feat(toolkit): add classification and migration

Implement the standard/non-standard/not-macrolactone classification layer
and integrate it into analyzer, fragmenter, and CLI outputs.

Port the remaining legacy package capabilities into new visualization and
workflow modules, restore batch/statistics/SDF scripts on top of the flat
CSV workflow, and update active docs to the new package API.

scripts/batch_process.py

@@ -1,10 +1,67 @@
 from __future__ import annotations
 
-import sys
+import argparse
+import json
+from pathlib import Path
 
-from macro_lactone_toolkit.cli import main
+import pandas as pd
+
+from macro_lactone_toolkit.workflows import _fragment_csv_with_errors, results_to_dataframe
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(description="Batch fragment macrolactones into a flat CSV workflow.")
+    parser.add_argument("--input", required=True)
+    parser.add_argument("--output", required=True)
+    parser.add_argument("--errors-output", default=None)
+    parser.add_argument("--summary-output", default=None)
+    parser.add_argument("--smiles-column", default="smiles")
+    parser.add_argument("--id-column", default="id")
+    parser.add_argument("--ring-size", type=int, default=None)
+    parser.add_argument("--max-rows", type=int, default=None)
+    return parser
+
+
+def main(argv: list[str] | None = None) -> None:
+    args = build_parser().parse_args(argv)
+    results, errors = _fragment_csv_with_errors(
+        input_csv=args.input,
+        smiles_column=args.smiles_column,
+        id_column=args.id_column,
+        ring_size=args.ring_size,
+        max_rows=args.max_rows,
+    )
+
+    fragments = results_to_dataframe(results)
+    output_path = Path(args.output)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    fragments.to_csv(output_path, index=False)
+
+    if args.errors_output:
+        errors_output = Path(args.errors_output)
+        errors_output.parent.mkdir(parents=True, exist_ok=True)
+        pd.DataFrame(
+            [
+                {key: value for key, value in error.items() if key != "exception"}
+                for error in errors
+            ]
+        ).to_csv(errors_output, index=False)
+
+    summary = {
+        "processed": len(results) + len(errors),
+        "successful": len(results),
+        "failed": len(errors),
+        "fragments": int(len(fragments)),
+        "ring_size": args.ring_size,
+        "output": str(output_path),
+    }
+    if args.summary_output:
+        summary_path = Path(args.summary_output)
+        summary_path.parent.mkdir(parents=True, exist_ok=True)
+        summary_path.write_text(json.dumps(summary, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
+    else:
+        print(json.dumps(summary, indent=2, ensure_ascii=False))
 
 
 if __name__ == "__main__":
-    sys.argv = ["macro-lactone-toolkit", "fragment", *sys.argv[1:]]
     main()