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-quality-platforms-and-rule-managementgit 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-quality-platforms-and-rule-management)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-quality-platforms-and-rule-management"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-quality-platforms-and-rule-management/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-quality-platforms-and-rule-management"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-quality-platforms-and-rule-management.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 | $0.00061 | $0.00652 |
| Opus 5 | $0.00030 | $0.00326 |
| Sonnet 5 | $0.00012 | $0.00130 |
| Haiku 4.5 | $0.00006 | $0.00065 |
Grade A, and why
data-quality-platforms-and-rule-management 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 11d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Quality Platforms And Rule Management
Overview
Use this skill when the question is not only what checks to write, but how the quality program should operate across tools, teams, and publish stages. It helps agents design rule ownership, severity, evidence, and enforcement across multiple quality frameworks.
When to Use
- selecting or combining data-quality tools
- designing rule portfolios and severity models
- aligning
dbttests,Great Expectations,Deequ,Cuallee,Soda, or warehouse-native checks - deciding what blocks publish versus what raises warnings
- improving long-term maintainability of data-quality controls
Do not assume more checks automatically improve quality. The operating model matters as much as the framework.
Workflow
-
Define the quality operating model. Clarify:
- who owns rules
- who triages failures
- what blocks publish
- what becomes an alert or trend signal
-
Group rules by purpose. Typical groups:
- contract correctness
- completeness and freshness
- distribution and anomaly checks
- reconciliation and financial controls
- governance or regulated-data controls
-
Choose tools intentionally. Decide where each type of rule belongs:
dbttests for warehouse-native model validationGreat Expectations,Deequ,Cuallee, orSodafor reusable framework-based checks- warehouse-native monitors for platform-local health signals
-
Define evidence and routing. Require:
- actionable output
- severity and ownership
- trend visibility
- links to incident and publish workflows
-
Control portfolio growth. Review overlapping, stale, noisy, or low-value checks so quality stays credible and maintainable.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "We should standardize on one tool for everything." | Different rule types often fit different execution surfaces and operating models. |
| "If a rule fails, we can decide the impact later." | Publish and incident behavior must already be defined when the rule is introduced. |
| "More rules always mean better quality." | Noisy or duplicate checks reduce trust and slow triage. |
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.
- 11d ago First seen · 78 lines · 61 tokens per session scan A cb5270c40c34
data-quality-platforms-and-rule-management is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 2mo ago), licensed MIT. It adds 61 tokens to every session and 652 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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