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checkpoints-actions-and-data-docs

checkpoints-actions-and-data-docs

Build, save, and run GX Checkpoints; configure actions, notifications, custom actions, and local Data Docs updates safely.

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Skill-Dokumentation

GitHub

name: checkpoints-actions-and-data-docs description: "Build, save, and run GX Checkpoints; configure actions, notifications, custom actions, and local Data Docs updates safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Checkpoints, Actions, and Data Docs

Use this sub-skill when a task needs production-style Great Expectations orchestration: running one or more saved validations through a Checkpoint, updating Data Docs, sending notifications, or adding custom post-validation actions.

Route by task

  • Read Checkpoint API to construct gx.Checkpoint, persist it with context.checkpoints, pass batch_parameters and expectation_parameters, choose checkpoint result formats, and summarize CheckpointResult objects.
  • Read Actions and notifications before adding UpdateDataDocsAction, SlackNotificationAction, EmailAction, or a custom ValidationAction; this reference covers notify_on, notify_with, credential safety, and no-network tests.
  • Read Data Docs when configuring local or networked Data Docs sites, calling context.build_data_docs(), opening docs, or diagnosing stale docs.
  • Read Troubleshooting when checkpoints have no validation definitions, notification credentials are missing, Slack/email sends happen unexpectedly, Data Docs are absent/stale, results are too verbose, or custom action payloads do not match.
  • Run scripts/run_checkpoint_smoke.py for a tiny no-network checkpoint smoke workflow that builds an ephemeral pandas validation, runs a checkpoint, and optionally exercises a safe local UpdateDataDocsAction in a temporary file project.

Prerequisite routes

  • For context mode, file-project persistence, config variables, and credential substitution, read ../contexts-and-configuration/SKILL.md.
  • For datasource, asset, and batch-definition setup, read ../datasources-and-assets/SKILL.md.
  • For expectation suites and custom expectations, read ../expectations-and-suites/SKILL.md.
  • For validation-definition creation, result interpretation, and unexpected rows, read ../validations-and-results/SKILL.md.

Core public APIs

  • Import GX as import great_expectations as gx.
  • Import actions from great_expectations.checkpoint or great_expectations.checkpoint.actions, including UpdateDataDocsAction, SlackNotificationAction, EmailAction, and ValidationAction.
  • Construct checkpoints with gx.Checkpoint(name=..., validation_definitions=[...], actions=[...], result_format=...).
  • Persist checkpoints with context.checkpoints.add(...) or context.checkpoints.add_or_update(...); retrieve with context.checkpoints.get(name) and list/delete with all() and delete(name).
  • Run checkpoints with checkpoint.run(batch_parameters=..., expectation_parameters=..., run_id=...) and inspect result.success, result.run_results, and result.describe_dict().

Guardrails

  • Never place real Slack webhooks, bot tokens, SMTP passwords, or recipient lists in code, generated skills, logs, or committed GX config; use config variables or environment substitution.
  • Do not include Slack/email actions in smoke tests unless the test explicitly mocks network calls; notification actions call external APIs when notify_on matches the result.
  • Put UpdateDataDocsAction before notification actions for readability, even though GX prioritizes Data Docs actions internally so notification renderers can use fresh docs links.
  • Prefer SUMMARY or BASIC for routine checkpoint runs; reserve COMPLETE for bounded diagnostics because it can inflate stored validation results and notification payloads.
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