---
name: "great-expectations-profiler-suite"
description: "Profile tabular datasets and validate automated data quality expectations in CI/CD."
metadata:
  author: "Great Expectations"
  version: "1.0.0"
  runtime_compatibility:
    - "codex"
    - "claude-code"
    - "cursor"
    - "windsurf"
triggers:
  - "create great expectations suite"
  - "run great expectations checkpoint"
  - "generate data docs"
permissions:
  fs_read: true
  fs_write: "workspace"
  shell_exec: false
  net_access: false
---

# Great Expectations Profiler & Data Quality Suites

Implements enterprise data testing with Great Expectations: building Expectation Suites, running automated UserConfigurableProfilers, generating HTML Data Docs, and integrating Checkpoints into Airflow/Prefect.

## When to Invoke
Invoke this skill whenever you need to:
- create great expectations suite
- run great expectations checkpoint
- generate data docs

## Execution SOP & Rules
1. Initialize context: context = gx.get_context().
2. Connect data source: data_source = context.sources.add_pandas('my_pandas_source').
3. Add expectations: suite.add_expectation(ExpectColumnValuesToNotBeNull(column='customer_id')).
4. Run checkpoint and inspect generated static HTML Data Docs for visual audit logs.

## Verification Checklist
- Run local linting, syntax validation, and type-checks before reporting completion.
- Ensure all created or modified files adhere to workspace boundaries and avoid uncommitted secrets.
- Verify tests or build commands exit cleanly with return code 0.