---

name: great-expectations description: "Use Great Expectations GX Core for data context setup, datasource and asset configuration, expectation suites, validation runs, checkpoints, actions, and Data Docs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Great Expectations

Use this repo skill when a task is about Great Expectations (GX Core): validating data quality, configuring GX contexts, connecting data assets, authoring expectation suites, running validations, interpreting validation results, or orchestrating checkpoints and Data Docs.

Start Here

1. Install GX Core if needed with pip install great_expectations, then confirm the package imports with import great_expectations as gx; for environment checks, run python scripts/inspect_gx_environment.py.
2. Pick the route that matches the user's immediate task; do not read every sub-skill up front.
3. Keep workflows modular: contexts own configuration, datasources own batches, suites own expectations, validations own results, and checkpoints own actions/Data Docs.
4. Prefer safe local examples and tiny fixtures before adding optional services, cloud credentials, or warehouse-specific dependencies.

Route by Task

• Read sub-skills/contexts-and-configuration/SKILL.md for gx.get_context, ephemeral vs file contexts, metadata stores, config variables, credentials, analytics toggles, Data Docs settings, and import/context sanity checks.
• Read sub-skills/datasources-and-assets/SKILL.md for context.data_sources, pandas dataframe assets, filesystem assets, SQLite/SQL assets, batch definitions, batch parameters, and optional Spark/cloud/warehouse backends.
• Read sub-skills/expectations-and-suites/SKILL.md for built-in expectations, ExpectationSuite, suite persistence, runtime parameters, row conditions, custom expectation subclasses, and suite troubleshooting.
• Read sub-skills/validations-and-results/SKILL.md for ValidationDefinition, batch.validate, batch/suite parameters, result formats, validation result interpretation, and unexpected-row retrieval.
• Read sub-skills/checkpoints-actions-and-data-docs/SKILL.md for Checkpoint, checkpoint actions, Slack/email notification safety, custom actions, Data Docs updates, and production validation orchestration.

Shared References and Scripts

• Read references/troubleshooting.md for cross-cutting install/import, optional dependency, version, pandas/pydantic, credential, and workflow routing failures.
• Read references/repo-provenance.md to decide whether this skill is stale relative to the source repository revision.
• Read references/repo-routing-metadata.json only when importing or updating the managed repo-skills-router.
• Run scripts/inspect_gx_environment.py --help for a safe public API inspection helper that prints GX version, top-level exports, context managers, and datasource factory availability.

Minimal API Shape

import great_expectations as gx

context = gx.get_context(mode="ephemeral")
suite = gx.ExpectationSuite(name="quality_suite")
suite.add_expectation(gx.expectations.ExpectColumnValuesToNotBeNull(column="id"))

The installed package exposes GX primarily through Python APIs. This checkout did not expose package console scripts, so treat command-line guidance as project-specific unless a future version adds documented entry points.

Minimal verification command after installation:

python -c "import great_expectations as gx; print(gx.__version__); print(gx.get_context(mode='ephemeral'))"

Common Workflow Order

1. Create a context with gx.get_context(...) and decide whether metadata should persist.
2. Add or retrieve a datasource and data asset, then create a reusable batch definition.
3. Create an ExpectationSuite and add built-in or custom expectation objects.
4. Create a ValidationDefinition from a batch definition and suite, then run it with the required batch_parameters and optional expectation_parameters.
5. Add a Checkpoint only when the workflow needs repeatable production orchestration, actions, notifications, or Data Docs.

Guardrails

• Do not hardcode database passwords, Slack webhooks, SMTP credentials, cloud tokens, or local filesystem secrets in GX config, generated code, logs, or expectation metadata.
• Do not install all optional backends by default; add only the extras or driver packages required for the selected datasource family.
• Do not use ResultFormat.COMPLETE or row-level unexpected output on large batches unless the user explicitly needs diagnostics and accepts memory/storage cost.
• Do not send real Slack/email notifications from smoke tests; use configuration checks or mocked actions unless the user authorizes a live send.
• Do not rely on original repository docs, examples, tests, or scripts at runtime; use the bundled references and helper scripts in this skill.