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.
Profile →npx skills add wshobson/agents --skill data-quality-frameworks --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- When to Use This Skill
- Core Concepts
- 1. Data Quality Dimensions
- 2. Testing Pyramid for Data
- Quick Start
- Great Expectations Setup
- Detailed patterns and worked examples
- Summary: {totalpassed}/{totaltables} tables passed")
- Best Practices
- Do's
- Don'ts
Install pip install great_expectations Initialize project great_expectations init Create datasource great_expectations datasource new
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 it?
Run `npx skills add wshobson/agents --skill data-quality-frameworks --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From wshobson/agents, a repository with 38,479 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.