Great Expectations Generator
Generates Great Expectations suites from data profiles and business rules
Best use case
Great Expectations Generator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Generates Great Expectations suites from data profiles and business rules
Teams using Great Expectations Generator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/great-expectations-generator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Great Expectations Generator Compares
| Feature / Agent | Great Expectations Generator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Generates Great Expectations suites from data profiles and business rules
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Great Expectations Generator
## Overview
Generates Great Expectations suites from data profiles and business rules. This skill automates the creation of comprehensive expectation suites that enforce data quality constraints.
## Capabilities
- Expectation suite generation from profiling
- Custom expectation creation
- Checkpoint configuration
- Data docs generation
- Validation result analysis
- Expectation parameterization
- Suite versioning recommendations
- Integration with dbt and Airflow
## Input Schema
```json
{
"dataProfile": "object",
"businessRules": ["object"],
"existingSuite": "object",
"strictness": "strict|moderate|lenient"
}
```
## Output Schema
```json
{
"expectationSuite": "object",
"checkpointConfig": "object",
"documentation": "string",
"coverageReport": {
"columnsWithExpectations": "number",
"totalExpectations": "number"
}
}
```
## Target Processes
- Data Quality Framework
- ETL/ELT Pipeline
- dbt Project Setup
## Usage Guidelines
1. Provide data profile results from profiling analysis
2. Define business rules that should be enforced
3. Specify strictness level based on use case requirements
4. Include existing suite if extending an existing configuration
## Best Practices
- Start with moderate strictness and adjust based on validation results
- Include both column-level and table-level expectations
- Document business rationale for each custom expectation
- Version expectation suites alongside data transformations
- Configure appropriate data docs for stakeholder visibilityRelated Skills
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