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/engineerwithai/engineerwith-agents/data-quality-frameworksnpx skills add EngineerWithAI/engineerwith-agents --skill data-quality-frameworksgit clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWrote 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/engineerwithai/engineerwith-agents/data-quality-frameworks)<a href="https://agentmods.dev/skills/engineerwithai/engineerwith-agents/data-quality-frameworks"><img src="https://agentmods.dev/badge/skills/engineerwithai/engineerwith-agents/data-quality-frameworks.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 5.1 | $0.00037 | $0.03564 |
| Opus 5 | $0.00018 | $0.01782 |
| Sonnet 5 | $0.00007 | $0.00713 |
| Haiku 4.5 | $0.00004 | $0.00356 |
Grade A, and why
data-quality-frameworks 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 2d 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.
This is a copy
100% identical to data-quality-frameworks — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 588 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Quality Frameworks
Production patterns for implementing data quality with Great Expectations, dbt tests, and data contracts to ensure reliable data pipelines.
When to Use This Skill
- Implementing data quality checks in pipelines
- Setting up Great Expectations validation
- Building comprehensive dbt test suites
- Establishing data contracts between teams
- Monitoring data quality metrics
- Automating data validation in CI/CD
Core Concepts
1. Data Quality Dimensions
| Dimension | Description | Example Check |
|---|---|---|
| Completeness | No missing values | expect_column_values_to_not_be_null |
| Uniqueness | No duplicates | expect_column_values_to_be_unique |
| Validity | Values in expected range | expect_column_values_to_be_in_set |
| Accuracy | Data matches reality | Cross-reference validation |
| Consistency | No contradictions | expect_column_pair_values_A_to_be_greater_than_B |
| Timeliness | Data is recent | expect_column_max_to_be_between |
2. Testing Pyramid for Data
/\
/ \ Integration Tests (cross-table)
/────\
/ \ Unit Tests (single column)
/────────\
/ \ Schema Tests (structure)
/────────────\
Quick Start
Great Expectations Setup
# Install
pip install great_expectations
# Initialize project
great_expectations init
# Create datasource
great_expectations datasource new
# great_expectations/checkpoints/daily_validation.yml
import great_expectations as gx
# Create context
context = gx.get_context()
# Create expectation suite
suite = context.add_expectation_suite("orders_suite")
# Add expectations
suite.add_expectation(
gx.expectations.ExpectColumnValuesToNotBeNull(column="order_id")
)
suite.add_expectation(
gx.expectations.ExpectColumnValuesToBeUnique(column="order_id")
)
# Validate
results = context.run_checkpoint(checkpoint_name="daily_orders")
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.
- 2d ago First seen · 588 lines · 37 tokens per session scan A 867330322634
data-quality-frameworks is a skill published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It adds 37 tokens to every session and 3,564 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to data-quality-frameworks, differing in 0 lines, and is treated as a copy.
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