{
  "context": "Evaluates how well the solution leverages numpydoc's docstring validation engine and error codes to gate documentation quality, and how cleanly it integrates the validation into a Sphinx build workflow. Points reward correct use of the library APIs, honoring configuration knobs, and producing meaningful summaries tied to the package's code-based diagnostics.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Validation API",
      "description": "Uses numpydoc.validate.validate to run checks on the provided modules and returns structured results that include qualified object names, error codes (GL**, RT**, SS**), messages, and line numbers.",
      "max_score": 30
    },
    {
      "name": "Ignore Controls",
      "description": "Honors ignore lists and inline \"numpydoc ignore=\" markers by passing them through to the validator or filtering on its outputs so specified codes drop while unrelated codes remain.",
      "max_score": 20
    },
    {
      "name": "Sphinx Hook",
      "description": "Registers a Sphinx event handler (e.g., via app.connect) that invokes validation during the build and respects numpydoc Sphinx settings such as numpydoc_validation_checks/fail_on and numpydoc_validation_exclude when deciding which objects to scan.",
      "max_score": 25
    },
    {
      "name": "Fail/Warn Logic",
      "description": "Differentiates fail_on versus warn_on codes using validation results, raising sphinx.errors.BuildWarning (or halting the build) for fail_on hits while emitting warnings for non-fatal codes.",
      "max_score": 15
    },
    {
      "name": "Reporting",
      "description": "Aggregates validation output by code, produces totals per code class, and surfaces an overall status that flips to failed whenever any fail_on code appears.",
      "max_score": 10
    }
  ]
}