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 great-expectations-deequ-and-cualleegit 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/great-expectations-deequ-and-cuallee)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/great-expectations-deequ-and-cuallee"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/great-expectations-deequ-and-cuallee/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/great-expectations-deequ-and-cuallee"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/great-expectations-deequ-and-cuallee.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 | $0.00049 | $0.01014 |
| Opus 5 | $0.00024 | $0.00507 |
| Sonnet 5 | $0.00010 | $0.00203 |
| Haiku 4.5 | $0.00005 | $0.00101 |
Grade A, and why
great-expectations-deequ-and-cuallee 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 12d 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Great Expectations Deequ And Cuallee
Overview
Use this skill when the team wants structured quality enforcement through a data-quality framework instead of ad hoc checks. It helps agents align contracts with expectation suites, route failures by severity, and produce reviewable validation evidence.
When to Use
- implementing
Great Expectationsexpectation suites or checkpoints - adding
Deequanalyzers and constraints to Spark pipelines - using
Cualleefor lightweight PySpark or Pandas validation - standardizing reusable quality checks across datasets
- integrating framework-based quality gates into CI/CD or publish workflows
- producing validation evidence for audits or release gates
Do not use this when a few inline assertions are sufficient or when the team has no plan to reuse checks across datasets.
Workflow
-
Ground the validation suite in a real contract.
- start from the dataset contract: grain, freshness, allowed nulls, value ranges
- do not invent checks that are not tied to a documented expectation
- map each contract field to one or more framework checks
- classify checks by severity: critical (blocks publish), warning (alert only), informational
-
Choose the right framework for the runtime context.
Great Expectations: best for warehouse/lake validation with rich documentation outputDeequ: best for Spark pipelines with compile-time constraint definitionsCuallee: best for lightweight validation in PySpark or Pandas without heavy setup- consider execution environment: batch, streaming micro-batch, or CI tests
- avoid framework lock-in by keeping contract definitions separate from framework syntax
-
Design expectation suites with maintenance in mind.
- organize expectations by dataset and domain, not by framework capability
- keep suites small and focused — one suite per dataset or model output
- parameterize thresholds so they can be adjusted without code changes
- version suites alongside the pipeline code that produces the data
- document when and why each expectation was added
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.
- 12d ago First seen · 93 lines · 49 tokens per session scan A 2e6a499688c4
great-expectations-deequ-and-cuallee is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 49 tokens to every session and 1,014 once invoked, about $0.0002 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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