Great Expectations Data Validation Pipeline
Validate data quality using the Great Expectations Python library. Define expectations as unit tests for your data, run validation suites, and generate human-readable data quality reports.
npx skills add agentskillexchange/skills --skill great-expectations-data-validation-pipeline
The Great Expectations Data Validation Pipeline skill uses Great Expectations (GX), the open-source Python framework with over 11,000 GitHub stars, to bring rigorous data quality testing to AI agent workflows. Great Expectations lets you define “expectations” — declarative assertions about what your data should look like — and then validate real data against those expectations.
What this skill actually does
This skill enables agents to set up data validation pipelines that catch data quality issues before they propagate through downstream systems. Agents can create expectation suites that check column types, value ranges, null rates, uniqueness, referential integrity, distribution shapes, and custom business rules. Each expectation is a testable statement like “this column should never be null” or “values should be between 0 and 100.”
The workflow proceeds in three phases. First, the agent connects to a data source — this could be a Pandas DataFrame, a SQL database (PostgreSQL, Snowflake, BigQuery, Redshift, Databricks, and more), or a Spark DataFrame. Second, the agent defines or loads an expectation suite, either by profiling existing data to auto-generate expectations or by specifying custom expectations. Third, the agent runs validation and inspects the results.
Great Expectations produces rich validation output including pass/fail status per expectation, observed values, unexpected values counts and samples, and success percentages. The framework also generates “Data Docs” — auto-generated HTML documentation of your data quality results that can be served locally or pushed to cloud storage.
Integration points include dbt for post-transformation validation, Airflow and Dagster for orchestrating validation checkpoints in data pipelines, and Slack or PagerDuty for alerting on validation failures. Available on PyPI as the great-expectations package, licensed under Apache 2.0.