{
  "context": "Evaluates whether the solution wires numpydoc's citation deduplication hooks into a Sphinx app so doctrees get unique citation labels and cleaned backreferences. Focuses on calling the package's built-in handlers and citation configuration rather than custom string munging.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Autodoc citation hook",
      "description": "Connects numpydoc's docstring citation handler (e.g., rename_references via autodoc processing) so citation labels are rewritten using the package's logic instead of manual parsing.",
      "max_score": 25
    },
    {
      "name": "Doctree relabel",
      "description": "Invokes numpydoc.relabel_references during doctree-read (or equivalent) to replace hashed citation labels with per-object names, ensuring collisions are resolved by the package's relabeling.",
      "max_score": 25
    },
    {
      "name": "Backref cleanup",
      "description": "Runs numpydoc.clean_backrefs on the resolved doctree so dangling or duplicate backreferences are removed by the package routine, not custom node pruning.",
      "max_score": 20
    },
    {
      "name": "Citation regex config",
      "description": "Respects the numpydoc_citation_re configuration value (default or customized) when registering citation handling so detection of citation targets matches package expectations.",
      "max_score": 15
    },
    {
      "name": "Setup integration",
      "description": "Registers the above handlers via the Sphinx setup flow (numpydoc.setup or app.connect usage) returning the proper metadata so the package manages event ordering and stability.",
      "max_score": 15
    }
  ]
}