Data Quality Frameworks

From the author

Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.

— the description by Seth Hobson

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to ensuring data reliability within pipelines. It covers core data quality dimensions such as completeness, uniqueness, and timeliness, while offering patterns for implementing validation suites and checkpoints. It emphasizes a testing pyramid approach for data, ranging from schema validation to complex integration tests, and provides best practices for maintaining robust data quality in production environments.

What it does

Defines data quality dimensions for systematic validation
Provides patterns for building Great Expectations suites
Outlines a data testing pyramid for structured validation
Demonstrates how to configure checkpoints for automated validation
Offers best practices for integrating quality checks into CI/CD pipelines
Explains how to establish data contracts between teams

When to use it

When building or maintaining data pipelines
When implementing automated validation rules for datasets
When setting up Great Expectations or dbt test suites
When establishing data contracts to ensure cross-team reliability
When monitoring data quality metrics in production

Advantages

Standardizes data quality checks across different pipelines
Promotes proactive error detection before data reaches downstream systems
Provides clear patterns for both schema and statistical validation
Supports automated alerting and reporting on data quality failures

Usage examples

“How do I ensure my orders table has no null values and unique IDs?”: The skill provides a pattern for defining an expectation suite that includes expect_column_values_to_not_be_null and expect_column_values_to_be_unique for the order_id column.
“How can I validate that my data is fresh?”: The skill suggests using the expect_column_max_to_be_between expectation to verify that the timestamp column falls within a recent, expected range.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/wshobson/data-quality-frameworks/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip data-quality-frameworks.zip -d .agents/skills

Documentation by Seth Hobson

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")

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Summary: {total_passed}/{total_tables} tables passed")

    report.append("")

    for table, result in results.items():
        status = "✅" if result.passed else "❌"
        report.append(f"### {status} {table}")
        report.append(f"- Expectations: {result.total_expectations}")
        report.append(f"- Failed: {result.failed_expectations}")

        if not result.passed:
            report.append("- Failed checks:")
            for detail in result.details:
                if not detail["success"]:
                    report.append(f"  - {detail['expectation']}: {detail['observed_value']}")
        report.append("")

    return "\n".join(report)

Usage

context = gx.get_context() pipeline = DataQualityPipeline(context)

tables_to_validate = { "orders": "orders_suite", "customers": "customers_suite", "products": "products_suite", }

results = pipeline.run_all(tables_to_validate) report = pipeline.generate_report(results)

Fail pipeline if any table failed

if not all(r.passed for r in results.values()): print(report) raise ValueError("Data quality checks failed!")


## Best Practices

### Do's

- **Test early** - Validate source data before transformations
- **Test incrementally** - Add tests as you find issues
- **Document expectations** - Clear descriptions for each test
- **Alert on failures** - Integrate with monitoring
- **Version contracts** - Track schema changes

### Don'ts

- **Don't test everything** - Focus on critical columns
- **Don't ignore warnings** - They often precede failures
- **Don't skip freshness** - Stale data is bad data
- **Don't hardcode thresholds** - Use dynamic baselines
- **Don't test in isolation** - Test relationships too

Files

ATTRIBUTION.mdAdded by AutoGineer1001 B
LICENSELicence file added by AutoGineer1.0 KiB
SKILL.md4.4 KiB
references/details.md11.4 KiB

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download data-quality-frameworks.zip