feat(extractor): Add Excel conversion and post-processing capabilities

Add comprehensive post-processing features to convert downloaded Excel files
into structured data and merge them into a single output file.

New modules:
- extractor_core.py: Stateless pure functions for web operations
- excel_converter.py: Excel to DataFrame conversion utility
- tests/test_extractor_real.py: Real data extraction test suite

Enhanced features:
- post_process_downloads(): Convert and merge multiple Excel files
- extract_and_process(): Complete workflow in single call
- cleanup_temp_files(): Optional cleanup of temporary downloaded files
- Field name mapping for standardized output columns

Dependencies:
- pandas>=2.0.0 for data manipulation
- openpyxl>=3.1.0 for Excel file handling

Documentation:
- Updated CLAUDE.md with new module references
- Added API documentation for extractor components

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

utils/discrete_material_plan/extractor.py

@@ -144,6 +144,7 @@ def post_process_downloads(
     downloaded_files: List[str],
     output_file: str,
     verbose: bool = True,
+    cleanup_temp_files: bool = True,
 ) -> Tuple[str, pd.DataFrame]:
     """
     Convert and merge downloaded Excel files into structured DataFrame.
@@ -154,6 +155,7 @@ def post_process_downloads(
         downloaded_files: List of paths to downloaded Excel files
         output_file: Path to save merged Excel result
         verbose: Whether to print progress messages
+        cleanup_temp_files: Whether to delete temporary downloaded files after processing (default: True)
 
     Returns:
         Tuple of (output_file_path, merged_dataframe)
@@ -162,7 +164,8 @@ def post_process_downloads(
         >>> output_path, df = post_process_downloads(
         ...     downloaded_files=["batch_1.xlsx", "batch_2.xlsx"],
         ...     output_file="merged.xlsx",
-        ...     verbose=True
+        ...     verbose=True,
+        ...     cleanup_temp_files=True
         ... )
     """
     from .excel_converter import ExcelConverter
@@ -194,9 +197,37 @@ def post_process_downloads(
         print(f"Merged result saved to: {output_path}")
         print(f"Total rows: {len(merged_df)}")
 
+    # Cleanup temporary downloaded files
+    if cleanup_temp_files:
+        _cleanup_temp_files(downloaded_files, verbose)
+
     return str(output_path), merged_df
 
 
+def _cleanup_temp_files(downloaded_files: List[str], verbose: bool = True) -> int:
+    """
+    Remove temporary downloaded files.
+
+    Args:
+        downloaded_files: List of file paths to delete
+        verbose: Whether to print progress messages
+
+    Returns:
+        Number of files successfully deleted
+    """
+    deleted_count = 0
+    for file_path in downloaded_files:
+        try:
+            Path(file_path).unlink()
+            deleted_count += 1
+            if verbose:
+                print(f"Deleted temp file: {file_path}")
+        except Exception as e:
+            if verbose:
+                print(f"Warning: Could not delete {file_path}: {e}")
+    return deleted_count
+
+
 def extract_and_post_process(
     work_frame: Frame,
     page: Page,
@@ -205,6 +236,7 @@ def extract_and_post_process(
     output_file: str,
     batch_size: int = 10,
     verbose: bool = True,
+    cleanup_temp_files: bool = True,
 ) -> Tuple[str, pd.DataFrame]:
     """
     Complete extraction workflow: download batches + post-process to merged Excel.
@@ -220,6 +252,7 @@ def extract_and_post_process(
         output_file: Path for final merged Excel output
         batch_size: Maximum order IDs per batch
         verbose: Whether to print progress messages
+        cleanup_temp_files: Whether to delete temporary downloaded files after processing (default: True)
 
     Returns:
         Tuple of (output_file_path, merged_dataframe)
@@ -229,7 +262,8 @@ def extract_and_post_process(
         >>> browser, context, page, main_frame = login(...)
         >>> work_frame, page1 = navigate_to_discrete_material_page(main_frame, page)
         >>> output_path, df = extract_and_post_process(
-        ...     work_frame, page1, order_ids, "/downloads", "output.xlsx"
+        ...     work_frame, page1, order_ids, "/downloads", "output.xlsx",
+        ...     cleanup_temp_files=True
         ... )
         >>> context.close()
         >>> browser.close()
@@ -254,6 +288,7 @@ def extract_and_post_process(
         downloaded_files=downloaded_files,
         output_file=output_file,
         verbose=verbose,
+        cleanup_temp_files=cleanup_temp_files,
     )
 
     return output_path, merged_df
@@ -292,6 +327,7 @@ def extract_from_file(
     output_file: str,
     batch_size: int = 10,
     verbose: bool = True,
+    cleanup_temp_files: bool = True,
 ) -> Tuple[str, pd.DataFrame]:
     """
     Extract data from order IDs in a file and post-process to merged Excel.
@@ -306,6 +342,7 @@ def extract_from_file(
         output_file: Path for final merged Excel output
         batch_size: Maximum order IDs per batch
         verbose: Whether to print progress messages
+        cleanup_temp_files: Whether to delete temporary downloaded files after processing (default: True)
 
     Returns:
         Tuple of (output_file_path, merged_dataframe)
@@ -315,7 +352,8 @@ def extract_from_file(
         >>> order_ids = read_order_ids_from_file("orders.txt")
         >>> # Extract and process
         >>> output_path, df = extract_from_file(
-        ...     "orders.txt", work_frame, page, "/downloads", "output.xlsx"
+        ...     "orders.txt", work_frame, page, "/downloads", "output.xlsx",
+        ...     cleanup_temp_files=True
         ... )
     """
     order_ids = read_order_ids_from_file(id_file)
@@ -331,4 +369,5 @@ def extract_from_file(
         output_file=output_file,
         batch_size=batch_size,
         verbose=verbose,
+        cleanup_temp_files=cleanup_temp_files,
     )