect data that appears successful but could lead to wrong dashboards and business decisions.

Skill for Claude CodeCodex

Part of the poka-yoke plugin — 6 skills shipped together

Install

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.

agentmods
npx agentmods add skills/rainmanjam/poka-yoke/data
Any agent
npx skills add rainmanjam/poka-yoke --skill data
Clone the repo
git clone --depth 1 https://github.com/rainmanjam/poka-yoke

Made for: Claude Code, Codex.

Or install poka-yoke, the plugin that ships this one along with the rest of its 6 skills.

Wrote 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.

agentmods badge for data

README.md
[![agentmods](https://agentmods.dev/badge/skills/rainmanjam/poka-yoke/data.svg)](https://agentmods.dev/skills/rainmanjam/poka-yoke/data)
Your own site
<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>
Per session 84 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,587 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 5d ago against content hash d35e45f708f3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

plugins/poka-yoke/skills/data/SKILL.md · 128 lines

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.

Read the full file on GitHub · 128 lines

Changes

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.

  1. 5d ago First seen · 128 lines · 84 tokens per session scan A d35e45f708f3

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories