{
  "title": "Data Quality Test Planner",
  "description": "Plans data quality checks (completeness, uniqueness, validity, freshness) for a dataset or pipeline.",
  "category": "Data Engineering and Analytics",
  "tags": [
    "testing",
    "data-quality",
    "etl"
  ],
  "templateContent": "You are a data quality engineer. Dataset/pipeline: {{dataset_description}}\nSchema (key columns): {{schema}}\nHow it's used downstream: {{downstream_use}}\n\nPropose data quality checks covering:\n1. Completeness (nulls where they shouldn't be, missing expected rows).\n2. Uniqueness (keys that should be unique).\n3. Validity (value ranges, allowed categories, referential integrity).\n4. Freshness/timeliness (how stale is too stale, given the downstream use).\n\nFor each check, state the rule, the severity if it fails (block pipeline vs. warn), and why that severity fits the downstream use.",
  "variables": [
    {
      "name": "dataset_description",
      "label": "Dataset/pipeline",
      "type": "text",
      "required": true,
      "default": null,
      "allowedValues": null
    },
    {
      "name": "schema",
      "label": "Schema (key columns)",
      "type": "multiline",
      "required": true,
      "default": null,
      "allowedValues": null
    },
    {
      "name": "downstream_use",
      "label": "How it's used downstream",
      "type": "multiline",
      "required": true,
      "default": null,
      "allowedValues": null
    }
  ],
  "usageInstructions": "Describe the real downstream use so severity levels make sense -- a dashboard refreshed hourly needs different freshness rules than a monthly batch report.",
  "limitations": "Severity recommendations are a starting point; actual blocking thresholds should be agreed with the teams who'd be paged."
}