{
  "context": "Evaluates how the solution uses pyexcel-xlsx to read, modify, and write Excel workbooks entirely in memory, handling both raw upload bytes and BytesIO streams while keeping sheet data intact. The focus is on applying the package's in-memory helpers instead of filesystem workflows.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Save via BytesIO",
      "description": "Workbook bytes are generated using pyexcel_xlsx.save_data or pyexcel.save_data with a BytesIO buffer (or Book.save_to_memory), avoiding any filesystem paths and returning the buffer's raw bytes.",
      "max_score": 25
    },
    {
      "name": "file_content load",
      "description": "Raw upload bytes are parsed through pyexcel.get_data/pyexcel_xlsx.get_data (or get_book) using the file_content parameter to return sheet rows without touching disk.",
      "max_score": 25
    },
    {
      "name": "Stream parity",
      "description": "BytesIO streams are read with the same pyexcel-xlsx loader (e.g., get_data(BytesIO(...)) or get_book(file_stream=...)) to yield identical sheet dictionaries as raw bytes, rather than custom parsing.",
      "max_score": 20
    },
    {
      "name": "In-memory append",
      "description": "Rows are appended by loading bytes through pyexcel-xlsx (file_content or BytesIO), updating a Sheet/Book object, and writing back with save_data or save_to_memory to produce new bytes with no temp files.",
      "max_score": 20
    },
    {
      "name": "Order preserved",
      "description": "Sheet ordering and row fidelity are preserved by relying on pyexcel-xlsx data structures (ordered sheet dicts) rather than reordering or retyping data during round-trips.",
      "max_score": 10
    }
  ]
}