data-quality-frameworks
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/wshobson/agents --skill data-quality-frameworks- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads data-quality-frameworks automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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")
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
FAQ
What does the data-quality-frameworks skill do?
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.
How do I install data-quality-frameworks?
Run npx skills add https://github.com/wshobson/agents --skill data-quality-frameworks in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/data-quality-frameworks/ (personal) or .claude/skills/data-quality-frameworks/ (project).
Which AI agents can use data-quality-frameworks?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is data-quality-frameworks safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer flagged items to review; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains data-quality-frameworks?
It is published by wshobson in wshobson/agents. It has 11,330 recorded installs.
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