{
  "context": "Assesses whether the solution uses Vedro's parameterization API to build one templated scenario that covers the delivery quote cases and handles the unsupported route skip. Emphasizes correct decorator placement, stacking, and dataset handling per the spec.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Stacked params",
      "description": "Uses stacked `vedro.params` decorators on the scenario template (constructor or scenario function) to create separate metro instances for 5 km/2.0 kg expecting 14.50 and 18 km/1.0 kg expecting 33.00 rather than manual loops.",
      "max_score": 35
    },
    {
      "name": "Keyword params",
      "description": "Applies a `vedro.params` decorator with keyword arguments to feed the suburb dataset (city=\"suburb\", distance_km=8.0, weight_kg=0.5, expected_total=17.50) into the same template.",
      "max_score": 15
    },
    {
      "name": "Decorator piping",
      "description": "Uses `vedro.params.__class_getitem__` syntax to wrap the remote dataset with a `vedro.skip` (or equivalent) decorator so that the unsupported route instance is skipped with a reason instead of asserting totals.",
      "max_score": 25
    },
    {
      "name": "Single template",
      "description": "Keeps one scenario template (subclass of `vedro.Scenario` or `@vedro.scenario` function) reused by all `params` entries instead of defining separate scenarios per dataset.",
      "max_score": 15
    },
    {
      "name": "Param value flow",
      "description": "Extracts the expected_total and other values directly from each `vedro.params` dataset inside scenario steps (e.g., via initializer arguments) rather than hardcoding or recomputing them.",
      "max_score": 10
    }
  ]
}