---

name: expectations-and-suites description: "Create and maintain Great Expectations expectation classes and suites, including parameters, row conditions, and custom subclasses." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Expectations and Suites

Use this sub-skill when a GX Core task is about choosing built-in Expectations, configuring threshold parameters, grouping Expectations into an ExpectationSuite, testing expectations against a small batch, or turning repeated checks into a custom Expectation subclass.

Route First

• For context creation, store configuration, config variables, and file/ephemeral/cloud mode choices, read ../contexts-and-configuration/SKILL.md first.
• For datasource, asset, batch definition, and batch request setup, read ../datasources-and-assets/SKILL.md first.
• For running full validations, interpreting ExpectationSuiteValidationResult, unexpected rows, and result format trade-offs, read ../validations-and-results/SKILL.md next.
• For checkpoint orchestration, severity-triggered actions, notifications, and Data Docs, read ../checkpoints-actions-and-data-docs/SKILL.md next.

Local References

• Read references/expectation-api-patterns.md when selecting built-in gx.expectations classes, adding them to suites, or using severity, notes, meta, and result formats.
• Read references/row-conditions-and-parameters.md when an expectation needs $PARAMETER values, validation-time expectation_parameters, or row-level conditional logic.
• Read references/custom-expectations.md when repeated ad hoc checks should become a custom subclass with defaults and a clear description.
• Read references/troubleshooting.md when expectation instantiation, suite persistence, row conditions, parameters, or custom classes fail.
• Run scripts/build_suite_from_dataframe.py --help to see a tiny, safe dataframe workflow that builds a suite and can optionally validate it.

Core Workflow

1. Import GX and expectations with import great_expectations as gx and use the class-first API under gx.expectations.
2. Create concrete expectation instances, for example gx.expectations.ExpectColumnValuesToNotBeNull(column="id"), rather than relying on legacy string-only configuration when writing new code.
3. Group expectations with suite = gx.ExpectationSuite(name="..."), then call suite.add_expectation(expectation) for each unique expectation.
4. Persist the suite only when the target DataContext should own it: context.suites.add(suite) for new suites, context.suites.add_or_update(suite) for replace/update workflows, and context.suites.get(name="...") to retrieve it.
5. Test an individual expectation or a whole suite against a sample Batch before production use with batch.validate(expectation_or_suite, expectation_parameters=..., result_format=...).

Guardrails

• Keep suite names stable and descriptive; ExpectationSuite(name=...) requires a non-empty string.
• Keep meta JSON-serializable and reserve credentials or machine-specific paths for context configuration, not expectation metadata.
• Use severity="critical", "warning", or "info" to communicate failure impact; downstream checkpoint actions can route on severity.
• Use $PARAMETER dictionaries only for expectation fields that accept runtime/suite parameters, and always document the required expectation_parameters keys near the suite.
• Do not put datasource setup, checkpoint actions, or large validation-result interpretation in this sub-skill; route to the sibling sub-skills listed above.