data-quality-frameworks

Validate data pipelines with Great Expectations, dbt tests, and data contracts.

wshobson/agents|Updated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGoose

What it does

Covers three complementary frameworks: Great Expectations for statistical and schema validation, dbt tests for transformation layer checks, and data contracts for cross-team data agreements
Includes six core quality dimensions (completeness, uniqueness, validity, accuracy, consistency, timeliness) with ready-to-use expectation patterns and custom test examples
Provides checkpoint automation for CI/CD inte

Repository

wshobson/agents

Last updated

Apr 8, 2026

Installation Guide

Select your AI agent

How to use data-quality-frameworks on Cursor

AI-first code editor with Composer

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

›Cursor installed and configured on your machine
›Node.js 16+ with npm — verify with node --version
›Active project directory where you want to add data-quality-frameworks

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/wshobson/agents --skill data-quality-frameworks

Fetches data-quality-frameworks from wshobson/agents and configures it for Cursor.

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?

│

│ ── Universal (.agents/skills) ────────────────

│ · Cline · Codex · Goose · Windsurf

│ ●Cursor(selected)

│ · Cursor · Aider · Continue

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/data-quality-frameworks

Restart Cursor to activate data-quality-frameworks. Access via /data-quality-frameworks in your agent's command palette.

⚠

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

›View source on GitHub ›Skills CLI docs ›About Cursor ›What are agent skills?

Documentation

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

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")

Patterns

Pattern 1: Great Expectations Suite

# expectations/orders_suite.py import great_expectations as gx from great_expectations.core import ExpectationSuite from great_expectations.core.expectation_configuration import ExpectationConfiguration def build_orders_suite() -> ExpectationSuite: """Build comprehensive orders expectation suite""" suite = ExpectationSuite(expectation_suite_name="orders_suite") # Schema expectations suite.add_expectation(ExpectationConfiguration( expectation_type="expect_table_columns_to_match_set", kwargs={ "column_set": ["order_id", "customer_id", "amount", "status", "created_at"], "exact_match": False # Allow additional columns } )) # Primary key suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_values_to_not_be_null", kwargs={"column": "order_id"} )) suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_values_to_be_unique", kwargs={"column": "order_id"} )) # Foreign key suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_values_to_not_be_null", kwargs={"column": "customer_id"} )) # Categorical values suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_values_to_be_in_set", kwargs={ "column": "status", "value_set": ["pending", "processing", "shipped", "delivered", "cancelled"] } )) # Numeric ranges suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_values_to_be_between", kwargs={ "column": "amount", "min_value": 0, "max_value": 100000, "strict_min": True # amount > 0 } )) # Date validity suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_values_to_be_dateutil_parseable", kwargs={"column": "created_at"} )) # Freshness - data should be recent suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_max_to_be_between", kwargs={ "column": "created_at", "min_value": {"$PARAMETER": "now - timedelta(days=1)"}, "max_value": {"$PARAMETER": "now"} } )) # Row count sanity suite.add_expectation(ExpectationConfiguration( expectation_type="expect_table_row_count_to_be_between", kwargs={ "min_value": 1000, # Expect at least 1000 rows "max_value": 10000000 } )) # Statistical expectations suite.add_expectation(ExpectationConfiguration( expectation_type="expect_column_mean_to_be_between", kwargs={ "column": "amount", "min_value": 50, "max_value": 500 } )) return suite

Pattern 2: Great Expectations Checkpoint

# great_expectations/checkpoints/orders_checkpoint.yml name: orders_checkpoint config_version: 1.0 class_name: Checkpoint run_name_template: "%Y%m%d-%H%M%S-orders-validation" validations: - batch_request: datasource_name: warehouse data_connector_name: default_inferred_data_connector_name data_asset_name: orders data_connector_query: index: -1 # Latest batch expectation_suite_name: orders_suite action_list: - name: store_validation_result action: class_name: StoreValidationResultAction - name: store_evaluation_parameters action: class_name: StoreEvaluationParametersAction - name: update_data_docs action: class_name: UpdateDataDocs

data-quality-frameworks

What it does

Category

Repository

Last updated

Installation Guide

How to use data-quality-frameworks on Cursor

Prerequisites

Run the install command

Select Cursor when prompted

Verify installation

Security Notice

Documentation

List & Monetize Your Skill

Use Cases

User Story & Requirements Generation

Competitive Analysis

Roadmap Prioritization

Install Skill

Data Quality Frameworks

When to Use This Skill

Core Concepts

1. Data Quality Dimensions

2. Testing Pyramid for Data

Quick Start

Great Expectations Setup

Patterns

Pattern 1: Great Expectations Suite

Pattern 2: Great Expectations Checkpoint

Stakeholder Communication

Implementation Guide

Best Practices

When to Use This

Learning Path

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Reviews

Discussion

`Stakeholder Communication`

`Implementation Guide`

`Best Practices`

`When to Use This`

`Learning Path`

`Related Skills`

`Reviews`

`Discussion`